Dynamic Stop Loss DemoWhat does this script do ?
This script is for pine script programmers and explains how to implement a dynamic stop-loss strategy. It is different from trailing stop-loss. Trailing stop-loss can only set the retracement value, but this script can take profit on part of the position at a fixed price and allows users to decide whether to take profit on all positions based on whether a certain track is breached or other conditions author want. In this demo, it use rsi crossover and crossunder to decide the strategy condition, and use close price as open price, and use lowest low / highest high as stop price, and use 1.5 risk ratio to calculate the fixed first profit price. It will take 50% position size when the first profit price was reached. Then it will close all rest positions when the inverse condition come out or the dynamic stop(calculated by ATR) breached or when the price back to the open price or the stop price.
How is this script implemented
When start strategy by strategy.entry , it gives a custom id which contains direction, openPrice, stopPrice, profitPrice, qty, etc. It can be get from the global variable strategy.posiition_entry_name .
Pesquisar nos scripts por "track"
TENKAN SCALPER STRATEGYTENKAN SCALP is a fully automatic trading system.
It is a continuation of our previous ichimoku release. This time however we throw out the rule book and use ICHIMOKU in a very different way.
It applies non traditional money management tactics.
While most trading strategies rely on a stop loss and a take profit target to manage risk. This strategy uses either no stop loss at all or a time based stop loss.
You might ask yourself the question why would you keep a trade open if it goes against you? Here are a phew reasons why the script does what it does.
Forex Markets consolidate most of the time. If you wait long enough your Take Profit will get hit anyways most of the time
You don't have to risk everything per trade. I keep my orders small so to keep some powder to get into some more trades
All the extra trades you take while one trade is in drawdown limit the drawdown as they provide cashflow
On lower timeframes the markets are so chaotic that a stop loss is very likely to get hit by a wick
About backtest below
This backtest uses a spread of 2 pips for entries and a default position size of 100% of equity. This is only possible on exchanges where spread is low and you have 10:1 leverage or more. It does not represent results obtainable without leverage. Do take into account that there are a lot of forex exchanges that provide this leverage, however a 2 pip spread is not always guaranteed and only applies to major pairs.
This backtest does not use the TIME BASED STOPS functionality.
Always start with small position sizing and see how the strategy performs before adding risk.
Explanation of variables:
Chikou(lagging span): pink line, this is price plotted 26 bars ago. People ignore the power of this it is crucial to see how chikou behaves towards past price action as seen in the chart below where we got an entry at red arrow because chikou bounced from past fractal bottom.
Kijun-Sen(base line): Black line or color coded line. This is the equilibrium of last 26 candles. To me this is the most important line in the system as it attracts price.
Kijun = (Highest high of 26 periods + Lowest low of 26 periods) ÷ 2
Tenkan-Sen(conversion line): Blue line. This is the equilibrium of last 9 candles. In a strong uptrend price stays above this line.
Tenkan = (Highest high of 9 periods + Lowest low of 9 periods) ÷ 2
Senkou A (Leading span A)= Pink cloud line, this is the average of the 2 components projected 26 bars in the future.
Senkou A = (Tenkan + Kijun) ÷ 2
Senkou B (Leading span B) = Green cloud line, this is the 52 day equilibrium projected 26 bars in the future.
Senkou B = (Highest high of prior 52 periods + Lowest low of prior 52 periods) ÷ 2
projection: Script uses same function for variable calculation and substracts a number on each next bar as to make a projection of where the variable will be in future bars if price stayed the same. This works as ICHIMOKU calculations use the middle point of a past set of data. The shorter that amount of bars will be in line with the data that it will be restricted to in future if price stayed the same.
Detection of Market Environment
To enter trades the script uses a lot of ICHIMOKU concepts. Contrary to how most people trade ICHIMOKU this script takes an environment that ICHIMOKU identifies as trending upwards and shorts in that environment. The same will be applied to a downtrend where it will open LONGS.
List of CRITERIA for a trend:
Grapling Hook: this is a component based on the chikou span (closing price displaced 26 bars into the past). The script will use an ATR based range to define a possible future projection to the CHIKOU line. For a market to be bullish there should be no price action happening within this area. Market is free to move upwards. Vice versa for bearish .
Kumo Cloud: script will check if price is above the cloud for bullish trend and below cloud for bearish trend .
Chikou above Kijun: script will check if the chikou line is above the KIJUN line of 26 bars ago. This is further confirmation that price is trending high enough compared to it's past data. Vice versa for downtrend.
Kijun projection: script will check if past Kijun is lower than future projected Kijun. This to ensure we get an equilibrium in our favour in the future. Vice versa for downtrend
Tenkan projection: script will check if future Tenkan-sen will be higher than Kijun-sen for an uptrend. Vice versa for downtrend.
Cloud projection: script will check if in 9 bars the Senkou Span A will be higher than Senkou Span B for an uptrend. Vice versa for downtrend.
Example:
This script does not visualise the prediction lines like I show in the example. I show them here to clarify how the script works.
Usage
Backtests are not indicative of future results, although a trader may want to use a strategy script to have a deeper understanding of how their strategy responds to varying market conditions, or as a tool for identifying possible flaws for a strategy that may be indicative of good or bad performance in the future.
Strategy Settings:
Minimum Body Size (atr): this is the minimum ATR a signal bar needs to be for entry. This is useful because our TP is based on previous bar.
Lot size per trade: this setting does not impact backtest. It is used to for the signals to let tradingconnect.com know your position size.
Direction: do you want to trade longs or shorts. I personally use both a long bot and a short bot at the same time.
Positions Allowed: the amount of positions the script will keep open as a maximum. You do not want to open too many positions, this is for risk management.
Close all positions at drawdown: if total open positions loss gets to this % target it will close all positions.
MetaTrader Prefix: when the script sends a signal it will put this text right before the symbol name from syminfo.ticker
MetaTrader Suffix: when the script sends a signal it will put this text right after the symbol name from syminfo.ticker
Charts below are some examples on how the script handles orders on default settings:
without time based SL
with time based SL
how it handles pyramiding
www.tradingview.com
Tradingconnector.com:
For full automation of the forex market the script uses this connector to execute trade on MT4. The alerts the script sends using the alerts() function call are structured in a way tradingconnector will recognise and send directly to MT4. You can find documentation about this tool on their own website.
Personal recommendation is to start with a minimum lot size and track performance, if you are comfortable scale the size up. You can do that by increasing the lot size setting in the script and making a new alert. Make sure to delete the old one.
How to access
You can see the Author's Instructions below to visit our telegram to get more information on how to get access.
I11L - Better Buy Low Volatility or High Volatility?This Pine Script code defines a TradingView strategy called "I11L - Better Buy Low Volatility or High Volatility?". The strategy aims to study the difference between buying when an asset's volatility is low and when it is high. It allows the user to select whether to buy during low or high volatility periods by changing the input variable mode.
Here's a brief explanation of the System:
The strategy is initialized with relevant settings such as overlay, pyramiding, default quantity type, initial capital, and others.
The mode input allows the user to choose between "Buy low Volatility" and "Buy high Volatility" options.
volatilityTargetRatio is the user-defined threshold to be used for making buy decisions. A value of 1 equals the average ATR (Average True Range) for the security. A lower value indicates lower volatility.
atrLength is the number of periods to calculate the ATR.
sellAfterNBarsLength sets the number of bars to hold the position before selling it.
The script calculates the ATR using the ta.atr() function, and then divides it by the closing price to normalize the value. It also calculates the simple moving average (SMA) of the normalized ATR over a period of 5 times the ATR length, and then computes the ratio between the normalized ATR and its average.
The script keeps track of the number of holding bars using the variable holdingBarsCounter. When there are open trades, the holding bars counter is incremented.
The decision to buy is made based on the selected mode and whether the computed ratio is above or below the user-defined threshold.
When the holding bars counter exceeds the user-defined limit, the position is closed.
The script plots the computed ratio with different colors based on the buy and close conditions. The ratio is plotted in green when a buy signal is triggered, red when a close signal is triggered, and white in all other cases. The value of 1 (the reference for the average ATR) is also plotted on the chart in white color.
This strategy helps traders study the difference between buying during low and high volatility periods and compare the performance of these conditions. It can be useful for analyzing the effectiveness of volatility-based trading strategies, such as entering positions when the market is calm or during periods of strong price movement.
I11L - Risk Adjusted LeveragingThis trading system, called "I11L - Risk Adjusted Leveraging", is designed to manage trades based on the current market volatility relative to its historical average. The system calculates the target number of open trades based on the ATR (Average True Range) indicator and adjusts the leverage accordingly. The system opens and closes trades using a pyramiding approach, allowing multiple positions to be opened at the same time.
Here's a step-by-step explanation of the system:
1. Calculate the ATR with a 14-day period and normalize it by dividing it by the current closing price.
2. Calculate the 100-day simple moving average (SMA) of the normalized ATR.
3. Calculate the ratio of the normalized ATR to its 100-day SMA.
4. Determine the target leverage based on the inverse of the ratio (2 / ratio).
5. Calculate the target number of open trades by multiplying the target leverage by 5.
6. Plot the target number of open trades and the current number of open trades on the chart.
7. Check if there's an opportunity to buy (if the current number of open trades is less than the target) or close a trade (if the current number of open trades is more than the target plus 1).
8. If there's an opportunity to buy, open a long trade and add the trade's name to the openTrades array.
9. If there's an opportunity to close a trade and there are trades in the openTrades array, close the most recent trade by referencing the array and remove it from the array.
This system aims to capture trends in the market by dynamically adjusting the number of open trades and leverage based on the market's volatility. It uses an array to keep track of open trades, allowing for better control over the opening and closing of individual trades.
Rocket Grid Algorithm - The Quant ScienceThe Rocket Grid Algorithm is a trading strategy that enables traders to engage in both long and short selling strategies. The script allows traders to backtest their strategies with a date range of their choice, in addition to selecting the desired strategy - either SMA Based Crossunder or SMA Based Crossover.
