[3Commas] HA & MAHA & MA
🔷What it does: This tool is designed to test a trend-following strategy using Heikin Ashi candles and moving averages. It enters trades after pullbacks, aiming to let profits run once the risk-to-reward ratio reaches 1:1 while securing the position.
🔷Who is it for: It is ideal for traders looking to compare final results using fixed versus dynamic take profits by adjusting parameters and trade direction—a concept applicable to most trading strategies.
🔷How does it work: We use moving averages to define the market trend, then wait for opposite Heikin Ashi candles to form against it. Once these candles reverse in favor of the trend, we enter the trade, using the last swing created by the pullback as the stop loss. By applying the breakeven ratio, we protect the trade and let it run, using the slower moving average as a trailing stop.
A buy signal is generated when:
The previous candle is bearish (ha_bear ), indicating a pullback.
The fast moving average (ma1) is above the slow moving average (ma2), confirming an uptrend.
The current candle is bullish (ha_bull), showing trend continuation.
The Heikin Ashi close is above the fast moving average (ma1), reinforcing the bullish bias.
The real price close is above the open (close > open), ensuring bullish momentum in actual price data.
The signal is confirmed on the closed candle (barstate.isconfirmed) to avoid premature signals.
dir is undefined (na(dir)), preventing repeated signals in the same direction.
A sell signal is generated when:
The previous candle is bullish (ha_bull ), indicating a temporary upward move before a potential reversal.
The fast moving average (ma1) is below the slow moving average (ma2), confirming a downtrend.
The current candle is bearish (ha_bear), showing trend continuation to the downside.
The Heikin Ashi close is below the fast moving average (ma1), reinforcing bearish pressure.
The real price close is below the open (close < open), confirming bearish momentum in actual price data.
The signal is confirmed after the candle closes (barstate.isconfirmed), avoiding premature entries.
dir is undefined (na(dir)), preventing consecutive signals in the same direction.
In simple terms, this setup looks for trend continuation after a pullback, confirming entries with both Heikin Ashi and real price action, supported by moving average alignment to avoid false signals.
If the price reaches a 1:1 risk-to-reward ratio, the stop will be moved to the entry point. However, if the slow moving average surpasses this level, it will become the new exit point, acting as a trailing stop
🔷Why It’s Unique
Easily visualizes the benefits of using risk-to-reward ratios when trading instead of fixed percentages.
Provides a simple and straightforward approach to trading, embracing the "keep it simple" concept.
Offers clear visualization of DCA Bot entry and exit points based on user preferences.
Includes an option to review the message format before sending signals to bots, with compatibility for multi-pair and futures contract pairs.
🔷 Considerations Before Using the Indicator
⚠️Very important: The indicator must be used on charts with real price data, such as Japanese candlesticks, line charts, etc. Do not use it on Heikin Ashi charts, as this may lead to unrealistic results.
🔸Since this is a trend-following strategy, use it on timeframes above 4 hours, where market noise is reduced and trends are clearer. Also, carefully review the statistics before using it, focusing on pairs that tend to have long periods of well-defined trends.
🔸Disadvantages:
False Signals in Ranges: Consolidating markets can generate unreliable signals.
Lagging Indicator: Being based on moving averages, it may react late to sudden price movements.
🔸Advantages:
Trend Focused: Simplifies the identification of trending markets.
Noise Reduction: Uses Heikin Ashi candles to identify trend continuation after pullbacks.
Broad Applicability: Suitable for forex, crypto, stocks, and commodities.
🔸The strategy provides a systematic way to analyze markets but does not guarantee successful outcomes. Use it as an additional tool rather than relying solely on an automated system.
Trading results depend on various factors, including market conditions, trader discipline, and risk management. Past performance does not ensure future success, so always approach the market cautiously.
🔸Risk Management: Define stop-loss levels, position sizes, and profit targets before entering any trade. Be prepared for potential losses and ensure your approach aligns with your overall trading plan.
🔷 STRATEGY PROPERTIES
Symbol: BINANCE:BTCUSDT (Spot).
Timeframe: 4h.
Test Period: All historical data available.
