Short Selling EMA Cross (By Coinrule)BINANCE:AVAXUSDT
This short selling script works best in periods of downtrends and general bearish market conditions, with the ultimate goal to sell as the the price decreases further and buy back before a rebound.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to to hodl this cash, or use it to reinvest when the market reaches attractive buying levels.
Entry
The exponential moving average ( EMA ) 20 and EMA 50 have been used for the variables determining the entry to the short. EMAs can operate better than simple moving averages due to the additional weighting placed on the most recent data points, whereas simple moving averages weight all the data the same. This means that price is tracked more closely and the most recent volatile moves can be captured and exploited more efficiently using EMAs.
Our backtesting data revealed that the most profitable timeframe was the 30-minute timeframe, this also enabled a good frequency of trades and high profitability.
A fast (shorter term) exponential moving average , in this strategy the EMA 20, crossing under a slow (longer term) moving average, in this example the EMA 50, signals the price of an asset has started to trend to the downside, as the most recent data signals price is declining compared to earlier data. The entry acts on this principle and executes when the EMA 20 crosses under the EMA 50.
Enter Short: EMA 20 crosses under EMA 50.
Exit
This script utilises a take profit and stop loss for the exit. The take profit is set at -8% and the stop loss is set at +16% from the entry price. This would normally be a poor trade due to the risk:reward equalling 0.5. However, when looking at the backtesting data, the high profitability of the strategy (93.33%) leads to increased confidence and showcases the high probability of success according to historical data.
The take profit (-8%) and the stop loss (+16%) of the strategy are widely placed to ensure the move is captured without being stopped out due to relief rallies. The stop loss also plays a role of mitigating losses and minimising risk of being stuck in a short position once there has been a fundamental trend reversal and the market has become bullish .
Exit Short: -8% price decrease from entry price.
OR
Exit Short: +16% price increase from entry price.
Tip: Research what coins have consistent and large token unlocks / highly inflationary tokenomics, and target these during bear markets to short as they will most likely have substantial selling pressure that outweighs demand - leading to declining prices.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
The backtesting data was recorded from December 1st 2021, just as the market was beginning its downtrend. We therefore recommend analysing the market conditions prior to utilising this strategy as it operates best on weak coins during downtrends and bearish conditions.
Pesquisar nos scripts por "stop loss"
action zone - ATR stop reverse order strategy v0.1 by 9nckACTION ZONE-ATR MOD v0.1 DOCUMENTATION
Overview
This tradingview pine script strategy is mainly created to enrich my coding skill. It is a combination of “CDC-ACTIONZONE” and my personal studies of trading techniques in various sources e.g.book, course or blog. This strategy purposefully built to connect with my automatic trading bot. However, It will be very useful to aid your trading routine by diminishing mental distraction which possibly leads to bad trades.
How does it work?
This strategy will do a basic simple thing that most traders do by creating entry signals on both sides long/short and also set the stop loss. Furthermore, It will also reverse the order (from long to short and vice versa (if long/short conditions are met). Finally, it will recalculate the stop loss/take profit price in every complete bar to increase the chance of winning and limit our loss.
Entry rules(Long/Short)
If you have no open order, an order will be created when a fast EMA crosses(up(long)/down(short) the slow EMA(It’s as simple as that).
If you have an open order, the current order will be (sold if long, covered if short) and the opposite side order will be created.
Exit and Reverse rules(Long/Short)
If fast EMA cross (DOWN(long), UP(short)), the current order will be closed, THE OPPOSITE SIDE ORDER WILL ALSO BE CREATED.
Risk management
FLEX STOP PRICE : initial value will be set at the bar which order created. It is a fast ema (+/-) MIDDLE ATR value.
If MIDDLE ATR value rises, it will be our new stop price.
If MIDDLE ATR value falls, stop price unchanged
If Price OVERBOUGHT(long)/SOLD(short), LOW of that bar will be a new stop price.
Minimum position hold period
In order to eliminate risk of repeatedly open, close orders in sideway trends. Minimum hold period must be passed to start exit our position. However, It always respects stop loss prices. The value refers to the number of bars.
MUST READ!!!
This strategy uses only MARKET ORDER. If you trade with a bot, make sure you choose only enormous market cap tokens.
This strategy is bi-direction strategy. It will work best in the DERIVATIVE market.
It was initially designed to compete in the cryptocurrency market which has very high volume and volatility.
I only use this strategy in 1HR (acceptable change rate, optimum trade frequency)
How (should) we use it?
Choose crypto future pairs (recommend only top 10-15 market volume pairs in Binance, let’s say 1000M+ trade value)
Choose your time frame (1H is strongly recommended)
Setup your portfolio profile (Setting->Properties) such as Initial cap, order size, commission. DO NOT USE CAL ON EVERY TICK IT WILL CAUSE REPAINTING AND YOUR CAPITAL IS BLEEDING !!!
BACKTEST FIRST!! Back test is a combination of art, math and statis(and a bit of luck). You can apply to train and test methods or whatever you are familiar with. In my opinion, your test period should include UPTREND, SIDEWAY, DOWNTREND. Fine tune fast, slow ema first(my best ema length of 1H timeframe around 7-10, 17-22). Try to eliminate fault breakout trade and use other options only necessary. Hopefully we can use automatic optimization on Pine Script soon.
Don’t forget to turn off using a specific backtest date option to start your strategy.A
THIS IS NOT A PERFECT (OR EVEN PROFITABLE) STRATEGY. USE AT YOUR OWN RISK AND TRADE RESPONSIBLY. DYOR DUDE.
[Hercules] Backtest FrameworkLevel: 5
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies,
However, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "fixed and flexiable", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Hercules/Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Hercules} Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Hercules} BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Hercules/Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Hercules/Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: Hercules Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Subscription
To encourage more people use this framework and avoid some abuse this one, I would like to set
100 Tradingview Coins per Monthly Subscription.
100X10 Tradingview Coins per Yearly Subscription.
[Sextan] PINEv4 Sextans Backtest FrameworkLevel: 5
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies,
However, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "fixed and flexiable", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Subscription
To encourage more people use this framework and avoid some abuse this one, I would like to set
100 Tradingview Coins per Monthly Subscription.
100X10 Tradingview Coins per Yearly Subscription.
Swing Trader-Pro V2The strategy- what is it?
This indicator is designed from a theory created by myself in order to distinguish a correction from an impulse. This comes down to the ability to compare "x" range of candles to "y" range of candles and highlight key differences to then correctly portray that the most recent move in price will be (or is) a correction.
Following this theory, we all understand that corrections don't go with the trend right? So this means at some point, there is a high probability of a rejection somewhere in this most recent move, that will ultimately push price higher or lower as it continues back with the trend. Therefore, through extensive quantitative research and back-testing, we are able to highlight areas of high-probability rejections within these supposed corrections.
How does it work?
Firstly, we need to establish a high and low point (using pivots ) that help us decide what the state is of the recent move between the high and low (we call this "point A" and "point B"). So we can only consider whether the recent move in price was an impulse or a correction until the move from "point B" to "point C" is made. But before that, once we have identified "Point A" and "point B", we use 2 (supposedly) strong levels which help integrate a box onscreen and thus, indicate this area of high liquidity. This box will continue to adjust according to the change of pivots (if price keeps creating HH's & HL's or LH's & LL's depending on market trend). But if we establish a strong high and low and price stays within this range, then the box will remain in place.
The default color of the box is red; the only time the color of the box will change is when:
- Price retraces from the high/low back to the box (price has to touch the box)
AND
-If any of our confirmations indicate a successful correction based on our theory.
So the box color varies:
- Red = very weak (or) no entry = no confirmations were made
- Yellow = weak entry = some but not all confirmations were made
- Green = strong entry = all confirmations have indicated that the move from "point B" to "point C" (remember that "point C" is where the box is) is a correction when compared with the move from "point A" to "point B"
These confirmations are all validated on the same candle during live candle activity (not when the candle has closed on the box). As this happens, the confirmations will determine the state of entry quality as soon as price touches the box.
In this time, we will see a new orange label highlighting what indicators have confirmed a successful correction and what haven't.
The label shows the different confirmation indicators in which we have provided different names (as this is the secret we intend to keep). So we have:
- "CC"
- "B1/B2"
- "B3"
Usually, we will see either an "OK" or "NOT OK" next to each confirmation indicator. This just tells us whether they have confirmed or not. Please note that this "point C" label does not stay permanently, regardless of the state of entry quality. The label will in fact stay on the screen until the next box has been generated, which is usually a few candles after the entry has been triggered.
Entries, SL's and TP's
This indicator shows the user an area of high-probability rejection. So in terms of specifying a precise entry, you're completely free to enter on the following:
- the moment price touches the box (depending on what color it is of course)
- the other end of the box (if you would like to catch a "sniper entry")
- or if price pierces the entire box and is still green, you can wait to see if price comes back through the box (which indicates a false breakout).
As for Stop-losses, i would recommend:
- Long entries: set your SL at the recent low (this should be "point A")
- Short entries: set your SL to the recent high (this should be "point A" as well, because if you're switching from the "long entry" setting to the "short entry" setting, the indicator labels flip around and are the opposite of what they are for long entries).
For Take profits, this is entirely up to the user. Because some entries will allow you to have great RR ratios depending on how you manage the active trades. Some recommendations below:
- Set TP to "point B" pivot
- Use trailing stop function or something similar if available
- Add other indicators such as the RSI and close when price reaches key levels
- When price shows signs of exhaustion or early stages of reversal then just close
Additional information and recommendations
- This works on any time frame and on any financial market, whether you prefer Forex, stocks, crypto, commodities , etc.
- In regards to trade direction, you can change in the settings to look for either long or short positions in the market. I would recommend using it in favor of the overall trend of the markets because you will find a lot better entries. Although, this does work against the trend at times as well. Additionally, this tool also works in consolidating markets which is beneficial.
- After becoming used to the script, i would say to apply it twice to your screen and have one looking for Long entries and the other looking for Short entries.
- As the user, you have the ability to remove the labels in the parameter settings (because it does look quite messy onscreen, especially if you have both long and short entries on at the same time). I would only personally show the labels when price hits the current box to see what confirmations have been identified.
- I will also provide the best parameters to use. You will only need one set of parameters for each long and short setting, as these parameters are universal for any time frame and any financial market.
FIRST UPDATE
After extensive back testing using our first version, we found that in fact, there are some great opportunities being wasted as the entry box stays red. This is due to some series of market structure that don't always fit our theory of continuations within the market. We found that although our theory is accurate, the amount of times the market fits this is more rare than times when price follows sequences. When we look for sequences in the market instead of specifying differences between impulses and corrections, we actually see areas of serious repetitiveness, thanks to how our indicator initially generates. Not how it confirms. So, understanding this new theory through one component of our previous indicator, we are still able to keep boxes at the same area yet accurately confirm more profitable entries external to our full previous strategy.
Moving towards the practical side of things:
-Make sure "add extra confirmation" parameter is selected, as this will allow the indicator to search for more valid entries rather than just our normal confirmations. (this is a tick box).
- Default parameters are already set for both C1 and C2
In a simple sense, this update is added to find more confirmations to turn more red boxes into green boxes based on other theories outside of our original one. How we do this exactly is part of the mystery.
SECOND UPDATE
- Fibonacci based moving average: using elements of the Fibonacci sequence and its relevance to being a hot-spot in price activity, we have integrated this into a moving average which is stronger than your usual MA. Here, you will notice it showing stronger signs of rejecting price, especially when trending. Hence, this is extremely useful to implement into your strategy as part of the trend identification. When price is consolidating, depending on how volatile or close-in the waves are during these periods, the FMA is similar to your typical MA, so therefore not so good. But the overall intention of this is to enhance your conclusion to whether price is trending and whether price is bullish or bearish.
- This is now a strategy, not just an indicator: So now we can choose from a huge variety of parameters in accordance to what ones work best with what pair, or time frame. The typical parameters to change would be the entry points, stop losses and take profits. We have also added in a "SL to entry" option. ALL PARAMETERS ARE FIBONACCI LEVELS AS THIS MAKES IT UNIVERSAL TO ANY PAIR/ TIME FRAME.
- Move the entry boxes : So this is very useful for certain pairs and mainly to help the user understand key sequences on a quantitative level. Sometimes we can notice that pairs spike higher than the typical entry (0.618) so we have allowed flexibility to the point where you can alter the box appearance to either the 0.618 level (default), 0.786 and the 0.9 level.
- Back-testing: Now the user can back-test the strategy and see the performance within any financial market you add this to! Please note that according to the strategy, once a trade is placed, it wont enter any more trades when the current one is still active. I have requested to change this, but it is out of our development team's reach. However, this doesn't discredit what the system can help you achieve, as you will still be able to find profitable parameters within the financial markets.
Strategy default properties
Backtest start: this date is when you would like to start the backtest, however, the indicator will go as far as the data can be read
Backtest end: choose your date to end the back test.
Trade session: choose the trading session you want this strategy to work on.
Filter by session: you can filter the backtested results depending on whether you want the strategy to take trades within the chosen trading session.
Filter by Fibonacci moving average: select this if you would like for the back tested results to consider whether the valid trade setups are in accordance to what the FMA displays (Bullish or Bearish). This is deselected.
Fibonacci Moving Average Timeframe: here you can select what timeframe you would like the FMA to work on, default is the “same as chart” button/ option.
TraderDirection: choose whether you would like LONG or SHORT entries for the indicator to find.
Max risk per trade: choose the risk setting per trade, i would suggest lowering this to 1% ((MODERATOR) This is the default setting!)
EntryFib: choose between the options as to where you would like the strategy to enter positions, the default is the 0.618 zone which is the closest side of the box to price. You will also see that when you choose to change this, the boxes on your screen will move accordingly. A very helpful function!
StopFib: choose your Stop Loss based on the same Fibonacci level as what you choose for your entry, remember that the higher the fib level, the higher (or safer) your Stop Loss is from price spiking. It all comes down to preference.
TakeProfitFib: choose your Take Profit based on the same Fibonacci level as what you choose for your entry, remember that the lower the fib level, the higher your Take Profit is again, It all comes down to preference.