The script is a combination of trend following and short-term mean reversing strategies. Trend following involves identifying the current market trend and riding it for as long as possible until it changes direction. This type of strategy can be used over a medium- to long-term time horizon, typically several months to a few years.
Short-term mean reversing, on the other hand, involves taking advantage of short-term price movements that deviate from the average price. This type of strategy is usually applied over a much shorter time horizon, such as a few days to a few weeks. By rapidly entering and exiting positions, the strategy seeks to capture small, quick gains in volatile market conditions.
Overall, the script blends the best of both worlds by combining the long-term stability of trend following with the quick gains of short-term mean reversing, allowing traders to potentially benefit from both short-term and long-term market trends.
Traders can configure the start and end dates, months, and years, and choose the length of the data they want to work with. Additionally, they can set the percentage grid and the upper and lower destroyers to manage their trades effectively. The script also calculates the Simple Moving Average of the chosen data length and plots it on the chart.
The trigger for entering a trade is defined as a crossunder or crossover of the close price with the Simple Moving Average. Once the trigger is activated, the script calculates the total percentage of the side and creates a grid range. The grid range is then divided into ten equal parts, with each part representing a unique grid level. The script keeps track of each grid level, and once the close price reaches the grid level, it opens a trade in the specified direction.
The equity management strategy in the script involves a dynamic allocation of equity to each trade. The first order placed uses 10% of the available equity, while each subsequent order uses 1% less of the available equity. This results in the allocation of 9% for the second order, 8% for the third order, and so on, until a maximum of 10 open trades. This approach allows for risk management and can help to limit potential losses.
Overall, the Rocket Grid Algorithm is a flexible and powerful trading strategy that can be customized to meet the specific needs of individual traders. Its user-friendly interface and robust backtesting capabilities make it an excellent tool for traders looking to enhance their trading experience.
Auto Harmonic Pattern - Backtester [Trendoscope]We are finally here with the implementation of backtesting tool for Auto-Harmonic-Pattern-UltimateX .
CAUTION: THIS IS NOT A STRATEGY AND SHOULD NOT BE FOLLOWED BLINDLY. WE ENCOURAGE USERS TO UTILISE THIS AS BACKTESTING TOOL FOR BUILDING THEIR STRATEGY BASED ON HARMONIC PATTERNS
This script is based on our premium indicator - Auto-Harmonic-Pattern-UltimateX . In this script, along with implementation of scanning harmonic patterns, we provide various options via settings which enables users to build their own strategy based on harmonic patterns, use them with custom coded filters, backtest them on various tickers and timeframes.
Harmonic Patterns is concept and we can trade harmonic pattern in many ways. While general interest around harmonic patterns is to find reversal zones and use them for short term swing trades. But, using it along trend following strategies can also be very rewarding. Here is one of the educational idea I shared about using harmonic patterns for trend following. These are just few possibilities where users can explore further on how they want to trade this. The settings of this script are crafted in such a way that it enables users to explore all these possibilities.
🎲 Components
Chart components of this script is lighter compared to Auto Harmonic Pattern - UltimateX. This is because we want to keep lighter interface in order to support seamless execution of emulator. Since pine strategy framework does most of the things such as calculating profitability, keeping track of trades and results etc, display with respect to - "Closed Trade Stats" are removed from this script and "Open Trade Stats" are made lighter.
🎲 Settings
🎯 Trade Settings : Few important settings under this section are
Due to pine limitations, we will not be able to support both long and short in a same setup. Hence, users need to chose either long or short trade setup.
Entry/Base/Target play important role in defining your strategy.
Confluence is another important factor which lets users use multiple patterns at once as confirmation.
🎯 Zigzag Settings : Zigzag settings determine the size of patterns being formed.
Please note that smaller patterns may not yield very good results and larger patterns may take time to complete trade. Similarly higher depth can cause runtime issues. Recursive zigzag option is alternative to deep search algorithm.
🎯 Filters :
Filters enable users to select trades based on specific conditions. Ability to use external filter even allows writing and using custom filters to be used with this algorithm. Here is a video which explains how this can be done. HOW-TO-Use-external-filters
Pattern filters allow users to pick and chose patterns they want to trade. This can be done either individually or based on category
🎯 Alerts :
Apart from strategy specific alerts, the script also implements customisable alerts via pine alert() function. Alerts can be configured to send upon three conditions
When new pattern is created
When an existing pattern updates entry/stop/target due to safe repaint of D (Only happens when Trail Entry Price is selected)
When a pattern in trade closes either due to hitting stop or target
Important Note: Alerts fired via this method may not match the trades shown on chart as trades which are controlled via pine strategy emulator depends on various other factors such as pyramiding.
Alert template is customisable and users can make use of available placeholders to get dynamic data in alerts. Valid placeholders are
{alertType} - Alert type - New/Update/Close
{id} - Pattern Id
{ticker} - Ticker
{timeframe} - Chart timeframe
{price} - Current price
{patterns} - Identified pattern names
{direction} - Direction - Long/Short
{entry} - Entry Price
{stop} - Stop Price
{target} - Target Price
{orderType} - Limit/Stop - applicable for only New and Update types
{status} - Trade status. Valid values are Pending/Cancelled/Stopped/Success
Template is common for all custom alert types. Hence, updating the template will impact all custom alerts - New/Update/Close
{
"alert" : "{alertType}",
"id" : {id},
"ticker" : "{ticker}",
"timeframe" : "{timeframe}",
"price" : {price},
"patterns" : "{patterns}",
"direction" : "{direction}",
"entry" : {entry},
"stop" : {stop},
"target" : {target},
"orderType" : {orderType}
"status" : {status}
}
Here is a video on how to customise the alerts using templates and placeholders - HOW-TO-Customize-Alerts-With-Placeholders
🎯 Miscellaneous :
These are simple settings to control display and backtest bars. If you are running alerts, we suggest turning of Open Trades and Drawings and limit backtest to minimal value in order to improve efficiency of
🎯 Backtest Engine Parameters :
Default settings are optimised for trend following. Users are encouraged to play around with settings and filters to build strategy out of this tool.
Position sizing is not leveraged. Margin settings makes sure that trades cannot exceed capital.
All measures are taken to avoid repainting. Script does not use request.security and real time bars. This drastically reduces the risk of repainting in scripts.
If you are premium user, please select "Bar Magnifier".
Breakeven Line DemoPlug the breakeven line code into the bottom of your strategy to get visualization of your breakeven price. Line is green when net position is long, red when net short and invisible when strategy is risk neutral. Most useful for strategies which use pyramiding (successive entries in same direction stacked upon each other) as it can be difficult to track where prices needs to return to to allow closing in profit. Disclaimer: the strategy logic is garbage do not use it. It is only there so you can see how Breakeven Line logic is implemented.
3Commas Bot DCA Backtester & Signals FREEThis is a DCA Strategy backtester + signals, built to emulate the 3Commas DCA bots. It uses your choice of 4 different buy signals, 2 of which can be adjusted in the settings. Everything is customizable so you can backtest specific settings with different buy signals and find the best performing strategy for your risk tolerance and capital. It can be used to backtest strategies on stocks as well, but just make sure your base order is larger than the share price for the entire backtesting range or it will not calculate properly.
You can use this template to code your own buy signals and then backtest them as a DCA strategy if you know some basic pine script.
The indicator shows all of your backtesting orders on the chart. The red line is your take profit level, the blue line is your average price level, the white line is your first order and the green lines are your average down orders. If you enable a stop loss in the settings your stop loss will be shown as an orange line once all of your average down orders have been hit, it will not be set until price has dipped below your covered trading range.
These levels update when things change during backtesting so you can visualize your strategy and how it would perform as well as see if your percentage deviation is large enough to cover dips. When backtesting trades are taken, the chart will show where they were taken(in backtesting) along with info on those trades such as the number each order is, the size of that order and the percentage deviation that order is from the initial buy.
SENDING SIGNALS TO 3COMMAS
Tradingview cannot sync this backtester to 3Commas and with the way alerts are setup for strategies on Tradingview, the best option for you to give signals to your bot would be to use this backtester to figure out what trigger you want to use and then setup that indicator separately to send alerts to your bot. All of the indicators used for signals in this backtester are available for free and can be configured to match this backtester and send alerts to 3Commas for you. Just make sure you set your alerts to once per bar close and don’t use less than a 15 second timeframe because then you could trigger the Tradingview threshold for alerts and get your alerts shut off.
You can also use this backtester with your own buy triggers if you know a little pine script. Just make copy of the script and code in your own buy signals and see how it backtests.
INFO PANEL FOR ANALYZING YOUR STRATEGY
The right hand side of the screen will show an info panel that shows a lot of different information so you can quickly see your bot settings and how it performed right on the screen.
In the top right corner you will see in purple your bot settings. These include your stoploss % if turned on, take profit %, average down order %, average down order % multiplier, volume multiplier, max number of orders allowed and size of your base order.
The top section of the first column “Current Trade” shows these stats: the open trade’s average price, the open trade’s take profit price, the open trade’s PNL, how far price is from your open tarde’s take profit level in percentage, your open position size and number of open orders.
The bottom section of the first column “Overall Performance” shows these stats: total number of trades taken during backtesting range, the largest amount of trades that were open at one time during backtesting, the max drawdown, the average number of bars per trade, gross profit, net profit, percent profit from your initial capital, current portfolio value and your initial capital.
CUSTOMIZABLE OPTIONS TO FIND THE PERFECT STRATEGY
Stoploss On/Off
This will turn your stoploss on or off. By default it is set to off and will not affect anything unless turned on.
Stoploss Percentage
This is the percentage below your final average down order price that will be set as a stoploss to keep your account from going too far in the red on big dips.