Initial Capital: 10000 USDT.
Order Size per Trade: 1% of Capital, you can use a higher value e.g. 5%, be cautious that the Max Drawdown does not exceed 10%, as it would indicate a very risky trading approach.
Commission: Binance commission 0.1%, adjust according to the exchange being used, lower numbers will generate unrealistic results. By using low values e.g. 5%, it allows us to adapt over time and check the functioning of the strategy.
Slippage: 5 ticks, for pairs with low liquidity or very large orders, this number should be increased as the order may not be filled at the desired level.
Margin for Long and Short Positions: 100%.
Indicator Settings: Default Configuration.
MA1 Length: 9.
MA2 Length: 18.
MA Calculations: EMA.
Take Profit Ratio: Disable. Ratio 1:4.
Breakeven Ratio: Enable, Ratio 1:1.
Strategy: Long & Short.
🔷 STRATEGY RESULTS
⚠️Remember, past results do not guarantee future performance.
Net Profit: +324.88 USDT (+3.25%).
Max Drawdown: -81.18 USDT (-0.78%).
Total Closed Trades: 672.
Percent Profitable: 35.57%.
Profit Factor: 1.347.
Average Trade: +0.48 USDT (+0.48%).
Average # Bars in Trades: 13.
🔷 HOW TO USE
🔸 Adjust Settings:
The default values—MA1 (9) and MA2 (18) with EMA calculation—generally work well. However, you can increase these values, such as 20 and 40, to better identify stronger trends.
🔸 Choose a Symbol that Typically Trends:
Select an asset that tends to form clear trends. Keep in mind that the Strategy Tester results may show poor performance for certain assets, making them less suitable for sending signals to bots.
🔸 Experiment with Ratios:
Test different take profit and breakeven ratios to compare various scenarios—especially to observe how the strategy performs when only the trade is protected.
🔸This is an example of how protecting the trade works: once the price moves in favor of the position with a 1:1 risk-to-reward ratio, the stop loss is moved to the entry price. If the Slow MA surpasses this level, it will act as a trailing stop, aiming to follow the trend and maximize potential gains.
🔸In contrast, in this example, for the same trade, if we set a take profit at a 1:3 risk-to-reward ratio—which is generally considered a good risk-reward relationship—we can see how a significant portion of the upward move is left on the table.
🔸Results Review:
It is important to check the Max Drawdown. This value should ideally not exceed 10% of your capital. Consider adjusting the trade size to ensure this threshold is not surpassed.
Remember to include the correct values for commission and slippage according to the symbol and exchange where you are conducting the tests. Otherwise, the results will not be realistic.
If you are satisfied with the results, you may consider automating your trades. However, it is strongly recommended to use a small amount of capital or a demo account to test proper execution before committing real funds.
🔸Create alerts to trigger the DCA Bot:
Verify Messages: Ensure the message matches the one specified by the DCA Bot.
Multi-Pair Configuration: For multi-pair setups, enable the option to add the symbol in the correct format.
Signal Settings: Enable whether you want to receive long or short signals (Entry | TP | SL), copy and paste the the messages for the DCA Bots configured.
Alert Setup:
When creating an alert, set the condition to the indicator and choose "alert() function call only.
Enter any desired Alert Name.
Open the Notifications tab, enable Webhook URL, and paste the Webhook URL.
For more details, refer to the section: "How to use TradingView Custom Signals".
Finalize Alerts: Click Create, you're done! Alerts will now be sent automatically in the correct format.
🔷 INDICATOR SETTINGS
MA 1: Fast MA Length
MA 2: Slow MA Length
MA Calc: MA's Calculations (SMA,EMA, RMA,WMA)
TP Ratio: This is the take profit ratio relative to the stop loss, where the trade will be closed in profit.
BE Ratio: This is the breakeven ratio relative to the stop loss, where the stop loss will be updated to breakeven or if the MA2 is greater than this level.
Strategy: Order Type direction in which trades are executed.
Use Custom Test Period: When enabled signals only works in the selected time window. If disabled it will use all historical data available on the chart.