BreakevenFib: the default setting is on “disabled” however when you select a certain Fibonacci level, once price reaches there during the active trade, your Stop Loss will be set to entry, this function is designed to stop volatile price fluctuations rendering your in-profit trade result to hitting your Stop Loss and losing when it closes out.
TradePro's Trading Idea Cipher Divergence EMA Pb StrategyHere I present you on of Trade Pro's Trading Idea: Cipher B+ Divergence EMA Pullback Strategy.
Optimized the crypto pairBTC/USDT in the 30 minute chart.
There is the possibility to switch between short and long positions.
You can choose between 2 different take profit/stop loss types: The Lowest Low/ Highest High Stop Loss/ Take Profit and the ATR Take Profit/ Stop Loss.
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How does the strategy work?
ENTRY
Long
The price must be above the 200 EMA .
The price needs to make a pullback into the 50 EMA .
Right after that, the Cipher B indicator must give a buy signal, it must be below the zero line and the Money Flow cloud must be green.
Short
Contrary to the premise of long positions.
EXIT
Lowest Low/ Highest High Exit
The Lowest Low (long) / highest high (short) serves as the stop loss. The TP is formed on the basis of a factor.
(Long for example: *Lowest Low* multiplied by *Profitfactor* = TP).
ATR Exit
The value of ATR at the time of buying is multiplied by the value entered in "Profit factor ATR" and "Stop factor ATR". As soon as the price reaches this value, it is closed.
Important
The script must be optimized for each coin or currency pair. However, only the values for the profit factor, the stop loss and Lowest Low / Highest High are relevant.
Also, by changing the Chanel Length and the Chanel Average, you can create strong profit changes.
The results of the strategy are without commissions and leverage.
If you have any questions or feedback, please let me know in the comments.
If you need more information about the strategy and want to know exactly how to apply it, check out my profile. There I have created a tutorial for the function of the script.
SIDD-Master-Moving-AverageSIDD-Master-Moving-Average is based on RSI average calculation Moving average plotted on chart.
This Moving Average is giving 2 signals Bullish and Bearish .
Whenever Bullish signals is coming price is doing crossover with moving average on upside and this indicate price will go up from current market price.
Whenever Bearish signals is coming price is doing cross-under with moving average on downside and this indicate price will go down from current market price.
Moving average color is changing based on upside movement or downside movement , for upside its green and for downside its orange color.
This indicator i have created with stop loss line means any price close below cross line for bullish position then that trade should be closed and take the stop loss. similarly for Bearish trade and candle close above stop loss line means trade should be closed and take the stop loss.
Stop loss i have taken care with ATR and Super trend you can see the settings.
I have defined setting for general use of indicator if any modification on setting then result may vary.
Its multi time frame moving average. And I have given time frame for indicator as well so if any trending move need to capture then that setting need to be increased with respect to chart time frame else keep it same.
I have added commission and slippages as well in indicator.
Ping me or DM me to subscribe this indicator.
I have given all my indicator details below link (Signature URL). You can check indicators and call me on given number or email me on given email to access the scripts and indicators. Telegram link is also given you can ping me there.
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Disclaimer : Past performance of the indicator is not giving guarantee for future performance as well, it may change as per market condition.
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888 BOT #backtest█ 888 BOT #backtest
This is an Expert Advisor 'EA' or Automated trading script for ‘longs’ and ‘shorts’, which uses only a Take Profit or, in the worst case, a Stop Loss to close the trade.
It's a much improved version of the previous ‘Repanocha’. It doesn`t use 'Trailing Stop' or 'security()' functions (although using a security function doesn`t mean that the script repaints) and all signals are confirmed, therefore the script doesn`t repaint in alert mode and is accurate in backtest mode.
Apart from the previous indicators, some more and other functions have been added for Stop-Loss, re-entry and leverage.
It uses 8 indicators, (many of you already know what they are, but in case there is someone new), these are the following:
1. Jurik Moving Average
It's a moving average created by Mark Jurik for professionals which eliminates the 'lag' or delay of the signal. It's better than other moving averages like EMA, DEMA, AMA or T3.
There are two ways to decrease noise using JMA. Increasing the 'LENGTH' parameter will cause JMA to move more slowly and therefore reduce noise at the expense of adding 'lag'
The 'JMA LENGTH', 'PHASE' and 'POWER' parameters offer a way to select the optimal balance between 'lag' and over boost.
Green: Bullish, Red: Bearish.
2. Range filter
Created by Donovan Wall, its function is to filter or eliminate noise and to better determine the price trend in the short term.
First, a uniform average price range 'SAMPLING PERIOD' is calculated for the filter base and multiplied by a specific quantity 'RANGE MULTIPLIER'.
The filter is then calculated by adjusting price movements that do not exceed the specified range.
Finally, the target ranges are plotted to show the prices that will trigger the filter movement.
Green: Bullish, Red: Bearish.
3. Average Directional Index (ADX Classic) and (ADX Masanakamura)
It's an indicator designed by Welles Wilder to measure the strength and direction of the market trend. The price movement is strong when the ADX has a positive slope and is above a certain minimum level 'ADX THRESHOLD' and for a given period 'ADX LENGTH'.
The green color of the bars indicates that the trend is bullish and that the ADX is above the level established by the threshold.
The red color of the bars indicates that the trend is down and that the ADX is above the threshold level.
The orange color of the bars indicates that the price is not strong and will surely lateralize.
You can choose between the classic option and the one created by a certain 'Masanakamura'. The main difference between the two is that in the first it uses RMA () and in the second SMA () in its calculation.
4. Parabolic SAR
This indicator, also created by Welles Wilder, places points that help define a trend. The Parabolic SAR can follow the price above or below, the peculiarity that it offers is that when the price touches the indicator, it jumps to the other side of the price (if the Parabolic SAR was below the price it jumps up and vice versa) to a distance predetermined by the indicator. At this time the indicator continues to follow the price, reducing the distance with each candle until it is finally touched again by the price and the process starts again. This procedure explains the name of the indicator: the Parabolic SAR follows the price generating a characteristic parabolic shape, when the price touches it, stops and turns (SAR is the acronym for 'stop and reverse'), giving rise to a new cycle. When the points are below the price, the trend is up, while the points above the price indicate a downward trend.
5. RSI with Volume
This indicator was created by LazyBear from the popular RSI.
The RSI is an oscillator-type indicator used in technical analysis and also created by Welles Wilder that shows the strength of the price by comparing individual movements up or down in successive closing prices.
LazyBear added a volume parameter that makes it more accurate to the market movement.
A good way to use RSI is by considering the 50 'RSI CENTER LINE' centerline. When the oscillator is above, the trend is bullish and when it is below, the trend is bearish.
6. Moving Average Convergence Divergence (MACD) and (MAC-Z)
It was created by Gerald Appel. Subsequently, the histogram was added to anticipate the crossing of MA. Broadly speaking, we can say that the MACD is an oscillator consisting of two moving averages that rotate around the zero line. The MACD line is the difference between a short moving average 'MACD FAST MA LENGTH' and a long moving average 'MACD SLOW MA LENGTH'. It's an indicator that allows us to have a reference on the trend of the asset on which it is operating, thus generating market entry and exit signals.
We can talk about a bull market when the MACD histogram is above the zero line, along with the signal line, while we are talking about a bear market when the MACD histogram is below the zero line.
There is the option of using the MAC-Z indicator created by LazyBear, which according to its author is more effective, by using the parameter VWAP (volume weighted average price) 'Z-VWAP LENGTH' together with a standard deviation 'STDEV LENGTH' in its calculation.
7. Volume Condition
Volume indicates the number of participants in this war between bulls and bears, the more volume the more likely the price will move in favor of the trend. A low trading volume indicates a lower number of participants and interest in the instrument in question. Low volumes may reveal weakness behind a price movement.
With this condition, those signals whose volume is less than the volume SMA for a period 'SMA VOLUME LENGTH' multiplied by a factor 'VOLUME FACTOR' are filtered. In addition, it determines the leverage used, the more volume, the more participants, the more probability that the price will move in our favor, that is, we can use more leverage. The leverage in this script is determined by how many times the volume is above the SMA line.
The maximum leverage is 8.
8. Bollinger Bands
This indicator was created by John Bollinger and consists of three bands that are drawn superimposed on the price evolution graph.
The central band is a moving average, normally a simple moving average calculated with 20 periods is used. ('BB LENGTH' Number of periods of the moving average)
The upper band is calculated by adding the value of the simple moving average X times the standard deviation of the moving average. ('BB MULTIPLIER' Number of times the standard deviation of the moving average)
The lower band is calculated by subtracting the simple moving average X times the standard deviation of the moving average.
the band between the upper and lower bands contains, statistically, almost 90% of the possible price variations, which means that any movement of the price outside the bands has special relevance.
In practical terms, Bollinger bands behave as if they were an elastic band so that, if the price touches them, it has a high probability of bouncing.
Sometimes, after the entry order is filled, the price is returned to the opposite side. If price touch the Bollinger band in the same previous conditions, another order is filled in the same direction of the position to improve the average entry price, (% MINIMUM BETTER PRICE ': Minimum price for the re-entry to be executed and that is better than the price of the previous position in a given %) in this way we give the trade a chance that the Take Profit is executed before. The downside is that the position is doubled in size. 'ACTIVATE DIVIDE TP': Divide the size of the TP in half. More probability of the trade closing but less profit.
█ STOP LOSS and RISK MANAGEMENT.
A good risk management is what can make your equity go up or be liquidated.
The % risk is the percentage of our capital that we are willing to lose by operation. This is recommended to be between 1-5%.
% Risk: (% Stop Loss x % Equity per trade x Leverage) / 100
First the strategy is calculated with Stop Loss, then the risk per operation is determined and from there, the amount per operation is calculated and not vice versa.
In this script you can use a normal Stop Loss or one according to the ATR. Also activate the option to trigger it earlier if the risk percentage is reached. '% RISK ALLOWED'
'STOP LOSS CONFIRMED': The Stop Loss is only activated if the closing of the previous bar is in the loss limit condition. It's useful to prevent the SL from triggering when they do a ‘pump’ to sweep Stops and then return the price to the previous state.
█ BACKTEST
The objective of the Backtest is to evaluate the effectiveness of our strategy. A good Backtest is determined by some parameters such as:
- RECOVERY FACTOR: It consists of dividing the 'net profit' by the 'drawdown’. An excellent trading system has a recovery factor of 10 or more; that is, it generates 10 times more net profit than drawdown.
- PROFIT FACTOR: The ‘Profit Factor’ is another popular measure of system performance. It's as simple as dividing what win trades earn by what loser trades lose. If the strategy is profitable then by definition the 'Profit Factor' is going to be greater than 1. Strategies that are not profitable produce profit factors less than one. A good system has a profit factor of 2 or more. The good thing about the ‘Profit Factor’ is that it tells us what we are going to earn for each dollar we lose. A profit factor of 2.5 tells us that for every dollar we lose operating we will earn 2.5.
- SHARPE: (Return system - Return without risk) / Deviation of returns.
When the variations of gains and losses are very high, the deviation is very high and that leads to a very poor ‘Sharpe’ ratio. If the operations are very close to the average (little deviation) the result is a fairly high 'Sharpe' ratio. If a strategy has a 'Sharpe' ratio greater than 1 it is a good strategy. If it has a 'Sharpe' ratio greater than 2, it is excellent. If it has a ‘Sharpe’ ratio less than 1 then we don't know if it is good or bad, we have to look at other parameters.
- MATHEMATICAL EXPECTATION: (% winning trades X average profit) + (% losing trades X average loss).
To earn money with a Trading system, it is not necessary to win all the operations, what is really important is the final result of the operation. A Trading system has to have positive mathematical expectation as is the case with this script: ME = (0.87 x 30.74$) - (0.13 x 56.16$) = (26.74 - 7.30) = 19.44$ > 0
The game of roulette, for example, has negative mathematical expectation for the player, it can have positive winning streaks, but in the long term, if you continue playing you will end up losing, and casinos know this very well.
PARAMETERS
'BACKTEST DAYS': Number of days back of historical data for the calculation of the Backtest.
'ENTRY TYPE': For '% EQUITY' if you have $ 10,000 of capital and select 7.5%, for example, your entry would be $ 750 without leverage. If you select CONTRACTS for the 'BTCUSDT' pair, for example, it would be the amount in 'Bitcoins' and if you select 'CASH' it would be the amount in $ dollars.
'QUANTITY (LEVERAGE 1X)': The amount for an entry with X1 leverage according to the previous section.
'MAXIMUM LEVERAGE': It's the maximum allowed multiplier of the quantity entered in the previous section according to the volume condition.
The settings are for Bitcoin at Binance Futures (BTC: USDTPERP) in 30 minutes.
For other pairs and other timeframes, the settings have to be adjusted again. And within a month, the settings will be different because we all know the market and the trend are changing.
█ 888 BOT (SPANISH)
Este es un Expert Advisor 'EA' o script de trading automatizado para ‘longs’ y ‘shorts’, el cual, utiliza solo un Take Profit o, en el peor de los casos, un Stop Loss para cerrar el trade.
Es una versión muy mejorada del anterior ‘Repanocha’. No utiliza ‘Trailing Stop’, ni funciones ‘security()’ (aunque usar una función security no significa que el script repinte) y todas las señales son confirmadas, por consiguiente, el script no repinta en modo alertas y es preciso en en el modo backtest.
Aparte de los anteriores indicadores se han añadido algunos más y otras funciones para Stop-Loss, de re-entrada y apalancamiento.
Utiliza 8 indicadores, (muchos ya sabéis sobradamente lo que son, pero por si hay alguien nuevo), son los siguientes:
1. Jurik Moving Average
Es una media móvil creada por Mark Jurik para profesionales la cual elimina el ‘lag’ o retardo de la señal. Es mejor que otras medias móviles como la EMA, DEMA, AMA o T3.
Hay dos formas de disminuir el ruido utilizando JMA. El aumento del parámetro 'LENGTH' hará que JMA se mueva más lentamente y, por lo tanto, reducirá el ruido a expensas de añadir ‘lag’
Los parámetros 'JMA LENGTH', 'PHASE' y 'POWER' ofrecen una forma de seleccionar el equilibrio óptimo entre ‘lag’ y sobre impulso.