Take Profit Percentage - This is the percentage of profit you want the trade to hit before taking profit on your entire DCA trade. This level updates everytime you average down.
Average Down Percentage - This is the percentage that price has to drop from your initial order to initiate your first safety order. If the Average Down Percent Multiplier is set to 1 then this percentage will be the same for every average down order.
Average Down Percentage Multiplier - This multiplies your Average Down Percentage so each safety order needs a larger percentage deviation than the previous one. This keeps your buys closer together at the beginning and further apart when you hit more orders so you can extend your trading range but still be aggressive when price is going sideways.
Volume Multiplier Per New Order - This multiplies the size of each trade based on your base order. If you set it to a 2x multiplier then each average down order will be 2 times the size of the last one. So for example, a $100 base order with a 2x multiplier would have these values for the first 3 average down orders: 200, 400, 800.
Size Of Base Order - This is the size of your first position entry and will be used as a starting point for the volume multiplier. If your base order is $100 then it will buy $100 worth of whatever crypto you are backtesting this on. If you are looking at stock charts, you need to make sure your base order is higher than the share price across the entire backtesting range or it will not perform correctly.
Max Number Of Orders - This is the maximum number of orders the bot can take, including your base order. Adjust this to suit the amount of capital you are willing to allocate to your bot based on how much money it will require to run according to your bot settings.
TIPS ON HOW TO USE FOR BEST RESULTS
If you don’t have a lot of capital to work with, then use longer timeframes with a reasonable take profit percentage so that you don’t need a lot of average down orders. You can also try keeping the volume multiplier close to 1.
You can use the 3Commas dca bot settings page to see how much capital you will need for your strategy if you match it to the settings you have on this indicator. You can also check to see how much of a percentage deviation your bot is covering to make sure you have a reasonable range to trade in and orders to cover big dips. You can also check your coverage by seeing how far down the chart the green lines cover, which are your average down orders.
Make sure the initial capital in the properties tab of the settings has enough to cover all of your orders otherwise you will get unrealistic backtesting results. Also, make sure you leave the order size in the properties tab on contracts so it calculates your trades correctly. The only settings you need to touch in the properties tab is the initial capital. Unless you are trading somewhere that has lower commission fees, then you can change that to match, but leave all the other settings as is for it to function properly.
Increasing the volume multiplier will make your average price and take profit target follow the price action a lot closer as price falls, but it can also lead to having very large orders very quickly once you get into the 1.5-3x multiple range. Try using a high volume multiplier with less safety orders and you will get better results, however you need to have money on the sidelines to add on major dips to keep your bot turning a profit. Be very careful with this as greed and impatience will hurt your overall performance. This bot is meant to make money with lots of small wins so don’t get greedy and make sure you have enough money to cover large dips. If you are being aggressive with your bot, then I recommend only using 25% or less of your portfolio to trade aggressively and then use the smart trade feature on 3commas to add chunks of funds to your trades when price dips below your last safety order. Or if you want it to run without any supervision, then use lower volume multipliers and have lots of safety orders that can cover entire bear markets and still keep buying lower.
It’s a good idea to have some capital on the sidelines that you can add in when price dips quickly. This will help lower your average price and allow your bot to get out in profit quicker. 3Commas bot has a smart trade feature that will allow you to track your average price when adding extra funds and it will automatically update your other orders which is very convenient. Look at the longer timeframes when price dips and only add chunks at major areas where price is very likely to bounce. Or you can be aggressive when trading and add to your position when price dips and is at a likely bounce zone to maximize profits.
Only trade coins that have a good amount of liquidity as the larger your orders get, the harder it will be to sell if there isn’t much liquidity. Also, beware of how large your first order is as it will usually be a market order and can move the market if there is not much liquidity.
Since this bot takes a lot of trades and performs best when taking small profits consistently, you will need to factor in exchange fees. The bot is set to .5% commission(you can change this) on the buy and sell orders as most exchanges charge that amount. Some exchanges offer no fee trading on certain coins so be sure to look around for those so you can keep the commissions and maximize profits.
I strongly encourage you to try out a lot of different setting combinations across multiple different coins and do it across a few months to see how it would have performed under various market conditions. This will help you get a better idea of how much of a percentage deviation you’ll need to be able to cover to keep your bot running and making constant profits. You can also use the deep backtesting feature of the strategy panel to see how it would have done, but just beware that the info panel of the indicator will not reflect deep backtesting results, only the normal backtesting range.
MARKETS
This backtester can be used on any market including crypto, stocks, forex & futures. You just need to make sure your base order is larger than the share price when using this on things besides crypto.
TIMEFRAMES
This backtester can be used on all timeframes.
Strategy Based on Percent of Stocks Above/Below Key MovingThis Strategy looks to buy the market after the percentage of stocks below the 20 SMA moving average drops below 30% and crosses back above it. The strategy outperforms buy and hold on the S&P and more importantly only has a max draw down of 11% which gives it a much better risk adjusted performance then buy and hold alone.
It has three sell rules, 1. When the same indicator crosses into overbought territory. 2. Index Closes below the 200 SMA. 3. Stop Loss is triggered (default is Trailing stop loss).
The indicator used can be found here :
The Strategy has been coded so that all the variables can be adjusted so you can tweak it to get the best performance to whatever market you like. I have hard coded the best variables I could find to trade the AMEX:SPY .
You can track market breadth on the following markets :
Market Tickers Available = SP500 , DJI, NQ, NQ100, R2000, R3000, SP500 Financials, SP500 Materials, SP500 Energy, SP500 Staples, SP500 Discretionary, SP500 Industrials , SP500 Real Estates, Overall Market
The strategy can be used on any of these moving averages : 20, 50, 100, 150, 200
You can adjust the greed and fear levels to change when the strategy takes trades at Overbought and Oversold Levels
Stop Loss
Two Stop losses are available a fixed stop loss based on an ATR value or a trailing % Stop Loss
Regime Filters
Two Regime filters are available:
1. a simple moving average (Strategy wont take trades under the 200 SMA)
2. Advance/Decline Filter Details can be found here:
Date Filter
Tick StrategyTick Strategy:
Questions many pine coders/traders have is, How to enter/exit trade as soon as trade condition is met i.e. do not wait till candle completion to enter/exit the trade. This strategy will help you to understand one of the way to achieve it.
This is an educational strategy to demonstrate, how one can trade based on tick data. This being a strategy based on tick data, it can be tested only on real time candles. This strategy will not take any trades on historical candles and cannot be used for back testing. All the strategy trades taken on real time candles will disappear (repainting) once chart is refreshed and new trades will be entered on real time candles.
The strategy will do nothing during off market hours and will not take any trades.
The strategy has been designed based on rules/inputs below:
1. Count the ticks from start of a candle till end of candle
2. Bifurcate ticks as up-ticks and down-ticks. If tick price is above previous tick price the tick is considered as up-tick and vice versa
3. Count the successive up-ticks and successive down-ticks
Strategy rules:
1. Track candle type (green or red) continuously on each tick (green candle is when latest tick price > previous tick price)
2. Take a long trade if work in progress (WIP) candle is green candle and we get successive up-ticks equal to user input ticks for trade
3. Take a short trade if work in progress (WIP) candle is red candle and we get successive down-ticks equal to user input ticks for trade
4. Exit the trade when we get successive ticks equal to user input ticks in opposite direction
5. Optionally for trade entry, user can decide whether to calculate successive up-ticks/down-ticks from beginning of candle or successive up-ticks/down-ticks anytime during the candle formation
6. Optionally for trade exit/square off, user can decide whether to apply exit rules on the entry candle or only from subsequent candle
Strategy setting:
1. '' – This is just to describe when trades are entered. This parameter is not used for any calculation
2. 'No of successive ticks to enter the trade' – User input to decide, number of successive ticks for trade entry
3. 'Count successive ticks for trade only from start of candle' – check this to count successive ticks only from beginning of a candle
4. 'Exit if succussive ticks in opposite direction' - User input to decide, number of successive ticks in opposite direction for exiting the trade
5. 'Apply exit criteria on entry candle' – check to allow exit of trade on the entry candle, if un-checked, trade will not be exited on the entry candle i.e. opposite direction ticks will be counted from subsequent candle
Information below will be displayed continuously on the chart:
1. Candle no – Candles are counted from start of the trading session. This is current candle being formed on the chart
2. Candle now – This shows either ‘Green’ or ‘Red’ based on type of candle being formed
3. Tick count – This is current tick number being processed. Tick number starts from 1 for each new candle
4. Up-tick count – Number of up-ticks during formation of current candle
5. Down-tick count – Number of down-ticks during formation of current candle
6. Successive up-ticks – Current successive up-tick count
7. Successive down-ticks – Current successive down-tick count
8. Up-tick volume – Volume associated with up-ticks
9. Down-tick volume – Volume associated with down-ticks
10. Up-tick volume % - This is % of volume associated with up-ticks
11. Total volume – Candle volume till now. (Some times you might observe small difference between total volume and the volume shown by volume indicator. The difference could be because of refresh rate of your screen)
12. Candle completion % - This shows current candles completion %. This is candle progress from start of candle till close of candle
Candles - The WhaleThe strategy is based on candle patterns, with confluence indicators. Note some of the candles are rare, they can be found only a specific timeframe, or within specific stocks.
The added patterns are as follows:
- Bullish Engulfing
It is formed by two candles, the second candlestick engulfing the first candlestick. The first candle is a bearish candle that indicates the continuation of the downtrend.
The second candlestick is a long bullish candle that completely engulfs the first candle and shows that the bulls are back in the market.
- Bullish Harami
It consists of two candlestick charts, the first candlestick being a tall bearish candle, and the second, being a small bullish candle which should be in the range of the first candlestick.
The first bearish candle shows the continuation of the bearish trend and the second candle shows that the bulls are back in the market.
- Piercing Line
Two candles from it, the first candle being a bearish candle which indicates the continuation of the downtrend.