Test Start and End: Once the Custom Test Period is enabled, here you select the start and end date that you want to analyze.
Check Messages: Enable the table to review the messages to be sent to the bot.
Entry | TP | SL: Enable this options to send Buy Entry, Take Profit (TP), and Stop Loss (SL) signals.
Deal Entry and Deal Exit : Copy and paste the message for the deal start signal and close order at Market Price of the DCA Bot. This is the message that will be sent with the alert to the Bot, you must verify that it is the same as the bot so that it can process properly so that it executes and starts the trade.
DCA Bot Multi-Pair: You must activate it if you want to use the signals in a DCA Bot Multi-pair in the text box you must enter (using the correct format) the symbol in which you are creating the alert, you can check the format of each symbol when you create the bot.
👨🏻💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented.
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The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
Indicadores e estratégias
AO/AC Trading Zones Strategy [Skyrexio] Overview
AO/AC Trading Zones Strategy leverages the combination of Awesome Oscillator (AO), Acceleration/Deceleration Indicator (AC), Williams Fractals, Williams Alligator and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Combination of AO and AC is used for creating so-called trading zones to create the signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over. In some special cases strategy uses AO and AC combination to trail profit (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Both AC and AO shall print two consecutive increasing values. At the price candle close which corresponds to this condition algorithm opens the first long trade with 10% of capital.
4. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
5. If AO and AC both continue printing the rising values strategy opens the long trade on each candle close with 10% of capital while number of opened trades reaches 5.
6. If AO and AC both has printed 5 rising values in a row algorithm close all trades if candle's low below the low of the 5-th candle with rising AO and AC values in a row.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting:
EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation).
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about the trading zones concept and its signals. To understand this we need to briefly introduce what is AO and AC. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO) , where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now let's discuss the trading zones concept and how it can create the signal. Zones are created by the combination of AO and AC. We can divide three zone types:
Greed zone: when the AO and AC both are rising
Red zone: when the AO and AC both are decreasing
Gray zone: when one of AO or AC is rising, the other is falling
Gray zone is considered as uncertainty. AC and AO are moving in the opposite direction. Strategy skip such price action to decrease the chance to stuck in the losing trade during potential sideways. Red zone is also not interesting for the algorithm because both indicators consider the trend as bearish, but strategy opens only long trades. It is waiting for the green zone to increase the chance to open trade in the direction of the potential uptrend. When we have 2 candles in a row in the green zone script executes a long trade with 10% of capital.
Two green zone candles in a row is considered by algorithm as a bullish trend, but now so strong, that's the reason why trade is going to be closed when the combination of Alligator and Fractals will consider the the trend change from bullish to bearish. If id did not happens, algorithm starts to count the green zone candles in a row. When we have 5 in a row script change the trade closing condition. Such situation is considered is a high probability strong bull market and all trades will be closed if candle's low will be lower than fifth green zone candle's low. This is used to increase probability to secure the profit. If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. Each trade uses 10% of capital.
Why we use trading zones signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC and AO values in the direction of the most likely main trend signaling that we have the high probability of the fastest bullish phase on the market. The main idea is to take part in such rapid moves and add trades if this move continues its acceleration according to indicators.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -9.49%
Maximum Single Profit: +24.33%
Net Profit: +4374.70 USDT (+43.75%)
Total Trades: 278 (39.57% win rate)
Profit Factor: 2.203
Maximum Accumulated Loss: 668.16 USDT (-5.43%)
Average Profit per Trade: 15.74 USDT (+1.37%)
Average Trade Duration: 60 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
Boilerplate Configurable Strategy [Yosiet]This is a Boilerplate Code!
Hello! First of all, let me introduce myself a little bit. I don't come from the world of finance, but from the world of information and communication technologies (ICT) where we specialize in data processing with the aim of automating it and eliminating all human factors and actors in the processes. You could say that I am an algotrader.
That said, in my journey through trading in recent years I have understood that this world is often shown to be incomplete. All those who want to learn about trading only end up learning a small part of what it really entails, they only seek to learn how to read candlesticks. Therefore, I want to share with the entire community a fraction of what I have really understood it to be.