Verde : Alcista, Rojo: Bajista.
2. Range filter
Creado por Donovan Wall, su función es la de filtrar o eliminar el ruido y poder determinar mejor la tendencia del precio a corto plazo.
Primero, se calcula un rango de precio promedio uniforme 'SAMPLING PERIOD' para la base del filtro y se multiplica por una cantidad específica 'RANGE MULTIPLIER'.
A continuación, el filtro se calcula ajustando los movimientos de precios que no exceden el rango especificado.
Por último, los rangos objetivo se trazan para mostrar los precios que activarán el movimiento del filtro.
Verde : Alcista, Rojo: Bajista.
3. Average Directional Index (ADX Classic) y (ADX Masanakamura)
Es un indicador diseñado por Welles Wilder para medir la fuerza y dirección de la tendencia del mercado. El movimiento del precio tiene fuerza cuando el ADX tiene pendiente positiva y está por encima de cierto nivel mínimo 'ADX THRESHOLD' y para un periodo dado 'ADX LENGTH'.
El color verde de las barras indica que la tendencia es alcista y que el ADX está por encima del nivel establecido por el threshold.
El color Rojo de las barras indica que la tendencia es bajista y que el ADX está por encima del nivel de threshold.
El color naranja de las barras indica que el precio no tiene fuerza y seguramente lateralizará.
Se puede elegir entre la opción clásica y la creada por un tal 'Masanakamura'. La diferencia principal entre los dos es que en el primero utiliza RMA() y en el segundo SMA() en su cálculo.
4. Parabolic SAR
Este indicador, creado también por Welles Wilder, coloca puntos que ayudan a definir una tendencia. El Parabolic SAR puede seguir al precio por encima o por debajo, la particularidad que ofrece es que cuando el precio toca al indicador, este salta al otro lado del precio (si el Parabolic SAR estaba por debajo del precio salta arriba y viceversa) a una distancia predeterminada por el indicador. En este momento el indicador vuelve a seguir al precio, reduciendo la distancia con cada vela hasta que finalmente es tocado otra vez por el precio y se vuelve a iniciar el proceso. Este procedimiento explica el nombre del indicador: el Parabolic SAR va siguiendo al precio generando una característica forma parabólica, cuando el precio lo toca, se para y da la vuelta (SAR son las siglas en inglés de ‘stop and reverse’), dando lugar a un nuevo ciclo. Cuando los puntos están por debajo del precio, la tendencia es alcista, mientras que los puntos por encima del precio indica una tendencia bajista.
5. RSI with Volume
Este indicador lo creo un tal LazyBear de TV a partir del popular RSI.
El RSI es un indicador tipo oscilador utilizado en análisis técnico y creado también por Welles Wilder que muestra la fuerza del precio mediante la comparación de los movimientos individuales al alza o a la baja de los sucesivos precios de cierre.
LazyBear le añadió un parámetro de volumen que lo hace más preciso al movimiento del mercado.
Una buena forma de usar el RSI es teniendo en cuenta la línea central de 50 'RSI CENTER LINE'. Cuando el oscilador está por encima, la tendencia es alcista y cuando está por debajo la tendencia es bajista.
6. Moving Average Convergence Divergence (MACD) y (MAC-Z)
Fue creado por Gerald Appel. Posteriormente se añadió el histograma para anticipar el cruce de medias. A grandes rasgos podemos decir que el MACD es un oscilador consistente en dos medias móviles que van girando en torno a la línea de cero. La línea del MACD no es más que la diferencia entre una media móvil corta 'MACD FAST MA LENGTH' y una media móvil larga 'MACD SLOW MA LENGTH'. Es un indicador que nos permite tener una referencia sobre la tendencia del activo sobre el cual se está operando, generando de este modo señales de entrada y salida del mercado.
Podemos hablar de mercado alcista cuando el histograma del MACD se sitúe por encima de la línea cero, junto con la línea de señal, mientras que hablaremos de mercado bajista cuando el histograma MACD se situará por debajo de la línea cero.
Está la opción de utilizar el indicador MAC-Z creado por LazyBear que según su autor es más eficaz, por utilizar el parámetro VWAP (precio medio ponderado por volumen) 'Z-VWAP LENGTH' junto con una desviación standard 'STDEV LENGTH' en su cálculo.
7. Volume Condition
El volumen indica el número de participantes en esta guerra entre toros y osos, cuanto más volumen más probabilidad de que se mueva el precio a favor de la tendencia. Un volumen bajo de negociación indica un menor número de participantes e interés por el instrumento en cuestión. Los bajos volúmenes pueden revelar debilidad detrás de un movimiento de precios.
Con esta condición se filtran aquellas señales cuyo volumen es inferior a la SMA de volumen para un periodo 'SMA VOLUME LENGTH' multiplicado por un factor 'VOLUME FACTOR'. Además, determina el apalancamiento utilizado, a más volumen, más participantes, más probabilidad de que se mueva el precio a nuestro favor, es decir, podemos utilizar más apalancamiento. El apalancamiento en este script lo determina las veces que está el volumen por encima de la línea de la SMA.
El apalancamiento máximo es de 8.
8. Bollinger Bands
Este indicador fue creado por John Bollinger y consiste en tres bandas que se dibujan superpuestas al gráfico de evolución del precio.
La banda central es una media móvil, normalmente se emplea una media móvil simple calculada con 20 períodos. ('BB LENGTH' Número de periodos de la media móvil)
La banda superior se calcula sumando al valor de la media móvil simple X veces la desviación típica de la media móvil. ('BB MULTIPLIER' Número de veces la desviación típica de la media móvil)
La banda inferior de calcula restando a la media móvil simple X veces la desviación típica de la media móvil.
la franja comprendida entre las bandas superior e inferior contiene, estadísticamente, casi un 90% de las posibles variaciones del precio, lo que significa que cualquier movimiento del precio fuera de las bandas tiene especial relevancia.
En términos prácticos, las bandas de Bollinger se comporta como si de una banda elástica se tratara de manera que, si el precio las toca, éste tiene mucha probabilidad de rebotar.
En ocasiones, después de rellenarse la orden de entrada, el precio se devuelve hacia el lado contrario. Si toca la banda de Bollinger se rellena otra orden en la misma dirección de la posición para mejorar el precio medio de entrada, (% MINIMUM BETTER PRICE': Precio mínimo para que se ejecute la re-entrada y que sea mejor que el precio de la posición anterior en un % dado) de esta manera damos una oportunidad al trade de que el Take Profit se ejecute antes. La desventaja es que se dobla el tamaño de la posición. 'ACTIVATE DIVIDE TP': Divide el tamaño del TP a la mitad. Más probabilidad de que se cierre el trade pero menos ganancias.
█ STOP LOSS y RISK MANAGEMENT.
Una buena gestión de las pérdidas o gestión del riesgo es lo que puede hacer que tu cuenta suba o se liquide en poco tiempo.
El % de riesgo es el porcentaje de nuestro capital que estamos dispuestos a perder por operación. Este se aconseja que debe estar comprendido entre un 1-5%.
% Risk = (% Stop Loss x % Equity per trade x Leverage) / 100
Primero se calcula la estrategia con Stop Loss, después se determina el riesgo por operación y a partir de ahí se calcula el monto por operación y no al revés.
En este script puedes usar un Stop Loss normal o uno según el ATR. También activar la opción de que salte antes si se alcanza el porcentaje de riesgo. '% RISK ALLOWED'
'STOP LOSS CONFIRMED': Solamente se activa el Stop Loss si el cierre de la barra anterior se encuentra en la condición de límite de pérdidas. Es útil para evitar que se dispare el SL cuando hacen un ‘pump’ para barrer Stops y luego se devuelve el precio a la normalidad.
█ BACKTEST
El objetivo del Backtest es evaluar la eficacia de nuestra estrategia. Un buen Backtest lo determinan algunos parámetros como son:
- RECOVERY FACTOR: Consiste en dividir el ‘beneficio neto’ entre el ‘drawdown’. Un excelente sistema de trading tiene un recovery factor de 10 o más; es decir, genera 10 veces más beneficio neto que drawdown.
- PROFIT FACTOR: El ‘Profit Factor’ es otra medida popular del rendimiento de un sistema. Es algo tan simple como dividir lo que ganan las operaciones con ganancias entre lo que pierden las operaciones con pérdidas. Si la estrategia es rentable entonces por definición el ‘Profit Factor’ va a ser mayor que 1. Las estrategias que no son rentables producen factores de beneficio menores que uno. Un buen sistema tiene un profit factor de 2 o más. Lo bueno del ‘Profit Factor’ es que nos dice lo que vamos a ganar por cada dolar que perdemos. Un profit factor de 2.5 nos dice que por cada dolar que perdamos operando vamos a ganar 2.5.
- SHARPE: (Retorno sistema – Retorno sin riesgo) / Desviación de los retornos.
Cuando las variaciones de ganancias y pérdidas son muy altas, la desviación es muy elevada y eso conlleva un ratio de ‘Sharpe’ muy pobre. Si las operaciones están muy cerca de la media (poca desviación) el resultado es un ratio de ‘Sharpe’ bastante elevado. Si una estrategia tiene un ratio de ‘Sharpe’ mayor que 1 es una buena estrategia. Si tiene un ratio de ‘Sharpe’ mayor que 2, es excelente. Si tiene un ratio de ‘Sharpe’ menor que 1 entonces no sabemos si es buena o mala, hay que mirar otros parámetros.
- MATHEMATICAL EXPECTATION:(% operaciones ganadoras X ganancia media) + (% operaciones perdedoras X pérdida media).
Para ganar dinero con un sistema de Trading, no es necesario ganar todas las operaciones, lo verdaderamente importante es el resultado final de la operativa. Un sistema de Trading tiene que tener esperanza matemática positiva como es el caso de este script.
El juego de la ruleta, por ejemplo, tiene esperanza matemática negativa para el jugador, puede tener rachas positivas de ganancias, pero a la larga, si se sigue jugando se acabará perdiendo, y esto los casinos lo saben muy bien.
PARAMETROS
'BACKTEST DAYS': Número de días atrás de datos históricos para el calculo del Backtest.
'ENTRY TYPE': Para % EQUITY si tienes 10000$ de capital y seleccionas 7.5% tu entrada sería de 750$ sin apalancamiento. Si seleccionas CONTRACTS para el par BTCUSDT sería la cantidad en Bitcoins y si seleccionas CASH sería la cantidad en dólares.
'QUANTITY (LEVERAGE 1X)': La cantidad para una entrada con apalancamiento X! según el apartado anterior.
'MAXIMUM LEVERAGE': Es el máximo multiplicador permitido de la cantidad introducida en el apartado anterior según la condición de volumen.
Los settings son para Bitcoin en Binance Futures (BTC:USDTPERP) en 30 minutos.
Para otro pares y otras temporalidades se tienen que ajustar las opciones de nuevo. Además para dentro de un mes, los ajustes serán otros distintos ya que el mercado y la tendencia es cambiante.
MACKAVELLI Algorithmic StrategyI had a hard time finding a strategy that would work in different time-frames and multiple different currency pairs. This is what I ended up with after countless hours of research and testing. I designed this strategy for auto-algorithmic trading and it uses three different indicators for Long/Short positions and a 4th indicator for exiting positions.
1) A green-light indicator that tells you whether or not you can go long/short.
2) A confirmation indicator that executes the long/short positions.
3) A chop indicator that measures the distance between the two MA's. When they're too close a trade will not be executed, as this usually indicates a chop zone.
4) A third MA is used as an exit indicator.
5) Finally, a loop function is designed to prevent repeat signals. Once a signal is produced on bar close it cannot happen again until that trade is closed or a new position is opened.
Be advised, you need to adjust the settings for each currency pair and time-frame. Once you do that, back-test it and count the last 100 trades to determine accurately your wins/losses. Long entries are the top of the previous bar, shorts are the bottom of the previous bar. This is a more accurate way of counting wins/losses. Tradingview back-test's are not accurate because of where they estimate your long/short entries are, it's very misleading.
Right now I have it setup for 2hr USDCAD with a back-test of 58% win rate on the last 100 trades.
The chop indicator is set to 0.04, I suggest starting there. 0.03 is the lowest I would go. You can go all the way up to 0.1 and higher if needed. You'll start missing big trades though.
The EMA for green-light signal is set to 10, a lower number will give you more entries but less accurate results, bigger number will give less entries with more accuracy but with missed opportunities. 10 is a good starting point.
This strategy is also designed so you can use tight stop losses to prevent large losses. This is because the strategy typically catches trends on the way up/down, minimizing risk for reversal.
I use Heikin Ashi candles for a smoother chart to work with. I have not tested this strategy with normal candles.
DEMO this strategy before using it live and make sure you back-test and tune it before you start. This is written in PINE V3 SO IT WILL NOT REPAINT.
ParabolicSAR+EMA[TS_Indie]🚀 EMA + Parabolic SAR Reversal Trading Strategy
This trading system effectively combines the use of Exponential Moving Averages (EMA) with the Parabolic SAR to identify both price trends and key reversal points. The EMA Fast is used to signal the primary short-term trend, while the EMA Slow acts as a filter for the long-term trend direction. The Parabolic SAR then helps to confirm the reversal signals.
🛠️ Tools Used
1. EMA Fast – Primary Short-Term Trend
2. EMA Slow – Long-Term Trend Filter
3. Parabolic SAR – Reversal Confirmation
🎯 Entry Rules
📈 Buy Setup
1. Trend Filter: EMA Fast > EMA Slow → Uptrend
2. Pullback: Price pulls back and closes below the EMA Fast line.
3. Reversal: Price reverses/pulls back up and closes above the EMA Fast line.
4. SAR Confirmation: The previous Parabolic SAR dot is above the high, and the dot in the current candle is below the low → Reversal signal confirmed.