The second candle is a bullish candle that opens the gap down but closes more than 50% of the real body of the previous candle, which shows that the bulls are back in the market and a bullish reversal is going to take place.
- Bullish Belt
A Bullish Belt Hold, known as “yorikiri” in Japanese, is a single Japanese candlestick pattern that suggests a possible reversal of the current downtrend.
- Bullish Kicker
A bullish kicker is a two candlestick pattern that’s usually formed after a significant downtrend, but could also appear after an uptrend.
- Abandoned Baby
The abandoned baby candlestick pattern is a three-bar reversal pattern. It is similar to the morning and evening star formations and is a very reliable reversal signal when it occurs after a sharp rise or drop.
- Homing Pigeon
The bullish homing pigeon is a two-candle bullish reversal pattern that occurs at the end of a bearish trend. Both candles are negative, but the second candle is confined within the range of the previous candle.
- Breakaway
Breakaway patterns are multi-candle formations found on Japanese candlestick charts that suggest a market reversal may be in the offing. An actual breakaway is a five candlestick formation that occurs in either an upward or downward trend.
- Concealing Baby Swallow
The Concealing Baby Swallow candlestick pattern is a four-candlestick pattern that can signal a bullish reversal price movement or a bearish continuation move, depending on where it occurs in the overall market structure. However, the pattern is quite rare, and you may not encounter it in your trading.
- Doji Dragonfly
A dragonfly doji can be an indicator of a reversal in price. When the price of a security has shown a downward trend, it might signal an upcoming price increase.
- Doji Gravestone
The Gravestone Doji forms when the price closes at relatively the same level where it opened, providing that the open coincides with the low or at least the two are very close
- Doji Star
Consists of a long bullish candle, followed by a Doji that gaps up, then a third bearish candle that gaps down and closes well within the body of the first candle. An Evening Doji Star is a three-candle bearish reversal pattern similar to the Evening Star.
- Kicker
A kicker pattern is a two-bar candlestick pattern that predicts a change in the direction of an asset's price trend. This pattern is characterized by a sharp reversal in price over the span of two candlesticks. Traders use it to determine which group of market participants is in control of the direction.
- Ladder Bottom
Is a five-candle bullish reversal pattern that occurs at the end of a bullish trend. The four first candles are bearish and followed by a positive candle that starts with a positive gap.
- MatHold
A bullish pattern begins with a large bullish candle followed by a gap higher and three smaller candles which move lower.
- Matching Low
A matching low is a bullish two-candle reversal pattern that forms in an ongoing downtrend. As to its appearance, a matching low consists of two candlesticks that are negative, but the close at or around the same price.
- Meeting Lines
Bullish meeting lines are a two-candle bullish reversal pattern that occurs in a downtrend and signals a reversal of the trend. The first candle of the bullish meeting lines is bearish, and followed by a positive candle that closes very near the close of the previous candle.
- Morning Doji Star
It is made of 3 candlesticks, the first being a bearish candle, the second, a Doji, and the third being a bullish candle.
The first candle shows the continuation of the downtrend. The second candle being a doji indicates indecision in the market. The third bullish candle shows that the bulls are back in the market and a reversal will take place. The second candle should be completely out of the real bodies of the first and third candles.
- Rising Three Method1
Rising three methods is a bullish continuation pattern that appears in an ongoing uptrend. The Rising three methods pattern consists of five candles. where the first and last candles are long and bullish, with three small bearish candles in between.
- Rising Three Method2
It is the same as "Rising Three Method1" but with a different calculation
- Upside Tasuki Gap1
Is a bullish continuation candlestick pattern that forms in an ongoing uptrend. It consists of three candles, where the two first are bullish with a positive gap in-between, and followed by a negative candle that closes in the gap formed between the first two candles.
- Upside Tasuki Gap2
It is the same as "Upside Tasuki Gap1" but with a different calculation
- Three Line Strike1
Is a four-candle bullish continuation candlestick pattern. It forms a bullish trend and is believed to signal the continuation of the bullish trend.
- Three Line Strike2
It is the same as "Three Line Strike2" but with a different calculation
The confluence indicators:
Movement Averages: multi options checks, you can select what to check to open a position, and the description of each open is in the strategy.
Volume Oscillator: positive signal if the volume is above zero.
EMA: positive signal if the candle source is above EMA.
VWAP: positive signal if the candle source is above VWAP.
BTC Trend: it is an equation to track the BTC price movement
The profit is based on TSL, while the stop loss is based on ATR, or lowest candle.
The recommended time frame is 4 hours.
3LS | 3 Line Strike Strategy [Kintsugi Trading]What is the 3LS | 3 Line Strike Strategy?
Incorporating the 3 Line Strike candlestick pattern into our strategy was inspired by Arty at The Moving Average and the amazing traders at TheTrdFloor .
The Three Line Strike is a trend continuation candlestick pattern consisting of four candles. Depending on their heights and collocation, a bullish or a bearish trend continuation can be predicted.
In a symphony of trend analysis, price action, and volume we can find and place high-probability trades with the 3LS Strategy.
How to use it!
----- First, start by choosing a Stop-Loss Strategy, Stop PIP Size, and Risk/Reward Ratio -----
- Stop-Loss Strategy
Fixed PIP Size – This uses the top/bottom of the indicator candle and places a TP based on the chosen Risk:Reward ratio.
ATR Trail (No set Target Profit, only uses ATR Stop)
ATR Trail-Stop (Has set Target Profit, however, stop is based on ATR inputs)
**If you choose an ATR Stop-Loss Strategy - input the desired ATR period and Multiple you would like the stop to be calculated at**
**ATR Stop-Loss Strategies have a unique alert setup for Auto-Trading. See Auto-Trading Section**
- Risk/Reward Ratio = If you have a .5 risk/reward, it means you are risking $100 to make $50.
- Additional Stop PIP Size = Number of PIPs over the default stop location of the top or bottom of the indicator candle.
----- Next, we set the Session Filter -----
Set the Timezone and Trade Session you desire. If no specific session is desired, simply set the Trade Session to 00:00 - 00:00.
----- Next, we set the Moving Average Cloud Fill -----
Enter the Fast and Slow Moving Average Length used to calculate trend direction:
MA Period Fast
MA Period Slow
These inputs will determine whether the strategy looks for Long or Short positions.
----- Next, we set the VSA – Volume Spread Analysis Settings -----
Check the box to show the indicator at the bottom of the chart if desired.
This is just a different visual output of the VSA | Volume Spread Analysis indicator available for free under the community indicators tab. You can add that indicator to your chart and see the same output in candle format.
In combination with the Moving Average Cloud, the Volume Spread Analysis will help us determine when to take a trade and in what direction.
The strategy is essentially looking for small reversals going against the overall trend and placing a trade once that reversal ends and the price moves back in the direction of the overall trend.
The 3LS Strategy utilizes confirmation between trend, volume, and price action to place high probability trades.
The VSA is completely customizable by:
Moving Average Length
MA-1 Multiplier
MA-2 Multiplier
MA-3 Multiplier
Check out the VSA | Volume Spread Analysis indicator in the community scripts section under the indicators tab to use this awesome resource on other strategies.
----- Next, we have the option to view the automated KT Bull/Bear Signals -----
Check the boxes to show the buy-sell signal on the chart if desired.
----- Next, we set the risk we want to use if Auto Trading the strategy -----
I always suggest using no more than 1-3% of your total account balance per trade. Remember, if you have multiple strategies triggering per day with each using 1%, the total percent at risk will be much larger.
For Example – if you have 10 strategies each risking 1% your total risk is 10% of your account, not 1%! Be mindful to only use 1-3% of your total account balance across all strategies, not just each individual one.
----- Finally, we backtest our ideas -----
After using the 'Strategy Tester' tab on TradingView to thoroughly backtest your predictions you are ready to take it to the next level - Automated Trading!
This was my whole reason for creating the script. If you work a full-time job, live in a time zone that is hard to trade, or just don't have the patience, this will be a game-changer for you as it was for me.
Auto-Trading
When it comes to auto-trading this strategy I have included two options in the script that utilize the alert messages generated by TradingView.
*Note: Please trade on a demo account until you feel comfortable enough to use real money, and then please stick to 1%-2% of your total account value in risk per trade.*
AutoView
PineConnector
**ATR Auto-Trading Alert Setup**
How to create alerts on 3 Line Strike Strategy
For Trailing Stops:
1) Adjust autoview/pineconnector settings
2) Click "add alert"
3) Select "Condition" = Strategy Name
4) Select "Order Fills Only" from the drop-down
3) Remove template message text from "message" box and place the exact text. '{{strategy.order.alert_message}}'
4) Click "create"
For Fixed Pip Stop:
1) Adjust autoview/pineconnector settings
2) Click "add alert"
3) Select "Condition" = Strategy Name
4) Select "alert() function calls only"
5) I like to title my Alert Name the same thing I named it as an Indicator Template to keep track
Good luck with your trading!
STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT [Loxx]STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT is the backtest strategy for "STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones " seen below:
Included:
This backtest uses a special implementation of ATR and ATR smoothing called "True Range Double" which is a range calculation that accounts for volatility skew.