As a computer scientist, the most important thing is the data, it is the raw material of our work and without data you simply cannot do anything. Entropy is simple: Data in -> Data is transformed -> Data out.
The quality of the outgoing data will directly depend on the incoming data, there is no greater mystery or magic in the process. In trading it is no different, because at the end of the day it is nothing more than data. As we often say, if garbage comes in, garbage comes out.
Most people focus on the results only, on the outgoing data, because in the end we all want the same thing, to make easy money. Very few pay attention to the input data, much less to the process.
Now, I am not here to delude you, because there is no bigger lie than easy money, but I am here to give you a boilerplate code that will help you create strategies where you only have to concentrate on the quality of the incoming data.
To the Point
The code is a strategy boilerplate that applies the technique that you decide to customize for the criteria for opening a position. It already has the other factors involved in trading programmed and automated.
1. The Entry
This section of the boilerplate is the one that each individual must customize according to their needs and knowledge. The code is offered with two simple, well-known strategies to exemplify how the code can be reused for your own benefits.
For the purposes of this post on tradingview, I am going to use the simplest of the known strategies in trading for entries: SMA Crossing
// SMA Cross Settings
maFast = ta.sma(close, length)
maSlow = ta.sma(open, length)
The Strategy Properties for all cases published here:
For Stock TSLA H1 From 01/01/2025 To 02/15/2025
For Crypto XMR-USDT 30m From 01/01/2025 To 02/15/2025
For Forex EUR-USD 5m From 01/01/2025 To 02/15/2025
But the goal of this post is not to sell you a dream, else to show you that the same Entry decision works very well for some and does not for others and with this boilerplate code you only have to think of entries, not exits.
2. Schedules, Days, Sessions
As you know, there are an infinite number of markets that are susceptible to the sessions of each country and the news that they announce during those sessions, so the code already offers parameters so that you can condition the days and hours of operation, filter the best time parameters for a specific market and time frame.
3. Data Filtering
The data offered in trading are numerical series presented in vectors on a time axis where an endless number of mathematical equations can be applied to process them, with matrix calculation and non-linear regressions being the best, in my humble opinion.
4. Read Fundamental Macroeconomic Events, News
The boilerplate has integration with the tradingview SDK to detect when news will occur and offers parameters so that you can enable an exclusion time margin to not operate anything during that time window.
5. Direction and Sense
In my experience I have found the peculiarity that the same algorithm works very well for a market in a time frame, but for the same market in another time frame it is only a waste of time and money. So now you can easily decide if you only want to open LONG, SHORT or both side positions and know how effective your strategy really is.
6. Reading the money, THE PURPOSE OF EVERYTHING
The most important section in trading and the reason why many clients usually hire me as a financial programmer, is reading and controlling the money, because in the end everyone wants to win and no one wants to lose. Now they can easily parameterize how the money should flow and this is the genius of this boilerplate, because it is what will really decide if an algorithm (Indicator: A bunch of math equations) for entries will really leave you good money over time.
7. Managing the Risk, The Ego Destroyer
Many trades, little money. Most traders focus on making money and none of them know about statistics and the few who do know something about it, only focus on the winrate. Well, with this code you can unlock what really matters, the true success criteria to be able to live off of trading: Profit Factor, Sortino Ratio, Sharpe Ratio and most importantly, will you really make money?
8. Managing Emotions
Finally, the main reason why many lose money is because they are very bad at managing their emotions, because with this they will no longer need to do so because the boilerplate has already programmed criteria to chase the price in a position, cut losses and maximize profits.
In short, this is a boilerplate code that already has the data processing and data output ready, you only have to worry about the data input.
“And so the trader learned: the greatest edge was not in predicting the storm, but in building a boat that could not sink.”
DISCLAIMER
This post is intended for programmers and quantitative traders who already have a certain level of knowledge and experience. It is not intended to be financial advice or to sell you any money-making script, if you use it, you do so at your own risk.