5. Entry: Enter Buy immediately.
📉 Sell Setup
1. Trend Filter: EMA Fast < EMA Slow → Downtrend
2. Pullback: Price pulls back and closes above the EMA Fast line.
3. Reversal: Price reverses/pulls back down and closes below the EMA Fast line.
4. SAR Confirmation: The previous Parabolic SAR dot is below the low, and the dot in the current candle is above the high → Reversal signal confirmed.
5. Entry: Enter Sell immediately.
💰 Exit Management (Entry, Stop Loss, Take Profit)
1. Entry: Enter the order at the closing price of the signal candle.
2. Stop Loss (SL): Set the Stop Loss at the Parabolic SAR dot.
3. Take Profit (TP): Calculated from the Entry and Stop Loss points, multiplied by the Risk Reward Ratio.
⚙️ Optional Parameters
➭ Custom Risk/Reward Ratio for Take Profit.
➭ Option to add an ATR buffer to the Stop Loss.
➭ Adjustable EMA Fast period.
➭ Adjustable EMA Slow period.
➭ Adjustable Parabolic SAR parameters.
➭ Option to enable Long-only / Short-only positions.
➭ Customizable Backtest start and end date.
➭ Customizable trading session time.
🔔 Alert Function
Alerts display:
➭ Entry Price
➭ Stop Loss Price
➭ Take Profit Price
💡 This strategy allows for many parameter adjustments, such as the MA type, adding/subtracting from the Stop Loss using ATR, and selecting specific sessions for backtesting. If you find interesting or profitable results after adjusting the parameters, please share your comments with other traders!
⚠️ Disclaimer
This indicator is designed for educational and research purposes only. It does not guarantee profits and should not be considered financial advice. Trading in financial markets involves significant risk , including the potential loss of capital.
Quasimodo Pattern Strategy Back Test [TradingFinder] QM Trading🔵 Introduction
The QM pattern, also known as the Quasimodo pattern, is one of the popular patterns in price action, and it is often used by technical analysts. The QM pattern is used to identify trend reversals and provides a very good risk-to-reward ratio. One of the advantages of the QM pattern is its high frequency and visibility in charts.
Additionally, due to its strength, it is highly profitable, and as mentioned, its risk-to-reward ratio is very good. The QM pattern is highly popular among traders in supply and demand, and traders also use this pattern.
The Price Action QM pattern, like other Price Action patterns, has two types: Bullish QM and Bearish QM patterns. To identify this pattern, you need to be familiar with its types to recognize it.
🔵 Identifying the QM Pattern
🟣 Bullish QM
In the bullish QM pattern, as you can see in the image below, an LL and HH are formed. As you can see, the neckline is marked as a dashed line. When the price reaches this range, it will start its upward movement.
🟣 Bearish QM
The Price Action QM pattern also has a bearish pattern. As you can see in the image below, initially, an HH and LL are formed. The neckline in this image is the dashed line, and when the LL is formed, the price reaches this neckline. However, it cannot pass it, and the downward trend resumes.
🔵 How to Use
The Quasimodo pattern is one of the clearest structures used to identify market reversals. It is built around the concept of a structural break followed by a pullback into an area of trapped liquidity. Instead of relying on lagging indicators, this pattern focuses purely on price action and how the market reacts after exhausting one side of liquidity. When understood correctly, it provides traders with precise entry points at the transition between trend phases.
🟣 Bullish Quasimodo
A bullish Quasimodo forms after a clear downtrend when sellers start losing control. The market continues to make lower lows until a sudden higher high appears, signaling that buyers are entering with strength. Price then pulls back to retest the previous low, creating what is known as the Quasimodo low.
This area often becomes the final trap for sellers before the market shifts upward. A visible rejection or displacement from this zone confirms bullish momentum. Traders usually place entries near this level, stops below the low, and targets at previous highs or the next resistance zone. Combining the setup with demand zones or Fair Value Gaps increases its accuracy.
🟣 Bearish Quasimodo
A bearish Quasimodo forms near the top of an uptrend when buyers begin to lose strength. The market continues to make higher highs until a sudden lower low breaks the bullish structure, showing that selling pressure is entering the market. Price then retraces upward to retest the previous high, forming the Quasimodo high, where breakout buyers are often trapped.
Once rejection appears at this level, it indicates a likely reversal. Traders can enter short near this area, with stop-losses placed above the high and targets near the next support or previous lows. The setup gains more reliability when aligned with supply zones, SMT divergence, or bearish Fair Value Gaps.
🔵 Setting
Pivot Period : You can use this parameter to use your desired period to identify the QM pattern. By default, this parameter is set to the number 5.
Take Profit Mode : You can choose your desired Take Profit in three ways. Based on the logic of the QM strategy, you can select two Take Profit levels, TP1 and TP2. You can also choose your take profit based on the Reward to Risk ratio. You must enter your desired R/R in the Reward to Risk Ratio parameter.
Stop Loss Refine : The loss limit of the QM strategy is based on its logic on the Head pattern. You can refine it using the ATR Refine option to prevent Stop Hunt. You can enter your desired coefficient in the Stop Loss ATR Adjustment Coefficient parameter.
Reward to Risk Ratio : If you set Take Profit Mode to R/R, you must enter your desired R/R here. For example, if your loss limit is 10 pips and you set R/R to 2, your take profit will be reached when the price is 20 pips away from your entry point.
Stop Loss ATR Adjustment Coefficient : If you set Stop Loss Refine to ATR Refine, you must adjust your loss limit coefficient here. For example, if your buy position's loss limit is at the price of 1000, and your ATR is 10, if you set Stop Loss ATR Adjustment Coefficient to 2, your loss limit will be at the price of 980.
Entry Level Validity : Determines how long the Entry level remains valid. The higher the level, the longer the entry level will remain valid. By default it is 2 and it can be set between 2 and 15.
🔵 Results
The following examples show the backtest results of the Quasimodo (QM) strategy in action. Each image is based on specific settings for the symbol, timeframe, and input parameters, illustrating how the QM logic can generate signals under different market conditions. The detailed configuration for each backtest is also displayed on the image.
⚠ Important Note : Even with identical settings and the same symbol, results may vary slightly across different brokers due to data feed variations and pricing differences.
Default Properties of Backtests :
OANDA:XAUUSD | TimeFrame: 5min | Duration: 1 Year :
BINANCE:BTCUSD | TimeFrame: 5min | Duration: 1 Year :
CAPITALCOM:US30 | TimeFrame: 5min | Duration: 1 Year :
NASDAQ:QQQ | TimeFrame: 5min | Duration: 5 Year :
OANDA:EURUSD | TimeFrame: 5min | Duration: 5 Year :
PEPPERSTONE:US500 | TimeFrame: 5min | Duration: 5 Year :
AlgoIndexOS-ES-FuturesAlgoIndexOS — ES Futures Strategy v2.0 (5-Minute RTH)
Scope (read first)
ES on 5-minute only, RTH session. The strategy operates on U.S. Regular Trading Hours (09:30–16:00 ET) using a 5-minute ES chart. It builds an Opening Session Range (OSR) from the RTH open, then runs a breakout engine when internal quality conditions are met. Exits are target-based with an intrabar touch-to-flat safety. Positions are flattened at the RTH session end by default. Alerts can post JSON to your Webhook URL for automation.
What this is
One intraday engine with four curated presets (“Stages”) tuned for distinct segments of the NY session. Stages keep the core logic consistent while applying time-of-day context and conservative governors. Single invite-only listing; not a multi-post suite.
How it trades (high-level)
Range context: Builds and locks the OSR from the opening bell; entries only arm after the range is set.
Quality gating: Trades only when internal trend/volatility/confirmation conditions align (no parameter disclosure).
Breakout execution: Signals at bar close; bracket exits manage take-profit (limit) with an intrabar “TP-touch” safety to avoid phantom fills; optional stop-loss.
Session safety: Positions flat at RTH close by default (time exit).
(No settings or thresholds are disclosed; presets encapsulate research choices.)
Stages (session templates; one engine)
A single Stage selector chooses among four presets optimized for different parts of the RTH session (morning vs mid-day; long/short focus). Internal parameters remain fixed to preserve tested behavior.
Public inputs (kept minimal)
Stage (choose your preset)
TP / SL (points) shown for transparency; effective values are governed by the selected preset to maintain consistency with research.
Optional display overlays (status line/markers) for readability.
Alerts (how to use)
Create an alert on the strategy and choose Strategy → Order fills. Use a webhook if you want automation. The payload includes the exact chart symbol so it works on ES1! or a specific ES contract:
{
"tv_symbol": "{{ticker}}",
"tv_exchange": "{{exchange}}",
"action": "buy|sell|exit",
"price": {{close}},
"time": "{{timenow}}"
}
If your receiver needs a fixed root (e.g., “ES”), map it on your server using tv_symbol for context.
Backtest & assumptions
Backtest assumptions (initial capital, commission, slippage, margin) are user-configurable in TradingView. Results on your chart reflect your settings. This script evaluates ES fills on 5-minute RTH bars; live execution will differ.
Operating notes
Use on ES only, 5-minute timeframe, RTH session.
If you run multiple Stages, use separate charts/tabs and coordinate net exposure in your own tooling if needed.
Publish with a clean chart for clarity.
Disclosures (compliance)
No investment advice. This script is for research/education and tooling only. It does not provide investment, legal, tax, or accounting advice and does not recommend any security, instrument, or strategy. Use at your own risk.
Hypothetical performance (CFTC 4.41). Hypothetical or simulated results have many limitations, and no representation is made that any account will achieve similar outcomes. Past performance is not necessarily indicative of future results.
Futures risk. Trading futures involves substantial risk of loss and is not suitable for all investors. Leverage, gaps, slippage, and connectivity can cause losses exceeding initial investment.
Backtesting limitations. Results depend on data quality, chart resolution, session filters, and user assumptions; live execution will differ.
Intellectual property. © 2025 AlgoIndex. All Rights Reserved. Redistribution, resale, or decompilation prohibited without written consent.
Adaptive ATR Guardian PRO+ (Locked Lines)🎯 核心交易功能 / Core Trading Features
1. 智能参数配置系统 / Intelligent Parameter Configuration
多风格选择：稳健/激进/保守三种交易风格
Multi-style Selection: Conservative/Aggressive/Moderate trading styles
多时间周期：M5/M15/H1三种时间框架
Multi-timeframe: M5/M15/H1 timeframes
自适应参数：根据风格自动调整所有技术参数
Adaptive Parameters: Automatically adjusts all technical parameters based on style
2. 高级信号生成系统 / Advanced Signal Generation
双均线策略：快慢EMA交叉信号
Dual MA Strategy: Fast/Slow EMA crossover signals
趋势过滤：100周期EMA作为趋势方向过滤
Trend Filter: 100-period EMA for trend direction filtering
ADX强度确认：ADX > 最小值才确认趋势有效
ADX Strength Confirmation: ADX > minimum value for valid trend
交易时段控制：可设置交易开始和结束时间
Trading Session Control: Configurable start and end times
3. 智能风险管理 / Intelligent Risk Management
动态止损：基于ATR的智能止损计算
Dynamic Stop Loss: ATR-based intelligent stop loss calculation
分批止盈：TP1平仓50%，TP2平仓剩余50%
Partial Take Profit: TP1 closes 50%, TP2 closes remaining 50%
追踪止损：TP2部分启用追踪止损功能
Trailing Stop: TP2 portion uses trailing stop functionality
品种自适应：BTC和黄金品种特殊参数调整
Symbol Adaptation: Special parameter adjustments for BTC and Gold
4. 专业订单管理 / Professional Order Management
自动平仓：新信号自动平掉反向仓位
Auto Close: New signals automatically close opposite positions
仓位管理：基于账户权益的百分比仓位
Position Management: Percentage-based position sizing
佣金计算：包含交易佣金成本
Commission Calculation: Includes trading commission costs
📊 高级可视化功能 / Advanced Visualization Features
1. 实时交易线系统 / Real-time Trading Lines System
入场线：蓝色虚线，显示入场价格
Entry Line: Blue dashed line showing entry price
止损线：红色实线，显示止损价格
Stop Loss Line: Red solid line showing stop loss price
TP1线：青色实线，显示第一目标位
TP1 Line: Teal solid line showing first target
TP2线：青色实线，显示第二目标位
TP2 Line: Teal solid line showing second target
2. 智能标签管理 / Intelligent Label Management
动态字号：根据时间周期自动调整标签大小
Dynamic Font Size: Auto-adjusts label size based on timeframe
位置优化：标签固定在入场K线右侧3根位置
Position Optimization: Labels fixed 3 bars right of entry candle
实时更新：线条和标签随图表滚动延伸
Real-time Updates: Lines and labels extend with chart scrolling
3. 专业信息面板 / Professional Information Panel
策略状态：交易风格、时间周期、持仓方向
Strategy Status: Trading style, timeframe, position direction
指标数据：ADX强度、ATR波动率数值
Indicator Data: ADX strength, ATR volatility values
交易信息：入场价格、止损价格、止盈价格
Trade Information: Entry price, stop loss, take profit prices
实时更新：每根K线更新最新数据
Real-time Updates: Updates data on every candle
4. 模式状态标签 / Mode Status Label
顶部状态栏：显示周期、风格、ADX、ATR、持仓状态
Top Status Bar: Shows timeframe, style, ADX, ATR, position status
颜色编码：蓝色主题，专业视觉效果
Color Coding: Blue theme, professional visual appearance
⚙️ 技术特色功能 / Technical Special Features
1. 自适应波动率调整 / Adaptive Volatility Adjustment
ATR基准：基于14周期ATR计算
ATR Baseline: Based on 14-period ATR calculation
波动率调整：ATR相对于50周期均线的调整系数
Volatility Adjustment: ATR adjustment coefficient relative to 50-period MA
动态止盈：止盈距离根据波动率动态调整
Dynamic Take Profit: TP distances dynamically adjusted based on volatility
2. 多品种优化 / Multi-Symbol Optimization
BTC特殊处理：更大的止损倍数和TP2倍数
BTC Special Handling: Larger stop loss and TP2 multipliers
黄金特殊处理：适中的参数调整
Gold Special Handling: Moderate parameter adjustments
通用品种：标准参数适用于其他品种
General Symbols: Standard parameters for other symbols
3. 时间智能控制 / Intelligent Time Control
交易时段：可配置的交易时间窗口
Trading Sessions: Configurable trading time windows
时段逻辑：支持跨午夜的时间段设置
Session Logic: Supports cross-midnight time periods
时间过滤：只在交易时段内产生信号
Time Filtering: Only generates signals during trading hours
4. 内存管理优化 / Memory Management Optimization
自动清理：平仓时自动删除所有线条和标签
Auto Cleanup: Automatically deletes all lines and labels on position close
资源回收：避免图表元素堆积
Resource Recycling: Prevents chart element accumulation
性能优化：高效的实时更新机制
Performance Optimization: Efficient real-time update mechanism
🛡️ 风险控制功能 / Risk Control Features
1. 多层过滤系统 / Multi-layer Filtering System
趋势方向过滤 / Trend direction filtering
ADX强度过滤 / ADX strength filtering
交易时间过滤 / Trading time filtering
品种特性过滤 / Symbol characteristic filtering
2. 动态参数系统 / Dynamic Parameter System
快慢均线周期自适应 / Fast/slow MA period adaptation
止损倍数动态调整 / Stop loss multiplier dynamic adjustment
止盈倍数风格化配置 / Take profit multiplier style-based configuration
追踪止损灵敏度设置 / Trailing stop sensitivity settings
3. 资金管理 / Money Management
固定百分比仓位 / Fixed percentage position sizing
佣金成本计入 / Commission costs included
无金字塔加仓 / No pyramiding (no adding to positions)
自动反向平仓 / Automatic opposite position closing
📈 用户体验功能 / User Experience Features
1. 可视化定制 / Visualization Customization
交易线显示/隐藏开关 / Trading lines show/hide toggle
信息面板显示控制 / Information panel display control
线条延伸长度可调 / Line extension length adjustable
颜色方案统一管理 / Color scheme unified management
2. 实时监控 / Real-time Monitoring
持仓状态实时显示 / Real-time position status display
关键价格水平标记 / Key price level markings
指标数值动态更新 / Indicator values dynamic updates
交易统计信息 / Trading statistics information
3. 专业布局 / Professional Layout
右上角信息面板 / Top-right information panel
顶部状态标签 / Top status label
图表交易线条 / Chart trading lines
整洁的视觉层次 / Clean visual hierarchy
W%R Pullback+EMA Trend [TS_Indie]🔰 Core Concept of the Strategy
The main idea is “Trend-Following with Momentum Pullback.”