You can set the backtest to 1-2 take profits with stop-loss
Signals can't exit on the same candle as the entry, this is coded in a way for 1-candle delay post entry
This should be coupled with the INDICATOR version linked above for the alerts and signals. Strategies won't paint the signal "L" or "S" until the entry actually happens, but indicators allow this, which is repainting on current candle, but this is an FYI if you want to get serious with Pinescript algorithmic botting
You can restrict the backtest by dates
It is advised that you understand what Heikin-Ashi candles do to strategies, the default settings for this backtest is NON Heikin-Ashi candles but you have the ability to change that in the source selection
This is a mathematically heavy, heavy-lifting strategy with multi-layered adaptivity. Make sure you do your own research so you understand what is happening here. This can be used as its own trading system without any other oscillators, moving average baselines, or volatility/momentum confirmation indicators.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Breakout Finder Strategy by The Accumulation ZoneThe Breakout Strategy:
Indicators used:
Least Squared Moving Average by Tradingview
Smoothed Moving Average by Tradingview
MACD Support Resistance by venkatachari_n
About this Strategy:
This strategy is based on spotting a particular activity pattern involving the above listed indicators:
A fast moving average that will track closely with price while still smoothing out some price chop
A slower least squared moving average to help gauge short-term momentum
MACD Support and Resistance to help identify longer-term trends and potentially serve to also guide directional bias
If all entry conditions are met, the strategy enters a position. As well as sending an alert message for the Entry, TP/SL Signals
Long Condition:
Price close above MACD S/R Line
SMMA crossed MACD S/R Line to the upside
LSMA crossed MACD S/R Line to the upside
Short Condition:
Price close below MACD S/R Line
SMMA crossed MACD S/R Line to the downside
LSMA crossed MACD S/R Line to the downside
Strategy Settings
SL based on ATR Bands (0.9 ATR Multiplier recommended*)
TP based on RR (1.5 RR recommended*)
Optional EMA Filter (If set to 0 -> disabled)
Session Filter
Custom Strategy Backtesting Dashboard (Risk = 5%*)
*Recommended for a Daily BTC/USDT Chart
MarketCipher B Wavetrend DivergencesCreated for the MarketCipher Community and friends :)
I have published this before but it was taken down by Tradingview and PineCoders because they wanted a more in depth description so here it is:
This strategy is mainly based on Wavetrend Oscillator by LazyBear / blue momentum waves on MarketCipher B.
The Wavetrend indicator is a combination of 2 oscillator lines that signals the short term direction of the price once the lines cross. The Wavetrend indicator is useful but only once a divergence has been identified based on the crosses and the price which is what this strategy partly uses to open trades.
Here is a list and description of the different conditions that goes into the entries and exits.
Long trade:
1) Bullish divergence, regular or hidden
2) Price is above Exponential Moving Average
3) Chande Momentum Oscillator value is above x
Short trade:
1) Bearish divergence, regular or hidden
2) Price is below Exponential Moving Average
3) Chande Momentum Oscillator value is below x
The Exponential Moving Average (EMA) is a type of moving average that is price based, lagging (or reactive) indicator that displays the average price of a security over a set period of time. The EMA is however different from a normal moving average and values the recent price action. A Moving Average is a good way to confirm trends which is what it is used for in this strategy. If enabled the strategy will only open long trades above the EMA and only short trades below the EMA.
The Chande Momentum Oscillator is a technical momentum indicator and was designed specifically to track the movement and momentum of a security. The oscillator calculates the difference between the sum of both recent gains and recent losses, then dividing the result by the sum of all price movement over the same period. In this strategy it is used like the EMA to filter out bad trades that goes against the trend. The EMA is better at trading the overall trend but the Chande Momentum Oscillator is a lot better at identifying short term market conditions that are favorable for entering at divergences.
One of the most important aspects when creating a trading strategy is to know when to take profit and to make it as dynamic as possible so that it changes to the market conditions. This is what i have tried to do and the reason why this divergence trading strategy works well.
These are the 3 different exit conditions:
1) A dynamic take profit that will signal a short term trend reversal that is based on pivot points and moving averages.
2) Another dynamic take profit based on pivot points that like the previous take profit is used to determine and anticipate potential changes in market price and reversals.
3) A normal % fixed take profit
Photo of what the dynamic take profit looks like on the chart:
The pivot pointexit comes from this indicator that i have helped update and modify from the original script:
When you have found the right settings you can insert the messages from your automatic trading platform at the bottom of the inputs and then create an alert with your unique webhook address along with the alert message below:
{{strategy.order.alert_message}}
I hope this strategy will be useful to automate part of your trading or help you identify and backtest divergences for your manual trading.
Future updates to come.
Enjoy!
StochRSI + MA Strategy [Kintsugi Trading]What is the StochRSI + MA Strategy?
This premium indicator was inspired by my desire to find and place high probability forex trades in any market, direction, or time of day.
Why Forex?
The Forex markets operate 24 hours, 5.5 days a week
Access to meaningful leverage
Ability to easily trade long or short
High liquidity
How to use it!
----- First, start by choosing a Stop-Loss Strategy, Stop PIP Size, and Risk/Reward Ratio -----
- Stop-Loss Strategy
ATR Trail (No set Target Profit, only uses ATR Stop)
ATR Trail-Stop (Has set Target Profit, however, stop is based on ATR inputs)
Fixed PIP Size
**If you choose an ATR Stop-Loss Strategy - input the desired ATR period and Multiple you would like the stop to be calculated at**
**ATR Stop-Loss Strategies have a unique alert setup for Auto-Trading. See Auto-Trading Section**
- Stop PIP Size = How many PIPs will be representative of the max risk. i.e. - if you are risking $100 and you set the PIP stop to 10, that means 10 PIPs = $100.
- Risk/Reward Ratio = If you have a .5 risk/reward, it means you are risking $100 to make $50.
----- Next, we set the Session Filter. -----
Set the Timezone and Trade Session you desire. If no specific session is desired, simply set the Trade Session to 00:00 - 00:00.
----- Next, we set the Moving Average Cloud. -----
Enter the Moving Average Type:
Simple Moving Average
Exponential Moving Average
Hull Moving Average
Weighted Moving Average
Smoothed Moving Average
Double Exponential Moving Average
Triple Exponential Moving Average
Enter the fast, medium, and slow Moving Average Period you would like the Strategy to use. If you would like like to use (2) Moving Averages, simply set two of the Periods the same.
These inputs will determine whether the strategy looks for Long or Short positions.
**Boxes on the left of the fast, medium, and slow Moving Average Periods**
If you check any of these boxes, the strategy will ignore and set up where the price is trading below the checked moving average.
----- Next, we set the Stochastic RSI Parameters. -----
In combination with the Moving Average Cloud, the Stochastic RSI will help us determine when to take a trade and in what direction.
The strategy is essentially looking for small reversals going against the overall trend and placing a trade once that reversal ends and the price moves back in the direction of the overall trend.
The Stochastic RSI + MA Strategy utilizes confirmation between extreme RSI calculations and the overall trend as measured by (3) separate Moving Averages.
The Stochastic RSI is completely customizable by:
Long Entry Bar Cross Below
Short Entry Bar Cross Above
K
D
RSI Length
Stochastic Length
RSI Source
----- Finally, we backtest our ideas. -----
After using the 'Strategy Tester' tab on TradingView to thoroughly backtest your predictions you are ready to take it to the next level - Automated Trading!
This was my whole reason for creating the script. If you work a full-time job, live in a time zone that is hard to trade, or just don't have the patience, this will be a game-changer for you as it was for me.
Auto-Trading
When it comes to auto-trading this strategy I have included two options in the script that utilize the alert messages generated by TradingView.
*Note: Please trade on a demo account until you feel comfortable enough to use real money, and then please stick to 1%-2% of your total account value in risk per trade.*
AutoView
PineConnector
**ATR Auto-Trading Alert Setup**
How to create alerts on Stoch+MA Strategy
For Trailing Stops:
1) Adjust autoview/pineconnector settings
2) Click "add alert"
3) Select "Condition" = Strategy Name
4) Select "Order Fills Only" from the drop-down
3) Remove template message text from "message" box and place exact text. {{strategy.order.alert_message}}
4) Click "create"
For Fixed Pip Stop:
1) Adjust autoview/pineconnector settings
2) Click "add alert"
3) Select "Condition" = Strategy Name
4) Select "alert() function calls only"
5) I like to title my Alert Name the same thing I named it as an Indicator Template to keep track
Good luck with your trading!
BB + RSI double strategy developeI'm Korean, and it may not be enough to explain this script in English. I feel sorry for the users of TradingView for this lack of English skills. If you are Korean, please return it to the translator using Papago. It will be a useful manual for you.
This script referenced Chartart's Double Strategy. But there are some changes in his script.
0. Basically, when you break through the top or bottom of the 100th period balliser band and come back into the band, you track the overbuying and overselling of the RSI to determine your position entry. The order is triggered only when both conditions are satisfied at the same time. However, only one condition applies to clearing the position. This is because it is most effective in reducing risk and increasing assets in terms of profit and loss.
1. This script is optimized for 15 minutes of bitcoin futures chart and API via webbook alert. By default, 10x leverage usage and 10 pyramids are applied.
2. Setting a chart period other than 15 minutes will not guarantee sufficient effectiveness. It can also be applied to Ethereum , but it is not recommended to apply to other symbols.
3. I added Enable Date Filter because Chartart's script could not apply the strategy to the user's desired period. This feature allows you to set a period of time when you do not want to use the strategy. You can also uncheck it if you don't want to fully use this feature. Please remember that it is an exclusion period, not a usage period. With this feature, we can see the effectiveness of the strategy from a point in time, not from the entire period. You can also clearly differentiate the effectiveness of the strategy from the point you use it.
4. You can also stop using strategies at certain times of the day when you don't want to apply them. This works similarly to the Enalbe Date Filter described above. This allows you to sleep comfortably even if you don't fully trust this strategy.
5. The period, overbuying, and overselling figures of RSI can be set individually. For example, when you take a long position, you can set the RSI to a period of 7, and at the same time, the RSI entering the short position can be set to a period of 14. You can also set the base figures for overbuying and overselling to levels that you think are reasonable. This figure works in conjunction with the Bollinger Band and affects position entry when it is crossed or returned.
6. Based on API futures trading, basic Sleepy and commission are applied. This is geared towards market price transactions. This makes your revenue look more reasonable.
Thank you very much, Chartart. You are a genius.