This means trading in the direction of the main trend (defined by EMA) while using Williams %R to identify pullback entries (buying the dip or selling the rally) where momentum returns to the trend direction.
📊 Indicators Used
1. EMA Fast – Defines the short-term trend.
2. EMA Slow – Defines the long-term trend (used as a trend filter).
3. Williams %R
• Overbought zone: above -20
• Oversold zone: below -80
⚙️ Entry Rules
🔹 Buy Setup
1. EMA Fast > EMA Slow → Uptrend condition.
2. Williams %R on the previous candle dropped below -80, and on the current candle, it crosses back above -80 → indicates momentum returning to the upside.
3. Current close is above EMA Fast.
4. Entry Buy at the close of the candle where %R crosses above -80.
🎯 Entry, Stop Loss, and Take Profit
1. Entry : At the candle close where the signal occurs.
2. Stop Loss : At the lowest low between the current and previous candles.
3. Take Profit : Calculated based on entry price and stop loss distance multiplied by the Risk/Reward Ratio.
🔹 Sell Setup
1. EMA Fast < EMA Slow → Downtrend condition.
2. Williams %R on the previous candle went above -20, and on the current candle, it crosses back below -20 → indicates renewed selling momentum.
3. Current price is below EMA Fast.
4. Entry Sell at the close of the candle where %R crosses below -20.
🎯 Entry, Stop Loss, and Take Profit
1. Entry : At the candle close where the signal occurs.
2. Stop Loss : At the highest high between the current and previous candles.
3. Take Profit : Calculated based on entry price and stop loss distance multiplied by the Risk/Reward Ratio.
⚙️ Optional Parameters
• Custom Risk/Reward Ratio for Take Profit.
• Option to add ATR buffer to Stop Loss.
• Adjustable EMA Fast period.
• Adjustable EMA Slow period.
• Adjustable Williams %R period.
• Option to enable Long only / Short only positions.
• Customizable Backtest start and end date.
• Customizable trading session time.
⏰ Alert Function
Alerts display:
• Entry price
• Stop Loss price
• Take Profit price
Guys, try adjusting the parameters yourselves!
I’ve been tweaking the settings for several days and managed to get great results on XAU/USD in the 5-minute timeframe.
I think this strategy is quite interesting and could potentially deliver good results on other instruments as well.
⚠️ Disclaimer
This indicator is designed for educational and research purposes only.
It does not guarantee profits and should not be considered financial advice.
Trading in financial markets involves significant risk, including the potential loss of capital.
TurtleTrader Intraday Extended by exp3rts🐢 TurtleTrader Intraday Extended by exp3rts
A modern intraday adaptation of the classic Turtle Trading strategy, optimized for short-term breakout trading with built-in risk management, pyramiding, and optional trend filters.
This strategy captures strong directional moves by entering breakouts from price channels, using ATR-based stop losses and controlled position scaling.
🔑 Key Features:
📈 Channel Breakout Entries: Buy/sell on breakout of highest highs or lowest lows
🛑 Dynamic ATR Stop Loss: Automatically calculated from market volatility
🔁 Pyramiding: Adds up to 4 positions as price moves in your favor
🔄 Directional Mode: Choose Long-only or Short-only mode
🧠 Skip After Win Option: Avoid overtrading by skipping the next entry after a profitable trade
📊 Optional EMA Display: Plot up to 3 EMAs for trend filtering or visual confirmation
📉 On-Chart ATR Label: Displays real-time ATR metrics (including ½N size used in classic Turtle rules)
⚙️ Strategy Inputs:
Entry/Exit channel length
ATR multiplier and period
Entry delay (bar offset)
Optional trade filter after profitable trades
Show/hide EMAs and ATR label
🧪 Best For:
Intraday breakout traders (works well on 5m–1h timeframes)
Traders who prefer mechanical rules and structured risk
Anyone testing volatility-based entries and exits
Inspired by the original Turtle Trading system — redesigned for modern markets with more intraday flexibility and visual enhancements.
QZ Trend (Crypto Edition) v1.1a: Donchian, EMA, ATR, Liquidity/FThe "QZ Trend (Crypto Edition)" is a rules-based trend-following breakout strategy for crypto spot or perpetual contracts, focusing on following trends, prioritizing risk control, seeking small losses and big wins, and trading only when advantageous.
Key mechanisms include:
- Market filters: Screen favorable conditions via ADX (trend strength), dollar volume (liquidity), funding fee windows, session/weekend restrictions, and spot-long-only settings.
- Signals & entries: Based on price position relative to EMA and EMA trends, combined with breaking Donchian channel extremes (with ATR ratio confirmation), plus single-position rules and post-exit cooldowns.
- Position sizing: Calculate positions by fixed risk percentage; initial stop-loss is ATR-based, complying with exchange min/max lot requirements.
- Exits & risk management: Include initial stop-loss, trailing stop (tightens only), break-even rule (stop moves to entry when target floating profit is hit), time-based exit, and post-exit cooldowns.
- Pyramiding: Add positions only when profitable with favorable momentum, requiring ATR-based spacing; add size is a fraction of the base position, with layers sharing stop logic but having unique order IDs.
Charts display EMA, Donchian channels, current stop lines, and highlight low ADX, avoidable funding windows, and low-liquidity periods.
Recommend starting with 4H or 1D timeframes, with typical parameters varying by cycle. Liquidity settings differ by token; perpetuals should enable funding window filters, while spot requires "long-only" and matching fees. The strategy performs well in trends with quick stop-losses but faces whipsaws in ranges (filters mitigate but don’t eliminate noise). Share your symbol and timeframe for tailored parameters.
Hilly's Advanced Crypto Scalping Strategy - 5 Min ChartTo determine the "best" input parameters for the Advanced Crypto Scalping Strategy on a 5-minute chart, we need to consider the goals of optimizing for profitability, minimizing false signals, and adapting to the volatile nature of cryptocurrencies. The default parameters in the script are a starting point, but the optimal values depend on the specific cryptocurrency pair, market conditions, and your risk tolerance. Below, I'll provide recommended input values based on common practices in crypto scalping, along with reasoning for each parameter. I’ll also suggest how to fine-tune them using TradingView’s backtesting and optimization tools.
Recommended Input Parameters
These values are tailored for a 5-minute chart for liquid cryptocurrencies like BTC/USD or ETH/USD on exchanges like Binance or Coinbase. They aim to balance signal frequency and accuracy for day trading.
Fast EMA Length (emaFastLen): 9
Reasoning: A 9-period EMA is commonly used in scalping to capture short-term price movements while remaining sensitive to recent price action. It reacts faster than the default 10, aligning with the 5-minute timeframe.
Slow EMA Length (emaSlowLen): 21
Reasoning: A 21-period EMA provides a good balance for identifying the broader trend on a 5-minute chart. It’s slightly longer than the default 20 to reduce noise while confirming the trend direction.
RSI Length (rsiLen): 14
Reasoning: The default 14-period RSI is a standard choice for momentum analysis. It works well for detecting overbought/oversold conditions without being too sensitive on short timeframes.
RSI Overbought (rsiOverbought): 75
Reasoning: Raising the overbought threshold to 75 (from 70) reduces false sell signals in strong bullish trends, which are common in crypto markets.
RSI Oversold (rsiOversold): 25
Reasoning: Lowering the oversold threshold to 25 (from 30) filters out weaker buy signals, ensuring entries occur during stronger reversals.
MACD Fast Length (macdFast): 12
Reasoning: The default 12-period fast EMA for MACD is effective for capturing short-term momentum shifts in crypto, aligning with scalping goals.
MACD Slow Length (macdSlow): 26
Reasoning: The default 26-period slow EMA is a standard setting that works well for confirming momentum trends without lagging too much.
MACD Signal Smoothing (macdSignal): 9
Reasoning: The default 9-period signal line is widely used and provides a good balance for smoothing MACD crossovers on a 5-minute chart.
Bollinger Bands Length (bbLen): 20
Reasoning: The default 20-period Bollinger Bands are effective for identifying volatility breakouts, which are key for scalping in crypto markets.
Bollinger Bands Multiplier (bbMult): 2.0
Reasoning: A 2.0 multiplier is standard and captures most price action within the bands. Increasing it to 2.5 could reduce signals but improve accuracy in highly volatile markets.
Stop Loss % (slPerc): 0.8%
Reasoning: A tighter stop loss of 0.8% (from 1.0%) suits the high volatility of crypto, helping to limit losses on false breakouts while keeping risk manageable.
Take Profit % (tpPerc): 1.5%
Reasoning: A 1.5% take-profit target (from 2.0%) aligns with scalping’s goal of capturing small, frequent gains. Crypto markets often see quick reversals, so a smaller target increases the likelihood of hitting profits.
Use Candlestick Patterns (useCandlePatterns): True
Reasoning: Enabling candlestick patterns (e.g., engulfing, hammer) adds confirmation to signals, reducing false entries in choppy markets.
Use Volume Filter (useVolumeFilter): True
Reasoning: The volume filter ensures signals occur during high-volume breakouts, which are more likely to sustain in crypto markets.
Signal Arrow Size (signalSize): 2.0
Reasoning: Increasing the arrow size to 2.0 (from 1.5) makes buy/sell signals more visible on the chart, especially on smaller screens or volatile price action.
Background Highlight Transparency (bgTransparency): 85
Reasoning: A slightly higher transparency (85 from 80) keeps the background highlights subtle but visible, avoiding chart clutter.
How to Apply These Parameters
Copy the Script: Use the Pine Script provided in the previous response.
Paste in TradingView: Open TradingView, go to the Pine Editor, paste the code, and click "Add to Chart."
Set Parameters: In the strategy settings, manually input the recommended values above or adjust them via the input fields.
Test on a 5-Minute Chart: Apply the strategy to a liquid crypto pair (e.g., BTC/USDT, ETH/USDT) on a 5-minute chart.
Fine-Tuning for Optimal Performance
To find the absolute best parameters for your specific trading pair and market conditions, use TradingView’s Strategy Tester and optimization features:
Backtesting:
Run the strategy on historical data for your chosen pair (e.g., BTC/USDT on Binance).
Check metrics like Net Profit, Profit Factor, Win Rate, and Max Drawdown in the Strategy Tester.
Focus on a sample period of at least 1–3 months to capture various market conditions (bull, bear, sideways).
Parameter Optimization:
In the Strategy Tester, click the settings gear next to the strategy name.
Enable optimization for key inputs like emaFastLen (test range: 7–12), emaSlowLen (15–25), slPerc (0.5–1.5), and tpPerc (1.0–3.0).
Run the optimization to find the combination with the highest net profit or best Sharpe ratio, but avoid over-optimization (curve-fitting) by testing on out-of-sample data.
Market-Specific Adjustments:
Volatile Pairs (e.g., DOGE/USDT): Use tighter stop losses (e.g., 0.5–0.7%) and smaller take-profit targets (e.g., 1.0–1.2%) to account for rapid price swings.
Stable Pairs (e.g., BTC/USDT): Slightly wider stop losses (e.g., 1.0–1.2%) and take-profit targets (e.g., 1.8–2.5%) may work better.
Trending Markets: Increase emaSlowLen (e.g., to 25–30) to follow stronger trends.
Choppy Markets: Tighten bbMult (e.g., to 1.8) and lower rsiOverbought/rsiOversold (e.g., to 70/30) to filter noise.
Forward Testing:
After backtesting, run the strategy in real-time on a demo account for 1–2 weeks to validate performance under current market conditions.
Important Notes
No Universal "Best" Numbers: Optimal parameters vary by crypto pair, exchange, and market conditions (e.g., bull vs. bear market). The suggested values are a starting point based on common scalping practices.
Risk Management: Never risk more than 1–2% of your account per trade. Adjust position sizing in TradingView’s strategy settings to align with your risk tolerance.
Market Volatility: Crypto markets are highly volatile. Test the strategy on your specific pair and adjust parameters if signals are too frequent or too rare.
Not Financial Advice: This is an educational tool. Backtest thoroughly and use a demo account before trading with real funds.
If you want me to generate a chart showing backtest performance for specific parameters or to tweak the script further (e.g., adding more indicators or adjusting signal logic), let me know!can you add clear buy ans sell signs above the candles in this codeTo add clear buy and sell signals above the candles in the provided Pine Script, I'll modify the existing code to include prominent text labels directly above (for sell signals) and below (for buy signals) the candles. These labels will be larger, color-coded, and positioned to avoid clutter, ensuring they are easily visible on the chart. The core strategy logic (EMA, RSI, MACD, Bollinger Bands, candlestick patterns, and volume filter) will remain unchanged, and I'll reuse the same artifact_id since this is an update to the previous script.AdvancedCryptoScalpingStrategy.pinex-pine
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management.
Strategic Design and Methodological Basis
The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution.
Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation:
Manual Calculation: Provides a transparent and deterministic computation of HA values.
ticker.heikinashi(): Utilizes TradingView's built-in function, employing confirmed historical bars to prevent repainting artifacts.
Regular Candles: Allows for direct comparison with standard OHLC price action.
This multi-methodological approach to trend smoothing is critical for robust signal generation.
Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (
Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses.
EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.
Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation.
Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation.
Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment.
Applicability
The Mutanabby_AI | ATR+ strategy is particularly suited for:
Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.
Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach.
Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models.
Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments.
Implementation and Evaluation
To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on TradingView. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns.
I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
Opening-Range BreakoutNote: Default trading date range looks mediocre. Set date range to "Entire History" to see full effect of the strategy. 50.91% profitable trades, 1.178 profit factor, steady profits and limited drawdown. Total P&L: $154,141.18, Max Drawdown: $18,624.36. High R^2
█ Overview
The Opening-Range Breakout strategy is a mechanical, session‑based day‑trading system designed to capture the initial burst of directional momentum immediately following the market open. It defines a user‑configurable “opening range” window, measures its high and low boundaries, then places breakout stop orders at those levels once the range closes. Built‑in filters on minimum range width, reward‑to‑risk ratios, and optional reversal logic help refine entries and manage risk dynamically.
█ How It Works
Opening‑Range Formation
Between 9:30–10:15 AM ET (configurable), the script tracks the highest high and lowest low to form the day’s opening range box.
On the first bar after the range window closes, the range high (OR_high) and low (OR_low) are “locked in.”
Range‑Width Filter
To avoid false breakouts in low‑volatility mornings, the range must be at least X% of the current price (default 0.35%).
If the measured opening-range width < minimum threshold, no orders are placed that day.
Entry & Order Placement
Long: a stop‑buy order at the opening‑range high.
Short: a stop‑sell order at the opening‑range low.
Only one side can trigger (or both if reverse logic is enabled after a losing trade).
Risk Management
Once triggered, each trade uses an ATR‑style stop-loss defined as a percentage retracement of the range (default 50% of range width).
Profit target is set at a configurable Reward/Risk Ratio (default 1.1×).
Optional: Reverse on Stop‑Loss – if the initial breakout loses, immediately reverse into the opposite side on the same day.
Session Exit
Any open positions are closed at the end of the regular trading day (default 3:45 PM ET window end, with hard flat at session close).
Visual cues are provided via green (range high) and red (range low) step‑line plots directly on the chart, allowing you to see the range box and breakout triggers in real time.
█ Why It Works
Early Momentum Capture: The first 15 – 60 minutes of trading encapsulate overnight news digestion and institutional order flow, creating a well‑defined volatility “range.”
Mechanical Discipline: Clear, rule‑based entries and exits remove emotional guesswork, ensuring consistency.
Volatility Filtering: By requiring a minimum range width, the system avoids choppy, low‑range days where false breakouts are common.
Dynamic Sizing: Stops and targets scale with the opening range, adapting automatically to each day’s volatility environment.
█ How to Use
Set Your Instruments & Timeframe
-Apply to any futures contract on a 1‑ to 5‑minute chart.
-Ensure chart timezone is set to America/New_York.
Configure Inputs
-Opening‑Range Window: e.g. “0930-1015” for a 45‑minute range.
-Min. OR Width (%): e.g. 0.35 for 0.35% of current price.
-Reward/Risk Ratio: e.g. 1.1 for a modest profit target above your stop.
-Max OR Retracement %: e.g. 50 to set stop at 50% of range width.
-One Trade Per Day: toggle to limit to a single breakout.
-Reverse on Stop Loss: toggle to flip direction after a losing breakout.
Monitor the Chart
-Watch the green and red range boundaries form during the session open.
-Orders will automatically submit on the first bar after the range window closes, conditioned on your filters.
Review & Adjust
-Backtest across multiple months to validate performance on your preferred contract.
-Tweak range duration, minimum width, and R/R multiple to fit your risk tolerance and desired win‑rate vs. expectancy balance.
█ Settings Reference
Input Defaults
Opening‑Range Window - Time window to form OR (HHMM-HHMM) - 0930–1015
Regular Trading Day - Full session for EOD flat (HHMM-HHMM) - 0930–1545
Min. OR Width (%) - Minimum OR size as % of close to trigger orders - 0.35
Reward/Risk Ratio - Profit target multiple of stop‑loss distance - 1.1
Max OR Retracement (%) - % of OR width to use as stop‑loss distance - 50
One Trade Per Day - Limit to a single breakout order per day - false
Reverse on Stop Loss - Reverse direction immediately after a losing trade - true
Disclaimer
This strategy description and any accompanying code are provided for educational purposes only and do not constitute financial advice or a solicitation to trade. Futures trading involves substantial risk, including possible loss of capital. Past performance is not indicative of future results. Traders should assess their own risk tolerance and conduct thorough backtesting and forward-testing before committing real capital.
Strategy Chameleon [theUltimator5]Have you ever looked at an indicator and wondered to yourself "Is this indicator actually profitable?" Well now you can test it out for yourself with the Strategy Chameleon!
Strategy Chameleon is a versatile, signal-agnostic trading strategy designed to adapt to any external indicator or trading system. Like a chameleon changes colors to match its environment, this strategy adapts to match any buy/sell signals you provide, making it the ultimate backtesting and automation tool for traders who want to test multiple strategies without rewriting code.
🎯 Key Features
1) Connects ANY external indicator's buy/sell signals
Works with RSI, MACD, moving averages, custom indicators, or any Pine Script output
Simply connect your indicator's signal output to the strategy inputs
2) Multiple Stop Loss Types:
Percentage-based stops
ATR (Average True Range) dynamic stops
Fixed point stops
3) Advanced Trailing Stop System:
Percentage trailing
ATR-based trailing
Fixed point trailing
4) Flexible Take Profit Options:
Risk:Reward ratio targeting
Percentage-based profits
ATR-based profits
Fixed point profits
5) Trading Direction Control
Long Only - Bull market strategies
Short Only - Bear market strategies
Both - Full market strategies
6) Time-Based Filtering
Optional trading session restrictions
Customize active trading hours
Perfect for day trading strategies
📈 How It Works
Signal Detection: The strategy monitors your connected buy/sell signals
Entry Logic: Executes trades when signals trigger during valid time periods
Risk Management: Automatically applies your chosen stop loss and take profit levels
Trailing System: Dynamically adjusts stops to lock in profits
Performance Tracking: Real-time statistics table showing win rate and performance
⚙️ Setup Instructions
0) Add indicator you want to test, then add the Strategy to your chart
Connect Your Signals:
imgur.com
Go to strategy settings → Signal Sources
1) Set "Buy Signal Source" to your indicator's buy output
2) Set "Sell Signal Source" to your indicator's sell output
3) Choose table position - This simply changes the table location on the screen
4) Set trading direction preference - Buy only? Sell only? Both directions?
imgur.com
5) Set your preferred stop loss type and level
You can set the stop loss to be either percentage based or ATR and fully configurable.
6) Enable trailing stops if desired
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7) Configure take profit settings
8) Toggle time filter to only consider specific time windows or trading sessions.
🚀 Use Cases
Test various indicators to determine feasibility and/or profitability.
Compare different signal sources quickly
Validate trading ideas with consistent risk management
Portfolio Management
Apply uniform risk management across different strategies
Standardize stop loss and take profit rules
Monitor performance consistently
Automation Ready
Built-in alert conditions for automated trading
Compatible with trading bots and webhooks
Easy integration with external systems
⚠️ Important Notes
This strategy requires external signals to function
Default settings use 10% of equity per trade
Pyramiding is disabled (one position at a time)
Strategy calculates on bar close, not every tick
🔗 Integration Examples
Works perfectly with:
RSI strategies (connect RSI > 70 for sells, RSI < 30 for buys)
Moving average crossovers
MACD signal line crosses
Bollinger Band strategies
Custom oscillators and indicators
Multi-timeframe strategies
📋 Default Settings
Position Size: 10% of equity
Stop Loss: 2% percentage-based
Trailing Stop: 1.5% percentage-based (enabled)
Take Profit: Disabled (optional)
Trade Direction: Both long and short
Time Filter: Disabled
Momentum Long + Short Strategy (BTC 3H)Momentum Long + Short Strategy (BTC 3H)
🔍 How It Works, Step by Step
Detect the Trend (📈/📉)
Calculate two moving averages (100-period and 500-period), either EMA or SMA.
For longs, we require MA100 > MA500 (uptrend).
For shorts, we block entries if MA100 exceeds MA500 by more than a set percentage (to avoid fading a powerful uptrend).
Apply Momentum Filters (⚡️)
RSI Filter: Measures recent strength—only allow longs when RSI crosses above its smoothed average, and shorts when RSI dips below the oversold threshold.
ADX Filter: Gauges trend strength—ensures we only enter when a meaningful trend exists (optional).
ATR Filter: Confirms volatility—avoids choppy, low-volatility conditions by requiring ATR to exceed its smoothed value (optional).
Confirm Entry Conditions (✅)
Long Entry:
Price is above both MAs
Trend alignment & optional filters pass ✅
Short Entry:
Price is below both MAs and below the lower Bollinger Band
RSI is sufficiently oversold
Trend-blocker & ATR filter pass ✅
Position Sizing & Risk (💰)
Each trade uses 100 % of account equity by default.
One pyramid addition allowed, so you can scale in if the move continues.
Commission and slippage assumptions built in for realistic backtests.
Stops & Exits (🛑)
Long Stop-Loss: e.g. 3 % below entry.
Long Auto-Exit: If price falls back under the 500-period MA.
Short Stop-Loss: e.g. 3 % above entry.
Short Take-Profit: e.g. 4 % below entry.
🎨 Why It’s Powerful & Customizable
Modular Filters: Turn on/off RSI, ADX, ATR filters to suit different market regimes.
Adjustable Thresholds: Fine-tune stop-loss %, take-profit %, RSI lengths, MA gaps and more.
Multi-Timeframe Potential: Although coded for 3 h BTC, you can adapt it to stocks, forex or other cryptos—just recalibrate!
Backtest Fine-Tuned: Default settings were optimized via backtesting on historical BTC data—but they’re not guarantees of future performance.
⚠️ Warning & Disclaimer
This strategy is for educational purposes only and designed for a toy fund. Crypto markets are highly volatile—you can lose 100 % of your capital. It is not a predictive “holy grail” but a rules-based framework using past data. The parameters have been fine-tuned on historical data and are not valid for future trades without fresh calibration. Always practice with paper-trading first, use proper risk management, and do your own research before risking real money. 🚨🔒
Good luck exploring and experimenting! 🚀📊
Bober XM v2.0# ₿ober XM v2.0 Trading Bot Documentation
**Developer's Note**: While our previous Bot 1.3.1 was removed due to guideline violations, this setback only fueled our determination to create something even better. Rising from this challenge, Bober XM 2.0 emerges not just as an update, but as a complete reimagining with multi-timeframe analysis, enhanced filters, and superior adaptability. This adversity pushed us to innovate further and deliver a strategy that's smarter, more agile, and more powerful than ever before. Challenges create opportunity - welcome to Cryptobeat's finest work yet.
## !!!!You need to tune it for your own pair and timeframe and retune it periodicaly!!!!!
## Overview
The ₿ober XM v2.0 is an advanced dual-channel trading bot with multi-timeframe analysis capabilities. It integrates multiple technical indicators, customizable risk management, and advanced order execution via webhook for automated trading. The bot's distinctive feature is its separate channel systems for long and short positions, allowing for asymmetric trade strategies that adapt to different market conditions across multiple timeframes.
### Key Features
- **Multi-Timeframe Analysis**: Analyze price data across multiple timeframes simultaneously
- **Dual Channel System**: Separate parameter sets for long and short positions
- **Advanced Entry Filters**: RSI, Volatility, Volume, Bollinger Bands, and KEMAD filters
- **Machine Learning Moving Average**: Adaptive prediction-based channels
- **Multiple Entry Strategies**: Breakout, Pullback, and Mean Reversion modes
- **Risk Management**: Customizable stop-loss, take-profit, and trailing stop settings
- **Webhook Integration**: Compatible with external trading bots and platforms
### Strategy Components
| Component | Description |
|---------|-------------|
| **Dual Channel Trading** | Uses either Keltner Channels or Machine Learning Moving Average (MLMA) with separate settings for long and short positions |
| **MLMA Implementation** | Machine learning algorithm that predicts future price movements and creates adaptive bands |
| **Pivot Point SuperTrend** | Trend identification and confirmation system based on pivot points |
| **Three Entry Strategies** | Choose between Breakout, Pullback, or Mean Reversion approaches |
| **Advanced Filter System** | Multiple customizable filters with multi-timeframe support to avoid false signals |
| **Custom Exit Logic** | Exits based on OBV crossover of its moving average combined with pivot trend changes |
### Note for Novice Users
This is a fully featured real trading bot and can be tweaked for any ticker — SOL is just an example. It follows this structure:
1. **Indicator** – gives the initial signal
2. **Entry strategy** – decides when to open a trade
3. **Exit strategy** – defines when to close it
4. **Trend confirmation** – ensures the trade follows the market direction
5. **Filters** – cuts out noise and avoids weak setups
6. **Risk management** – controls losses and protects your capital
To tune it for a different pair, you'll need to start from scratch:
1. Select the timeframe (candle size)
2. Turn off all filters and trend entry/exit confirmations
3. Choose a channel type, channel source and entry strategy
4. Adjust risk parameters
5. Tune long and short settings for the channel
6. Fine-tune the Pivot Point Supertrend and Main Exit condition OBV
This will generate a lot of signals and activity on the chart. Your next task is to find the right combination of filters and settings to reduce noise and tune it for profitability.