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저는 한국인이고, 영어로 이 스크립트를 설명하는 것이 어렵기 때문에 설명이 부족할 수 있습니다. 이런 영어 능력 부족에 대해서 TradingView 사용자들에게 미안하게 생각합니다. 만약 당신이 한국인이라면 파파고를 사용하여 번역기에 돌려주십시오. 당신에게 유익한 설명서가 될 것입니다.
이 스크립트는 Chart art의 Double Strategy를 참조했습니다. 그러나 그의 스크립트에서 달라진 점이 몇 가지 있습니다.
0. 기본적으로 100기간의 볼린져밴드의 상단 또는 하단을 돌파한 뒤 다시 밴드 안으로 들어올 때 RSI의 과매수, 과매도를 추적하여 포지션 진입을 결정합니다. 두 가지 조건이 동시에 만족되어야만 주문이 트리거 됩니다. 그러나 포지션을 청산하는 것에는 볼린져밴드 하나의 조건만 적용합니다. 여러가지 테스트를 거친 결과 이것이 손익 면에서 가장 효과적으로 리스크를 줄이고 자산을 늘리는 것에 효율적이기 때문입니다.
1. 이 스크립트는 15분의 비트코인 선물 차트와 webhook alert을 통한 API에 최적화되어 있습니다. 기본적으로 10배의 레버리지 사용과 10개의 피라미딩이 적용되어 있습니다.
2. 15분 외에 다른 차트 기간을 설정한다면 충분한 효과를 보장할 수 없습니다. 또한 이더리움에도 적용할 수 있지만, 그 외에 다른 심볼에는 적용하지 않는 것을 권장합니다.
3. Chart art의 스크립트는 전략을 사용자가 원하는 기간에 적용할 수 없었기 때문에, 저는 Enable Date Filter를 추가하였습니다. 이 기능을 통해 전략 사용을 원하지 않는 기간을 설정할 수 있습니다. 또한 이 기능을 완전히 사용하고싶지 않다면 체크를 해제할 수 있습니다. 사용 기간이 아닌 제외 기간인 점을 상기하시길 바랍니다. 이 기능을 통해 우리는 전체 기간이 아닌 가까운 특정 시점부터의 전략 적용 효과를 확인할 수 있습니다. 또한 사용자가 전략을 사용한 시점부터의 효과를 명백히 구분할 수 있습니다.
4. 또한 사용자가 적용을 원하지 않는 하루 중의 특정 시간대에 전략 사용을 멈출 수도 있습니다. 이는 위에 설명한 Enalbe Date Filter와 유사하게 작동합니다. 이를 통해 당신이 온전히 이 전략을 신뢰하지 못하여도 당신은 마음 편하게 잠에 들 수 있습니다.
5. RSI의 기간 및 과매수, 과매도 수치를 개별적으로 설정할 수 있습니다. 예를 들어 당신이 롱 포지션을 취할 때에는 RSI를 7의 기간으로 설정할 수 있고, 동시에 숏 포지션을 진입하는 RSI는 14의 기간으로 설정될 수 있습니다. 또한 과매수 및 과매도의 기준 수치를 당신이 합리적이라고 생각하는 수준으로 설정할 수 있습니다. 이 수치는 볼린져밴드와 함께 작동하여 그것을 넘어서거나 다시 되돌아올 때 포지션 진입에 영향을 미칩니다.
6. API 선물거래를 기준으로 하여 기본적인 슬리피지와 커미션이 적용되어있습니다. 이는 시장가 거래에 맞춰져 있습니다. 이는 당신의 수익을 좀 더 합리적인 수치로 보일 수 있게 합니다.
Chartart에게 특별히 감사합니다. 당신은 천재입니다.
Small short for CryptoThis is the third modification of VuMAnChu cipher B indicator designed only for short sales.
The trigger signal is called sommi bearish diamond (according to my due diligence it has the best performance)
You can either fix a stop loss value or close position after crossing sma line.
Take profit also can be tuned.
Keep in mind that short positions are very risky, so deposit your money to the market only after your research. I guess this indicator has potential for repainting, so track backtests/forward tests carefully. The script is for entertainment purposes only.
Good luck and have fun!
Triple ThreatThis indicator provides buy and sell signals for Bitcoin based on confluence from well-known momentum, volatility, and trend indicators. It has successfully captured the major directional trends on Bitcoin's daily chart since 2018, and the settings are currently optimized for this chart in particular. This indicator implements RSI to gauge momentum, BBWP to gauge volatility, and an EMA to gauge trend. Maximum confluence signals are represented by horizontal bars in the indicator's pane, where the tallest green bar is a confirmed buy signal, and the tallest red bar is a confirmed sell signal. The shortest bar represents a momentum-only signal, and the second-shortest bar represents a volatility signal in confluence with the previously given momentum signal.
To track momentum, the RSI is plotted to the indicator plane against a moving average of the RSI. A momentum signal is generated when the RSI crosses over its moving average, retests/approaches the moving average, and then continues in the crossover direction (i.e., it fails to cross the moving average to the opposite side, creating a successful retest). The settings that affect this trigger are the "Crossover Threshold," which specifies how much the RSI should exceed the moving average to be considered a crossover, and the "Retest threshold," which specifies how closely the RSI should approach the moving average to be considered a retest. A momentum signal is ALSO generated if the RSI or its moving average exceed their counterpart by a certain threshold. For example, if the threshold was set at 10, a BUY signal would be generated when the RSI exceeds the moving average by 10, or a SELL signal would be generated when the moving average exceeds the RSI by 10. This threshold can be set using the "Instant Signal Threshold" setting. Either type of momentum signal will be plotted on the pane as the shortest horizontal bar, with its color indicating the signal's direction.
Volatility is primarily measured using the Bollinger Band Width Percentile (BBWP) indicator, which was created by The_Caretaker. BBWP plots the volatility of the asset's price, given by Bollinger Band width, relative to past volatility by assigning the volatility readings into percentiles. The indicator also includes a moving average of the BBWP itself, where a crossover to the upside represents expanding volatility and a crossover to the downside represents contracting volatility. This indicator is used to confirm a signal given by the momentum indicators - a momentum signal that is given during a period of expanding volatility has a greater likelihood of success. Therefore, when the BBWP crosses above its moving average by a given threshold, a previously triggered momentum signal is considered to be "confirmed." The threshold for this crossover can be set using the "BBWP Confirmation Threshold" setting. However, it is also relevant that periods of extreme volatility often accompany an extremity in price action (a "top" or "bottom"), in which case the BBWP is likely to contract after price reaches such an extremity. This phenomenon is captured by also using "extreme reads" on the momentum indicator to signal that there has already been enough volatility to confirm a momentum signal. If the RSI gives an "extreme read" before triggering a signal, the momentum signal is also considered to be confirmed. For example, if the RSI is above 80, breaks below 80, and then gives a SELL signal, this sell signal is considered to be confirmed without requiring the BBWP to crossover its moving average to the upside. The threshold that would confirm a SELL signal can be set with the "Overbought" setting, and the threshold that would confirm a BUY signal can be set with the "Oversold" setting. Whenever a volatility signal confirms a momentum signal, a medium-sized horizontal bar will be plotted on the pane in the same directional color as the momentum signal. Note that a momentum signal may trigger at the exact same time as the volatility signal which confirms it; in this case, only the medium-sized bar will be visible on the pane, but its direction can still be identified by its color.
Lastly, to reduce the likelihood of "false signals," a trend indicator is used to confirm the direction of the signal. This is typically an exponential moving average. If a confirmed volatility SELL signal is given, and the closing price is below the moving average, then the SELL signal is also confirmed by the trend. Likewise, if a confirmed volatility BUY signal is given, and the closing price is above the moving average, then the BUY signal is confirmed by the trend. The type and length of the moving average used to verify the trend can be set using the "Moving Average Type" and "Moving Average Length" settings found below the momentum/volatility settings. A trend signal is plotted on the pane as a tall horizontal bar, and is more deeply colored than the momentum and volatility signals.
For maximum confluence, it is recommended that the trend signal, given by the tallest bar, is the one that forms the basis of trades executed while using the Triple Threat indicator. It is possible to enter more aggressive trades with better entries by using only the volatility signal, given by the medium-sized bar, however this entails greater risk and should only be done in confluence with an additional trading strategy of your own discretion. Backtesting has shown that using the volatility signal alone underperforms using the volatility signal in confluence with the trend signal.
Please also be advised that the default setting are optimized for Bitcoin's daily chart only. The indicator is still applicable to other timeframes and asset classes, but the settings may need to be modified. I have a list of settings for other Bitcoin timeframes, and I would be happy to share them upon request.
I hope you can find this indicator to be of some use to your trading strategies. I'd be happy to hear any feedback from the community, so please don't hesitate to reach out. Stay safe, and happy trading.
QaSH Percentile BreakoutsThis script makes trades when price is trending past the x percentile ranked bar closing price. Entries can be made when price crosses the threshold, or when price pulls back and forms a pivot point. It can track up to 10 entries independently as a method of pyramiding, each with its own TP and SL price, and it provides a unique order ID to each one for your automation service to use. Several entry condition filters are provided for higher quality entries. Stoploss prices can be moved to breakeven when in profit.
Adaptive Hull MA Trend tracerBINANCE:ETHUSDT
This is the first attempt to create a "PEFECT" trend tracer.
All the Moving Averages have a lag but I personally like the Hull Moving Average for the fast response and noise reduction.
However, due to the market behavior it is hard to choose the correct length of the Moving Average that will be suitable for current situation.
This Indicator uses the Bolinger Bands Width to adapt the length of the Hull Moving Average according to the following rule:
1) if the BBW (Bolinger Bands Width) is small => length of the MA is bigger in order to filter the noise of small price changes.
2) if the BBW is bigger => MA length is smaller to track the trend more accurately.
This script also has a strategy logic with RSI for a better entry and exit as well as the minimum allowed MA length.
Further work:
1) Full adaptation of the Moving average
2) Filter bad entries
Please let me know of any suggestions for improving this indicator.
Combo Backtest 123 Reversal & T3 Averages This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
This indicator plots the moving average described in the January, 1998 issue
of S&C, p.57, "Smoothing Techniques for More Accurate Signals", by Tim Tillson.
This indicator plots T3 moving average presented in Figure 4 in the article.
T3 indicator is a moving average which is calculated according to formula:
T3(n) = GD(GD(GD(n))),
where GD - generalized DEMA (Double EMA) and calculating according to this:
GD(n,v) = EMA(n) * (1+v)-EMA(EMA(n)) * v,
where "v" is volume factor, which determines how hot the moving average’s response
to linear trends will be. The author advises to use v=0.7.
When v = 0, GD = EMA, and when v = 1, GD = DEMA. In between, GD is a less aggressive
version of DEMA. By using a value for v less than1, trader cure the multiple DEMA
overshoot problem but at the cost of accepting some additional phase delay.
In filter theory terminology, T3 is a six-pole nonlinear Kalman filter. Kalman
filters are ones that use the error — in this case, (time series - EMA(n)) —
to correct themselves. In the realm of technical analysis, these are called adaptive
moving averages; they track the time series more aggres-sively when it is making large
moves. Tim Tillson is a software project manager at Hewlett-Packard, with degrees in
mathematics and computer science. He has privately traded options and equities for 15 years.
WARNING:
- For purpose educate only
- This script to change bars colors.
Webhook Starter Kit [HullBuster]
Introduction
This is an open source strategy which provides a framework for webhook enabled projects. It is designed to work out-of-the-box on any instrument triggering on an intraday bar interval. This is a full featured script with an emphasis on actual trading at a brokerage through the TradingView alert mechanism and without requiring browser plugins.
The source code is written in a self documenting style with clearly defined sections. The sections “communicate” with each other through state variables making it easy for the strategy to evolve and improve. This is an excellent place for Pine Language beginners to start their strategy building journey. The script exhibits many Pine Language features which will certainly ad power to your script building abilities.
This script employs a basic trend follow strategy utilizing a forward pyramiding technique. Trend detection is implemented through the use of two higher time frame series. The market entry setup is a Simple Moving Average crossover. Positions exit by passing through conditional take profit logic. The script creates ten indicators including a Zscore oscillator to measure support and resistance levels. The indicator parameters are exposed through 47 strategy inputs segregated into seven sections. All of the inputs are equipped with detailed tool tips to help you get started.
To improve the transition from simulation to execution, strategy.entry and strategy.exit calls show enhanced message text with embedded keywords that are combined with the TradingView placeholders at alert time. Thereby, enabling a single JSON message to generate multiple execution events. This is genius stuff from the Pine Language development team. Really excellent work!
This document provides a sample alert message that can be applied to this script with relatively little modification. Without altering the code, the strategy inputs can alter the behavior to generate thousands of orders or simply a few dozen. It can be applied to crypto, stocks or forex instruments. A good way to look at this script is as a webhook lab that can aid in the development of your own endpoint processor, impress your co-workers and have hours of fun.
By no means is a webhook required or even necessary to benefit from this script. The setups, exits, trend detection, pyramids and DCA algorithms can be easily replaced with more sophisticated versions. The modular design of the script logic allows you to incrementally learn and advance this script into a functional trading system that you can be proud of.
Design
This is a trend following strategy that enters long above the trend line and short below. There are five trend lines that are visible by default but can be turned off in Section 7. Identified, in frequency order, as follows:
1. - EMA in the chart time frame. Intended to track price pressure. Configured in Section 3.
2. - ALMA in the higher time frame specified in Section 2 Signal Line Period.
3. - Linear Regression in the higher time frame specified in Section 2 Signal Line Period.
4. - Linear Regression in the higher time frame specified in Section 2 Signal Line Period.
5. - DEMA in the higher time frame specified in Section 2 Trend Line Period.
The Blue, Green and Orange lines are signal lines are on the same time frame. The time frame selected should be at least five times greater than the chart time frame. The Purple line represents the trend line for which prices above the line suggest a rising market and prices below a falling market. The time frame selected for the trend should be at least five times greater than the signal lines.
Three oscillators are created as follows:
1. Stochastic - In the chart time frame. Used to enter forward pyramids.
2. Stochastic - In the Trend period. Used to detect exit conditions.
3. Zscore - In the Signal period. Used to detect exit conditions.
The Stochastics are configured identically other than the time frame. The period is set in Section 2.
Two Simple Moving Averages provide the trade entry conditions in the form of a crossover. Crossing up is a long entry and down is a short. This is in fact the same setup you get when you select a basic strategy from the Pine editor. The crossovers are configured in Section 3. You can see where the crosses are occurring by enabling Show Entry Regions in Section 7.
The script has the capacity for pyramids and DCA. Forward pyramids are enabled by setting the Pyramid properties tab with a non zero value. In this case add on trades will enter the market on dips above the position open price. This process will continue until the trade exits. Downward pyramids are available in Crypto and Range mode only. In this case add on trades are placed below the entry price in the drawdown space until the stop is hit. To enable downward pyramids set the Pyramid Minimum Span In Section 1 to a non zero value.
This implementation of Dollar Cost Averaging (DCA) triggers off consecutive losses. Each loss in a run increments a sequence number. The position size is increased as a multiple of this sequence. When the position eventually closes at a profit the sequence is reset. DCA is enabled by setting the Maximum DCA Increments In Section 1 to a non zero value.
It should be noted that the pyramid and DCA features are implemented using a rudimentary design and as such do not perform with the precision of my invite only scripts. They are intended as a feature to stress test your webhook endpoint. As is, you will need to buttress the logic for it to be part of an automated trading system. It is for this reason that I did not apply a Martingale algorithm to this pyramid implementation. But, hey, it’s an open source script so there is plenty of room for learning and your own experimentation.
How does it work
The overall behavior of the script is governed by the Trading Mode selection in Section 1. It is the very first input so you should think about what behavior you intend for this strategy at the onset of the configuration. As previously discussed, this script is designed to be a trend follower. The trend being defined as where the purple line is predominately heading. In BiDir mode, SMA crossovers above the purple line will open long positions and crosses below the line will open short. If pyramiding is enabled add on trades will accumulate on dips above the entry price. The value applied to the Minimum Profit input in Section 1 establishes the threshold for a profitable exit. This is not a hard number exit. The conditional exit logic must be satisfied in order to permit the trade to close. This is where the effort put into the indicator calibration is realized. There are four ways the trade can exit at a profit:
1. Natural exit. When the blue line crosses the green line the trade will close. For a long position the blue line must cross under the green line (downward). For a short the blue must cross over the green (upward).
2. Alma / Linear Regression event. The distance the blue line is from the green and the relative speed the cross is experiencing determines this event. The activation thresholds are set in Section 6 and relies on the period and length set in Section 2. A long position will exit on an upward thrust which exceeds the activation threshold. A short will exit on a downward thrust.
3. Exponential event. The distance the yellow line is from the blue and the relative speed the cross is experiencing determines this event. The activation thresholds are set in Section 3 and relies on the period and length set in the same section.
4. Stochastic event. The purple line stochastic is used to measure overbought and over sold levels with regard to position exits. Signal line positions combined with a reading over 80 signals a long profit exit. Similarly, readings below 20 signal a short profit exit.
Another, optional, way to exit a position is by Bale Out. You can enable this feature in Section 1. This is a handy way to reduce the risk when carrying a large pyramid stack. Instead of waiting for the entire position to recover we exit early (bale out) as soon as the profit value has doubled.
There are lots of ways to implement a bale out but the method I used here provides a succinct example. Feel free to improve on it if you like. To see where the Bale Outs occur, enable Show Bale Outs in Section 7. Red labels are rendered below each exit point on the chart.
There are seven selectable Trading Modes available from the drop down in Section 1:
1. Long - Uses the strategy.risk.allow_entry_in to execute long only trades. You will still see shorts on the chart.
2. Short - Uses the strategy.risk.allow_entry_in to execute short only trades. You will still see long trades on the chart.
3. BiDir - This mode is for margin trading with a stop. If a long position was initiated above the trend line and the price has now fallen below the trend, the position will be reversed after the stop is hit. Forward pyramiding is available in this mode if you set the Pyramiding value in the Properties tab. DCA can also be activated.
4. Flip Flop - This is a bidirectional trading mode that automatically reverses on a trend line crossover. This is distinctively different from BiDir since you will get a reversal even without a stop which is advantageous in non-margin trading.
5. Crypto - This mode is for crypto trading where you are buying the coins outright. In this case you likely want to accumulate coins on a crash. Especially, when all the news outlets are talking about the end of Bitcoin and you see nice deep valleys on the chart. Certainly, under these conditions, the market will be well below the purple line. No margin so you can’t go short. Downward pyramids are enabled for Crypto mode when two conditions are met. First the Pyramiding value in the Properties tab must be non zero. Second the Pyramid Minimum Span in Section 1 must be non zero.
6. Range - This is a counter trend trading mode. Longs are entered below the purple trend line and shorts above. Useful when you want to test your webhook in a market where the trend line is bisecting the signal line series. Remember that this strategy is a trend follower. It’s going to get chopped out in a range bound market. By turning on the Range mode you will at least see profitable trades while stuck in the range. However, when the market eventually picks a direction, this mode will sustain losses. This range trading mode is a rudimentary implementation that will need a lot of improvement if you want to create a reliable switch hitter (trend/range combo).
7. No Trade. Useful when setting up the trend lines and the entry and exit is not important.
Once in the trade, long or short, the script tests the exit condition on every bar. If not a profitable exit then it checks if a pyramid is required. As mentioned earlier, the entry setups are quite primitive. Although they can easily be replaced by more sophisticated algorithms, what I really wanted to show is the diminished role of the position entry in the overall life of the trade. Professional traders spend much more time on the management of the trade beyond the market entry. While your trade entry is important, you can get in almost anywhere and still land a profitable exit.