### Default Strategy values
Default values are tuned for: Symbol BITGET:SOLUSDT.P 5min candle
Filters are off by default: Try to play with it to understand how it works
## Configuration Guide
### General Settings
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Long Positions** | Enable or disable long trades | Enabled |
| **Short Positions** | Enable or disable short trades | Enabled |
| **Risk/Reward Area** | Visual display of stop-loss and take-profit zones | Enabled |
| **Long Entry Source** | Price data used for long entry signals | hl2 (High+Low/2) |
| **Short Entry Source** | Price data used for short entry signals | hl2 (High+Low/2) |
The bot allows you to trade long positions, short positions, or both simultaneously. Each direction has its own set of parameters, allowing for fine-tuned strategies that recognize the asymmetric nature of market movements.
### Multi-Timeframe Settings
1. **Enable Multi-Timeframe Analysis**: Toggle 'Enable Multi-Timeframe Analysis' in the Multi-Timeframe Settings section
2. **Configure Timeframes**: Set appropriate higher timeframes based on your trading style:
- Timeframe 1: Default is now 15 minutes (intraday confirmation)
- Timeframe 2: Default is 4 hours (trend direction)
3. **Select Sources per Indicator**: For each indicator (RSI, KEMAD, Volume, etc.), choose:
- The desired timeframe (current, mtf1, or mtf2)
- The appropriate price type (open, high, low, close, hl2, hlc3, ohlc4)
### Entry Strategies
- **Breakout**: Enter when price breaks above/below the channel
- **Pullback**: Enter when price pulls back to the channel
- **Mean Reversion**: Enter when price is extended from the channel
You can enable different strategies for long and short positions.
### Core Components
### Risk Management
- **Position Size**: Control risk with percentage-based position sizing
- **Stop Loss Options**:
- Fixed: Set a specific price or percentage from entry
- ATR-based: Dynamic stop-loss based on market volatility
- Swing: Uses recent swing high/low points
- **Take Profit**: Multiple targets with percentage allocation
- **Trailing Stop**: Dynamic stop that follows price movement
## Advanced Usage Strategies
### Moving Average Type Selection Guide
- **SMA**: More stable in choppy markets, good for higher timeframes
- **EMA/WMA**: More responsive to recent price changes, better for entry signals
- **VWMA**: Adds volume weighting for stronger trends, use with Volume filter
- **HMA**: Balance between responsiveness and noise reduction, good for volatile markets
### Multi-Timeframe Strategy Approaches
- **Trend Confirmation**: Use higher timeframe RSI (mtf2) for overall trend, current timeframe for entries
- **Entry Precision**: Use KEMAD on current timeframe with volume filter on mtf1
- **False Signal Reduction**: Apply RSI filter on mtf1 with strict KEMAD settings
### Market Condition Optimization
| Market Condition | Recommended Settings |
|------------------|----------------------|
| **Trending** | Use Breakout strategy with KEMAD filter on higher timeframe |
| **Ranging** | Use Mean Reversion with strict RSI filter (mtf1) |
| **Volatile** | Increase ATR multipliers, use HMA for moving averages |
| **Low Volatility** | Decrease noise parameters, use pullback strategy |
## Webhook Integration
The strategy features a professional webhook system that allows direct connectivity to your exchange or trading platform of choice through third-party services like 3commas, Alertatron, or Autoview.
The webhook payload includes all necessary parameters for automated execution:
- Entry price and direction
- Stop loss and take profit levels
- Position size
- Custom identifier for webhook routing
## Performance Optimization Tips
1. **Start with Defaults**: Begin with the default settings for your timeframe before customizing
2. **Adjust One Component at a Time**: Make incremental changes and test the impact
3. **Match MA Types to Market Conditions**: Use appropriate moving average types based on the Market Condition Optimization table
4. **Timeframe Synergy**: Create logical relationships between timeframes (e.g., 5min chart with 15min and 4h higher timeframes)
5. **Periodic Retuning**: Markets evolve - regularly review and adjust parameters
## Common Setups
### Crypto Trend-Following
- MLMA with EMA or HMA
- Higher RSI thresholds (75/25)
- KEMAD filter on mtf1
- Breakout entry strategy
### Stock Swing Trading
- MLMA with SMA for stability
- Volume filter with higher threshold
- KEMAD with increased filter order
- Pullback entry strategy
### Forex Scalping
- MLMA with WMA and lower noise parameter
- RSI filter on current timeframe
- Use highest timeframe for trend direction only
- Mean Reversion strategy
## Webhook Configuration
- **Benefits**:
- Automated trade execution without manual intervention
- Immediate response to market conditions
- Consistent execution of your strategy
- **Implementation Notes**:
- Requires proper webhook configuration on your exchange or platform
- Test thoroughly with small position sizes before full deployment
- Consider latency between signal generation and execution
### Backtesting Period
Define a specific historical period to evaluate the bot's performance:
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Start Date** | Beginning of backtest period | January 1, 2025 |
| **End Date** | End of backtest period | December 31, 2026 |
- **Best Practice**: Test across different market conditions (bull markets, bear markets, sideways markets)
- **Limitation**: Past performance doesn't guarantee future results
## Entry and Exit Strategies
### Dual-Channel System
A key innovation of the Bober XM is its dual-channel approach:
- **Independent Parameters**: Each trade direction has its own channel settings
- **Asymmetric Trading**: Recognizes that markets often behave differently in uptrends versus downtrends
- **Optimized Performance**: Fine-tune settings for both bullish and bearish conditions
This approach allows the bot to adapt to the natural asymmetry of markets, where uptrends often develop gradually while downtrends can be sharp and sudden.
### Channel Types
#### 1. Keltner Channels
Traditional volatility-based channels using EMA and ATR:
| Setting | Long Default | Short Default |
|---------|--------------|---------------|
| **EMA Length** | 37 | 20 |
| **ATR Length** | 13 | 17 |
| **Multiplier** | 1.4 | 1.9 |
| **Source** | low | high |
- **Strengths**:
- Reliable in trending markets
- Less prone to whipsaws than Bollinger Bands
- Clear visual representation of volatility
- **Weaknesses**:
- Can lag during rapid market changes
- Less effective in choppy, non-trending markets
#### 2. Machine Learning Moving Average (MLMA)
Advanced predictive model using kernel regression (RBF kernel):
| Setting | Description | Options |
|---------|-------------|--------|
| **Source MA** | Price data used for MA calculations | Any price source (low/high/close/etc.) |
| **Moving Average Type** | Type of MA algorithm for calculations | SMA, EMA, WMA, VWMA, RMA, HMA |
| **Trend Source** | Price data used for trend determination | Any price source (close default) |
| **Window Size** | Historical window for MLMA calculations | 5+ (default: 16) |
| **Forecast Length** | Number of bars to forecast ahead | 1+ (default: 3) |
| **Noise Parameter** | Controls smoothness of prediction | 0.01+ (default: ~0.43) |
| **Band Multiplier** | Multiplier for channel width | 0.1+ (default: 0.5-0.6) |
- **Strengths**:
- Predictive rather than reactive
- Adapts quickly to changing market conditions
- Better at identifying trend reversals early
- **Weaknesses**:
- More computationally intensive
- Requires careful parameter tuning
- Can be sensitive to input data quality
### Entry Strategies
| Strategy | Description | Ideal Market Conditions |
|----------|-------------|-------------------------|
| **Breakout** | Enters when price breaks through channel bands, indicating strong momentum | High volatility, emerging trends |
| **Pullback** | Enters when price retraces to the middle band after testing extremes | Established trends with regular pullbacks |
| **Mean Reversion** | Enters at channel extremes, betting on a return to the mean | Range-bound or oscillating markets |
#### Breakout Strategy (Default)
- **Implementation**: Enters long when price crosses above the upper band, short when price crosses below the lower band
- **Strengths**: Captures strong momentum moves, performs well in trending markets
- **Weaknesses**: Can lead to late entries, higher risk of false breakouts
- **Optimization Tips**:
- Increase channel multiplier for fewer but more reliable signals
- Combine with volume confirmation for better accuracy
#### Pullback Strategy
- **Implementation**: Enters long when price pulls back to middle band during uptrend, short during downtrend pullbacks
- **Strengths**: Better entry prices, lower risk, higher probability setups
- **Weaknesses**: Misses some strong moves, requires clear trend identification
- **Optimization Tips**:
- Use with trend filters to confirm overall direction
- Adjust middle band calculation for market volatility
#### Mean Reversion Strategy
- **Implementation**: Enters long at lower band, short at upper band, expecting price to revert to the mean
- **Strengths**: Excellent entry prices, works well in ranging markets
- **Weaknesses**: Dangerous in strong trends, can lead to fighting the trend
- **Optimization Tips**:
- Implement strong trend filters to avoid counter-trend trades
- Use smaller position sizes due to higher risk nature
### Confirmation Indicators
#### Pivot Point SuperTrend
Combines pivot points with ATR-based SuperTrend for trend confirmation:
| Setting | Default Value |
|---------|---------------|
| **Pivot Period** | 25 |
| **ATR Factor** | 2.2 |
| **ATR Period** | 41 |
- **Function**: Identifies significant market turning points and confirms trend direction
- **Implementation**: Requires price to respect the SuperTrend line for trade confirmation
#### Weighted Moving Average (WMA)
Provides additional confirmation layer for entries:
| Setting | Default Value |
|---------|---------------|
| **Period** | 15 |
| **Source** | ohlc4 (average of Open, High, Low, Close) |
- **Function**: Confirms trend direction and filters out low-quality signals
- **Implementation**: Price must be above WMA for longs, below for shorts
### Exit Strategies
#### On-Balance Volume (OBV) Based Exits
Uses volume flow to identify potential reversals:
| Setting | Default Value |
|---------|---------------|
| **Source** | ohlc4 |
| **MA Type** | HMA (Options: SMA, EMA, WMA, RMA, VWMA, HMA) |
| **Period** | 22 |
- **Function**: Identifies divergences between price and volume to exit before reversals
- **Implementation**: Exits when OBV crosses its moving average in the opposite direction
- **Customizable MA Type**: Different MA types provide varying sensitivity to OBV changes:
- **SMA**: Traditional simple average, equal weight to all periods
- **EMA**: More weight to recent data, responds faster to price changes
- **WMA**: Weighted by recency, smoother than EMA
- **RMA**: Similar to EMA but smoother, reduces noise
- **VWMA**: Factors in volume, helpful for OBV confirmation
- **HMA**: Reduces lag while maintaining smoothness (default)
#### ADX Exit Confirmation
Uses Average Directional Index to confirm trend exhaustion:
| Setting | Default Value |
|---------|---------------|
| **ADX Threshold** | 35 |
| **ADX Smoothing** | 60 |
| **DI Length** | 60 |
- **Function**: Confirms trend weakness before exiting positions
- **Implementation**: Requires ADX to drop below threshold or DI lines to cross
## Filter System
### RSI Filter
- **Function**: Controls entries based on momentum conditions
- **Parameters**:
- Period: 15 (default)
- Overbought level: 71
- Oversold level: 23
- Multi-timeframe support: Current, MTF1 (15min), or MTF2 (4h)
- Customizable price source (open, high, low, close, hl2, hlc3, ohlc4)
- **Implementation**: Blocks long entries when RSI > overbought, short entries when RSI < oversold
### Volatility Filter
- **Function**: Prevents trading during excessive market volatility
- **Parameters**:
- Measure: ATR (Average True Range)
- Period: Customizable (default varies by timeframe)
- Threshold: Adjustable multiplier
- Multi-timeframe support
- Customizable price source
- **Implementation**: Blocks trades when current volatility exceeds threshold × average volatility
### Volume Filter
- **Function**: Ensures adequate market liquidity for trades
- **Parameters**:
- Threshold: 0.4× average (default)
- Measurement period: 5 (default)
- Moving average type: Customizable (HMA default)
- Multi-timeframe support
- Customizable price source
- **Implementation**: Requires current volume to exceed threshold × average volume
### Bollinger Bands Filter
- **Function**: Controls entries based on price relative to statistical boundaries
- **Parameters**:
- Period: Customizable
- Standard deviation multiplier: Adjustable
- Moving average type: Customizable
- Multi-timeframe support
- Customizable price source
- **Implementation**: Can require price to be within bands or breaking out of bands depending on strategy
### KEMAD Filter (Kalman EMA Distance)
- **Function**: Advanced trend confirmation using Kalman filter algorithm
- **Parameters**:
- Process Noise: 0.35 (controls smoothness)
- Measurement Noise: 24 (controls reactivity)
- Filter Order: 6 (higher = more smoothing)
- ATR Length: 8 (for bandwidth calculation)
- Upper Multiplier: 2.0 (for long signals)
- Lower Multiplier: 2.7 (for short signals)
- Multi-timeframe support
- Customizable visual indicators
- **Implementation**: Generates signals based on price position relative to Kalman-filtered EMA bands
## Risk Management System
### Position Sizing
Automatically calculates position size based on account equity and risk parameters:
| Setting | Default Value |
|---------|---------------|
| **Risk % of Equity** | 50% |
- **Implementation**:
- Position size = (Account equity × Risk %) ÷ (Entry price × Stop loss distance)
- Adjusts automatically based on volatility and stop placement
- **Best Practices**:
- Start with lower risk percentages (1-2%) until strategy is proven
- Consider reducing risk during high volatility periods
### Stop-Loss Methods
Multiple stop-loss calculation methods with separate configurations for long and short positions:
| Method | Description | Configuration |
|--------|-------------|---------------|
| **ATR-Based** | Dynamic stops based on volatility | ATR Period: 14, Multiplier: 2.0 |
| **Percentage** | Fixed percentage from entry | Long: 1.5%, Short: 1.5% |
| **PIP-Based** | Fixed currency unit distance | 10.0 pips |
- **Implementation Notes**:
- ATR-based stops adapt to changing market volatility
- Percentage stops maintain consistent risk exposure
- PIP-based stops provide precise control in stable markets
### Trailing Stops
Locks in profits by adjusting stop-loss levels as price moves favorably:
| Setting | Default Value |
|---------|---------------|
| **Stop-Loss %** | 1.5% |
| **Activation Threshold** | 2.1% |
| **Trailing Distance** | 1.4% |
- **Implementation**:
- Initial stop remains fixed until profit reaches activation threshold
- Once activated, stop follows price at specified distance
- Locks in profit while allowing room for normal price fluctuations
### Risk-Reward Parameters
Defines the relationship between risk and potential reward:
| Setting | Default Value |
|---------|---------------|
| **Risk-Reward Ratio** | 1.4 |
| **Take Profit %** | 2.4% |
| **Stop-Loss %** | 1.5% |
- **Implementation**:
- Take profit distance = Stop loss distance × Risk-reward ratio
- Higher ratios require fewer winning trades for profitability
- Lower ratios increase win rate but reduce average profit
### Filter Combinations
The strategy allows for simultaneous application of multiple filters:
- **Recommended Combinations**:
- Trending markets: RSI + KEMAD filters
- Ranging markets: Bollinger Bands + Volatility filters
- All markets: Volume filter as minimum requirement
- **Performance Impact**:
- Each additional filter reduces the number of trades
- Quality of remaining trades typically improves
- Optimal combination depends on market conditions and timeframe
### Multi-Timeframe Filter Applications
| Filter Type | Current Timeframe | MTF1 (15min) | MTF2 (4h) |
|-------------|-------------------|-------------|------------|
| RSI | Quick entries/exits | Intraday trend | Overall trend |
| Volume | Immediate liquidity | Sustained support | Market participation |
| Volatility | Entry timing | Short-term risk | Regime changes |
| KEMAD | Precise signals | Trend confirmation | Major reversals |
## Visual Indicators and Chart Analysis
The bot provides comprehensive visual feedback on the chart:
- **Channel Bands**: Keltner or MLMA bands showing potential support/resistance
- **Pivot SuperTrend**: Colored line showing trend direction and potential reversal points
- **Entry/Exit Markers**: Annotations showing actual trade entries and exits
- **Risk/Reward Zones**: Visual representation of stop-loss and take-profit levels
These visual elements allow for:
- Real-time strategy assessment
- Post-trade analysis and optimization
- Educational understanding of the strategy logic
## Implementation Guide
### TradingView Setup
1. Load the script in TradingView Pine Editor
2. Apply to your preferred chart and timeframe
3. Adjust parameters based on your trading preferences
4. Enable alerts for webhook integration
### Webhook Integration
1. Configure webhook URL in TradingView alerts
2. Set up receiving endpoint on your trading platform
3. Define message format matching the bot's output
4. Test with small position sizes before full deployment
### Optimization Process
1. Backtest across different market conditions
2. Identify parameter sensitivity through multiple tests
3. Focus on risk management parameters first
4. Fine-tune entry/exit conditions based on performance metrics
5. Validate with out-of-sample testing
## Performance Considerations
### Strengths
- Adaptability to different market conditions through dual channels
- Multiple layers of confirmation reducing false signals
- Comprehensive risk management protecting capital
- Machine learning integration for predictive edge
### Limitations
- Complex parameter set requiring careful optimization
- Potential over-optimization risk with so many variables
- Computational intensity of MLMA calculations
- Dependency on proper webhook configuration for execution
### Best Practices
- Start with conservative risk settings (1-2% of equity)
- Test thoroughly in demo environment before live trading
- Monitor performance regularly and adjust parameters
- Consider market regime changes when evaluating results
## Conclusion
The ₿ober XM v2.0 represents a significant evolution in trading strategy design, combining traditional technical analysis with machine learning elements and multi-timeframe analysis. The core strength of this system lies in its adaptability and recognition of market asymmetry.
### Market Asymmetry and Adaptive Approach
The strategy acknowledges a fundamental truth about markets: bullish and bearish phases behave differently and should be treated as distinct environments. The dual-channel system with separate parameters for long and short positions directly addresses this asymmetry, allowing for optimized performance regardless of market direction.
### Targeted Backtesting Philosophy
It's counterproductive to run backtests over excessively long periods. Markets evolve continuously, and strategies that worked in previous market regimes may be ineffective in current conditions. Instead:
- Test specific market phases separately (bull markets, bear markets, range-bound periods)
- Regularly re-optimize parameters as market conditions change
- Focus on recent performance with higher weight than historical results
- Test across multiple timeframes to ensure robustness
### Multi-Timeframe Analysis as a Game-Changer
The integration of multi-timeframe analysis fundamentally transforms the strategy's effectiveness:
- **Increased Safety**: Higher timeframe confirmations reduce false signals and improve trade quality
- **Context Awareness**: Decisions made with awareness of larger trends reduce adverse entries
- **Adaptable Precision**: Apply strict filters on lower timeframes while maintaining awareness of broader conditions
- **Reduced Noise**: Higher timeframe data naturally filters market noise that can trigger poor entries
The ₿ober XM v2.0 provides traders with a framework that acknowledges market complexity while offering practical tools to navigate it. With proper setup, realistic expectations, and attention to changing market conditions, it delivers a sophisticated approach to systematic trading that can be continuously refined and optimized.
Dskyz (DAFE) MAtrix with ATR-Powered Precision Dskyz (DAFE) MAtrix with ATR-Powered Precision
This cutting‐edge futures trading strategy built to thrive in rapidly changing market conditions. Developed for high-frequency futures trading on instruments such as the CME Mini MNQ, this strategy leverages a matrix of sophisticated moving averages combined with ATR-based filters to pinpoint high-probability entries and exits. Its unique combination of adaptable technical indicators and multi-timeframe trend filtering sets it apart from standard strategies, providing enhanced precision and dynamic responsiveness.
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Core Functional Components
1. Advanced Moving Averages
A distinguishing feature of the DAFE strategy is its robust, multi-choice moving averages (MAs). Clients can choose from a wide array of MAs—each with specific strengths—in order to fine-tune their trading signals. The code includes user-defined functions for the following MAs:
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Hull Moving Average (HMA):
The hma(src, len) function calculates the HMA by using weighted moving averages (WMAs) to reduce lag considerably while smoothing price data. This function computes an intermediate WMA of half the specified length, then a full-length WMA, and finally applies a further WMA over the square root of the length. This design allows for rapid adaptation to price changes without the typical delays of traditional moving averages.
Triple Exponential Moving Average (TEMA):
Implemented via tema(src, len), TEMA uses three consecutive exponential moving averages (EMAs) to effectively cancel out lag and capture price momentum. The final formula—3 * (ema1 - ema2) + ema3—produces a highly responsive indicator that filters out short-term noise.
Double Exponential Moving Average (DEMA):
Through the dema(src, len) function, DEMA calculates an EMA and then a second EMA on top of it. Its simplified formula of 2 * ema1 - ema2 provides a smoother curve than a single EMA while maintaining enhanced responsiveness.
Volume Weighted Moving Average (VWMA):
With vwma(src, len), this MA accounts for trading volume by weighting the price, thereby offering a more contextual picture of market activity. This is crucial when volume spikes indicate significant moves.
Zero Lag EMA (ZLEMA):
The zlema(src, len) function applies a correction to reduce the inherent lag found in EMAs. By subtracting a calculated lag (based on half the moving average window), ZLEMA is exceptionally attuned to recent price movements.
Arnaud Legoux Moving Average (ALMA):
The alma(src, len, offset, sigma) function introduces ALMA—a type of moving average designed to be less affected by outliers. With parameters for offset and sigma, it allows customization of the degree to which the MA reacts to market noise.
Kaufman Adaptive Moving Average (KAMA):
The custom kama(src, len) function is noteworthy for its adaptive nature. It computes an efficiency ratio by comparing price change against volatility, then dynamically adjusts its smoothing constant. This results in an MA that quickly responds during trending periods while remaining smoothed during consolidation.
Each of these functions—integrated into the strategy—is selectable by the trader (via the fastMAType and slowMAType inputs). This flexibility permits the tailored application of the MA most suited to current market dynamics and individual risk management preferences.
2. ATR-Based Filters and Risk Controls
ATR Calculation and Volatility Filter:
The strategy computes the Average True Range (ATR) over a user-defined period (atrPeriod). ATR is then used to derive both:
Volatility Assessment: Expressed as a ratio of ATR to closing price, ensuring that trades are taken only when volatility remains within a safe, predefined threshold (volatilityThreshold).
ATR-Based Entry Filters: Implemented as atrFilterLong and atrFilterShort, these conditions ensure that for long entries the price is sufficiently above the slow MA and vice versa for shorts. This acts as an additional confirmation filter.
Dynamic Exit Management:
The exit logic employs a dual approach:
Fixed Stop and Profit Target: Stops and targets are set at multiples of ATR (fixedStopMultiplier and profitTargetATRMult), helping manage risk in volatile markets.
Trailing Stop Adjustments: A trailing stop is calculated using the ATR multiplied by a user-defined offset (trailOffset), which captures additional profits as the trade moves favorably while protecting against reversals.
3. Multi-Timeframe Trend Filtering
The strategy enhances its signal reliability by leveraging a secondary, higher timeframe analysis:
15-Minute Trend Analysis:
By retrieving 15-minute moving averages (fastMA15m and slowMA15m) via request.security, the strategy determines the broader market trend. This secondary filter (enabled or disabled through useTrendFilter) ensures that entries are aligned with the prevailing market direction, thereby reducing the incidence of false signals.
4. Signal and Execution Logic
Combined MA Alignment:
The entry conditions are based primarily on the alignment of the fast and slow MAs. A long condition is triggered when the current price is above both MAs and the fast MA is above the slow MA—complemented by the ATR filter and volume conditions. The reverse applies for a short condition.
Volume and Time Window Validation:
Trades are permitted only if the current volume exceeds a minimum (minVolume) and the current hour falls within the predefined trading window (tradingStartHour to tradingEndHour). An additional volume spike check (comparing current volume to a moving average of past volumes) further filters for optimal market conditions.
Comprehensive Order Execution:
The strategy utilizes flexible order execution functions that allow pyramiding (up to 10 positions), ensuring that it can scale into positions as favorable conditions persist. The use of both market entries and automated exits (with profit targets, stop-losses, and trailing stops) ensures that risk is managed at every step.
5. Integrated Dashboard and Metrics
For transparency and real-time analysis, the strategy includes:
On-Chart Visualizations:
Both fast and slow MAs are plotted on the chart, making it easy to see the market’s technical foundation.
Dynamic Metrics Dashboard:
A built-in table displays crucial performance statistics—including current profit/loss, equity, ATR (both raw and as a percentage), and the percentage gap between the moving averages. These metrics offer immediate insight into the health and performance of the strategy.
Input Parameters: Detailed Breakdown
Every input is meticulously designed to offer granular control:
Fast & Slow Lengths:
Determine the window size for the fast and slow moving averages. Smaller values yield more sensitivity, while larger values provide a smoother, delayed response.
Fast/Slow MA Types:
Choose the type of moving average for fast and slow signals. The versatility—from basic SMA and EMA to more complex ones like HMA, TEMA, ZLEMA, ALMA, and KAMA—allows customization to fit different market scenarios.
ATR Parameters:
atrPeriod and atrMultiplier shape the volatility assessment, directly affecting entry filters and risk management through stop-loss and profit target levels.
Trend and Volume Filters:
Inputs such as useTrendFilter, minVolume, and the volume spike condition help confirm that a trade occurs in active, trending markets rather than during periods of low liquidity or market noise.
Trading Hours:
Restricting trade execution to specific hours (tradingStartHour and tradingEndHour) helps avoid illiquid or choppy markets outside of prime trading sessions.
Exit Strategies:
Parameters like trailOffset, profitTargetATRMult, and fixedStopMultiplier provide multiple layers of risk management and profit protection by tailoring how exits are generated relative to current market conditions.
Pyramiding and Fixed Trade Quantity:
The strategy supports multiple entries within a trend (up to 10 positions) and sets a predefined trade quantity (fixedQuantity) to maintain consistent exposure and risk per trade.
Dashboard Controls:
The resetDashboard input allows for on-the-fly resetting of performance metrics, keeping the strategy’s performance dashboard accurate and up-to-date.
Why This Strategy is Truly Exceptional
Multi-Faceted Adaptability:
The ability to switch seamlessly between various moving average types—each suited to particular market conditions—enables the strategy to adapt dynamically. This is a testament to the high level of coding sophistication and market insight infused within the system.
Robust Risk Management:
The integration of ATR-based stops, profit targets, and trailing stops ensures that every trade is executed with well-defined risk parameters. The system is designed to mitigate unexpected market swings while optimizing profit capture.
Comprehensive Market Filtering:
By combining moving average crossovers with volume analysis, volatility thresholds, and multi-timeframe trend filters, the strategy only enters trades under the most favorable conditions. This multi-layered filtering reduces noise and enhances signal quality.
-Final Thoughts-
The Dskyz Adaptive Futures Elite (DAFE) MAtrix with ATR-Powered Precision strategy is not just another trading algorithm—it is a multi-dimensional, fully customizable system built on advanced technical principles and sophisticated risk management techniques. Every function and input parameter has been carefully engineered to provide traders with a system that is both powerful and transparent.
For clients seeking a state-of-the-art trading solution that adapts dynamically to market conditions while maintaining strict discipline in risk management, this strategy truly stands in a class of its own.
****Please show support if you enjoyed this strategy. I'll have more coming out in the near future!!
-Dskyz
Caution
DAFE is experimental, not a profit guarantee. Futures trading risks significant losses due to leverage. Backtest, simulate, and monitor actively before live use. All trading decisions are your responsibility.