If DCA is enabled, the size of the position will increase in response to consecutive losses. The number of times the position can increase is limited by the number set in Maximum DCA Increments of Section 1. Once the position breaks the losing streak the trade size will return the default quantity set in the Properties tab. It should be noted that the Initial Capital amount set in the Properties tab does not affect the simulation in the same way as a real account. In reality, running out of money will certainly halt trading. In fact, your account would be frozen long before the last penny was committed to a trade. On the other hand, TradingView will keep running the simulation until the current bar even if your funds have been technically depleted.
Entry and exit use the strategy.entry and strategy.exit calls respectfully. The alert_message parameter has special keywords that the endpoint expects to properly calculate position size and message sequence. The alert message will embed these keywords in the JSON object through the {{strategy.order.alert_message}} placeholder. You should use whatever keywords are expected from the endpoint you intend to webhook in to.
Webhook Integration
The TradingView alerts dialog provides a way to connect your script to an external system which could actually execute your trade. This is a fantastic feature that enables you to separate the data feed and technical analysis from the execution and reporting systems. Using this feature it is possible to create a fully automated trading system entirely on the cloud. Of course, there is some work to get it all going in a reliable fashion. Being a strategy type script place holders such as {{strategy.position_size}} can be embedded in the alert message text. There are more than 10 variables which can write internal script values into the message for delivery to the specified endpoint.
Entry and exit use the strategy.entry and strategy.exit calls respectfully. The alert_message parameter has special keywords that my endpoint expects to properly calculate position size and message sequence. The alert message will embed these keywords in the JSON object through the {{strategy.order.alert_message}} placeholder. You should use whatever keywords are expected from the endpoint you intend to webhook in to.
Here is an excerpt of the fields I use in my webhook signal:
"broker_id": "kraken",
"account_id": "XXX XXXX XXXX XXXX",
"symbol_id": "XMRUSD",
"action": "{{strategy.order.action}}",
"strategy": "{{strategy.order.id}}",
"lots": "{{strategy.order.contracts}}",
"price": "{{strategy.order.price}}",
"comment": "{{strategy.order.alert_message}}",
"timestamp": "{{time}}"
Though TradingView does a great job in dispatching your alert this feature does come with a few idiosyncrasies. Namely, a single transaction call in your script may cause multiple transmissions to the endpoint. If you are using placeholders each message describes part of the transaction sequence. A good example is closing a pyramid stack. Although the script makes a single strategy.close() call, the endpoint actually receives a close message for each pyramid trade. The broker, on the other hand, only requires a single close. The incongruity of this situation is exacerbated by the possibility of messages being received out of sequence. Depending on the type of order designated in the message, a close or a reversal. This could have a disastrous effect on your live account. This broker simulator has no idea what is actually going on at your real account. Its just doing the job of running the simulation and sending out the computed results. If your TradingView simulation falls out of alignment with the actual trading account lots of really bad things could happen. Like your script thinks your are currently long but the account is actually short. Reversals from this point forward will always be wrong with no one the wiser. Human intervention will be required to restore congruence. But how does anyone find out this is occurring? In closed systems engineering this is known as entropy. In practice your webhook logic should be robust enough to detect these conditions. Be generous with the placeholder usage and give the webhook code plenty of information to compare states. Both issuer and receiver. Don’t blindly commit incoming signals without verifying system integrity.
Setup
The following steps provide a very brief set of instructions that will get you started on your first configuration. After you’ve gone through the process a couple of times, you won’t need these anymore. It’s really a simple script after all. I have several example configurations that I used to create the performance charts shown. I can share them with you if you like. Of course, if you’ve modified the code then these steps are probably obsolete.
There are 47 inputs divided into seven sections. For the most part, the configuration process is designed to flow from top to bottom. Handy, tool tips are available on every field to help get you through the initial setup.
Step 1. Input the Base Currency and Order Size in the Properties tab. Set the Pyramiding value to zero.
Step 2. Select the Trading Mode you intend to test with from the drop down in Section 1. I usually select No Trade until I’ve setup all of the trend lines, profit and stop levels.
Step 3. Put in your Minimum Profit and Stop Loss in the first section. This is in pips or currency basis points (chart right side scale). Remember that the profit is taken as a conditional exit not a fixed limit. The actual profit taken will almost always be greater than the amount specified. The stop loss, on the other hand, is indeed a hard number which is executed by the TradingView broker simulator when the threshold is breached.
Step 4. Apply the appropriate value to the Tick Scalar field in Section 1. This value is used to remove the pipette from the price. You can enable the Summary Report in Section 7 to see the TradingView minimum tick size of the current chart.
Step 5. Apply the appropriate Price Normalizer value in Section 1. This value is used to normalize the instrument price for differential calculations. Basically, we want to increase the magnitude to significant digits to make the numbers more meaningful in comparisons. Though I have used many normalization techniques, I have always found this method to provide a simple and lightweight solution for less demanding applications. Most of the time the default value will be sufficient. The Tick Scalar and Price Normalizer value work together within a single calculation so changing either will affect all delta result values.
Step 6. Turn on the trend line plots in Section 7. Then configure Section 2. Try to get the plots to show you what’s really happening not what you want to happen. The most important is the purple trend line. Select an interval and length that seem to identify where prices tend to go during non-consolidation periods. Remember that a natural exit is when the blue crosses the green line.
Step 7. Enable Show Event Regions in Section 7. Then adjust Section 6. Blue background fills are spikes and red fills are plunging prices. These measurements should be hard to come by so you should see relatively few fills on the chart if you’ve set this up as intended. Section 6 includes the Zscore oscillator the state of which combines with the signal lines to detect statistically significant price movement. The Zscore is a zero based calculation with positive and negative magnitude readings. You want to input a reasonably large number slightly below the maximum amplitude seen on the chart. Both rise and fall inputs are entered as a positive real number. You can easily use my code to create a separate indicator if you want to see it in action. The default value is sufficient for most configurations.
Step 8. Turn off Show Event Regions and enable Show Entry Regions in Section 7. Then adjust Section 3. This section contains two parts. The entry setup crossovers and EMA events. Adjust the crossovers first. That is the Fast Cross Length and Slow Cross Length. The frequency of your trades will be shown as blue and red fills. There should be a lot. Then turn off Show Event Regions and enable Display EMA Peaks. Adjust all the fields that have the word EMA. This is actually the yellow line on the chart. The blue and red fills should show much less than the crossovers but more than event fills shown in Step 7.
Step 9. Change the Trading Mode to BiDir if you selected No Trades previously. Look on the chart and see where the trades are occurring. Make adjustments to the Minimum Profit and Stop Offset in Section 1 if necessary. Wider profits and stops reduce the trade frequency.
Step 10. Go to Section 4 and 5 and make fine tuning adjustments to the long and short side.
Example Settings
To reproduce the performance shown on the chart please use the following configuration: (Bitcoin on the Kraken exchange)
1. Select XBTUSD Kraken as the chart symbol.
2. On the properties tab set the Order Size to: 0.01 Bitcoin
3. On the properties tab set the Pyramiding to: 12
4. In Section 1: Select “Crypto” for the Trading Model
5. In Section 1: Input 2000 for the Minimum Profit
6. In Section 1: Input 0 for the Stop Offset (No Stop)
7. In Section 1: Input 10 for the Tick Scalar
8. In Section 1: Input 1000 for the Price Normalizer
9. In Section 1: Input 2000 for the Pyramid Minimum Span
10. In Section 1: Check mark the Position Bale Out
11. In Section 2: Input 60 for the Signal Line Period
12. In Section 2: Input 1440 for the Trend Line Period
13. In Section 2: Input 5 for the Fast Alma Length
14. In Section 2: Input 22 for the Fast LinReg Length
15. In Section 2: Input 100 for the Slow LinReg Length
16. In Section 2: Input 90 for the Trend Line Length
17. In Section 2: Input 14 Stochastic Length
18. In Section 3: Input 9 Fast Cross Length
19. In Section 3: Input 24 Slow Cross Length
20. In Section 3: Input 8 Fast EMA Length
21. In Section 3: Input 10 Fast EMA Rise NetChg
22. In Section 3: Input 1 Fast EMA Rise ROC
23. In Section 3: Input 10 Fast EMA Fall NetChg
24. In Section 3: Input 1 Fast EMA Fall ROC
25. In Section 4: Check mark the Long Natural Exit
26. In Section 4: Check mark the Long Signal Exit
27. In Section 4: Check mark the Long Price Event Exit
28. In Section 4: Check mark the Long Stochastic Exit
29. In Section 5: Check mark the Short Natural Exit
30. In Section 5: Check mark the Short Signal Exit
31. In Section 5: Check mark the Short Price Event Exit
32. In Section 5: Check mark the Short Stochastic Exit
33. In Section 6: Input 120 Rise Event NetChg
34. In Section 6: Input 1 Rise Event ROC
35. In Section 6: Input 5 Min Above Zero ZScore
36. In Section 6: Input 120 Fall Event NetChg
37. In Section 6: Input 1 Fall Event ROC
38. In Section 6: Input 5 Min Below Zero ZScore
In this configuration we are trading in long only mode and have enabled downward pyramiding. The purple trend line is based on the day (1440) period. The length is set at 90 days so it’s going to take a while for the trend line to alter course should this symbol decide to node dive for a prolonged amount of time. Your trades will still go long under those circumstances. Since downward accumulation is enabled, your position size will grow on the way down.
The performance example is Bitcoin so we assume the trader is buying coins outright. That being the case we don’t need a stop since we will never receive a margin call. New buy signals will be generated when the price exceeds the magnitude and speed defined by the Event Net Change and Rate of Change.
Feel free to PM me with any questions related to this script. Thank you and happy trading!
CFTC RULE 4.41
These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.