[SHORT ONLY] 10 Bar Low Pullback█ STRATEGY DESCRIPTION
The "10 Bar Low Pullback" strategy is a contrarian short trading system designed to capture pullbacks after a new 10‐bar low is made. it identifies a potential short opportunity when the current bar’s low breaks below the lowest low of the previous 10 bars, provided that the bar exhibits strong internal momentum as measured by its IBS value. An optional trend filter further refines entries by requiring that the close is below a 200-period EMA.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
ibs = (close - low) / (high - low)
- Low IBS (≤ 0.2): Indicates the close is near the bar's low, suggesting oversold conditions.
- High IBS (≥ 0.8): Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The current bar’s low is below the lowest low of the past X bars (default: 10).
The bar’s IBS is greater than the specified threshold (default: 0.85).
The signal occurs within the defined trading window (between Start Time and End Time).
If the EMA Filter is enabled, the close must be below the 200-period EMA.
2. EXIT CONDITION
An exit Signal is generated when the current close falls below the previous bar’s low (close < low ), indicating a potential bearish reversal and prompting the strategy to close its short position.
█ ADDITIONAL SETTINGS
Lookback Period: Defines the number of bars (default is 10) over which the lowest low is calculated.
IBS Threshold: Sets the minimum required IBS value (default is 0.85) to qualify as a pullback.
Trading Window: Trades are only executed between the user-defined Start Time and End Time.
EMA Filter (Optional): When enabled, short entries are only considered if the current close is below the 200-period EMA, with the EMA period being adjustable (default is 200).
█ PERFORMANCE OVERVIEW
Designed for shorting opportunities, this strategy aims to capture pullbacks following an aggressive 10-bar low break.
It leverages a combination of a lookback low and IBS measurement to identify overextended bullish moves that may revert.
The optional EMA filter helps confirm a bearish market environment by ensuring the price remains under the trend line.
Suitable for use on various assets, including stocks and ETFs, on daily or similar timeframes.
Backtesting and parameter optimization are recommended to tailor the strategy to specific market conditions.
[SHORT ONLY] ATR Sell the Rip Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "ATR Sell the Rip Mean Reversion Strategy" is a contrarian system that targets overextended price moves on stocks and ETFs. It calculates an ATR‐based trigger level to identify shorting opportunities. When the current close exceeds this smoothed ATR trigger, and if the close is below a 200-period EMA (if enabled), the strategy initiates a short entry, aiming to profit from an anticipated corrective pullback.
█ HOW IS THE ATR SIGNAL BAND CALCULATED?
This strategy computes an ATR-based signal trigger as follows:
Calculate the ATR
The strategy computes the Average True Range (ATR) using a configurable period provided by the user:
atrValue = ta.atr(atrPeriod)
Determine the Threshold
Multiply the ATR by a predefined multiplier and add it to the current close:
atrThreshold = close + atrValue * atrMultInput
Smooth the Threshold
Apply a Simple Moving Average over a specified period to smooth out the threshold, reducing noise:
signalTrigger = ta.sma(atrThreshold, smoothPeriodInput)
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The current close is above the smoothed ATR signal trigger.
The trade occurs within the specified trading window (between Start Time and End Time).
If the EMA filter is enabled, the close must also be below the 200-period EMA.
2. EXIT CONDITION
An exit Signal is generated when the current close falls below the previous bar’s low (close < low ), indicating a potential bearish reversal and prompting the strategy to close its short position.
█ ADDITIONAL SETTINGS
ATR Period: The period used to calculate the ATR, allowing for adaptability to different volatility conditions (default is 20).
ATR Multiplier: The multiplier applied to the ATR to determine the raw threshold (default is 1.0).
Smoothing Period: The period over which the raw ATR threshold is smoothed using an SMA (default is 10).
Start Time and End Time: Defines the time window during which trades are allowed.
EMA Filter (Optional): When enabled, short entries are only executed if the current close is below the 200-period EMA, confirming a bearish trend.
█ PERFORMANCE OVERVIEW
This strategy is designed for use on the Daily timeframe, targeting stocks and ETFs by capitalizing on overextended price moves.
It utilizes a dynamic, ATR-based trigger to identify when prices have potentially peaked, setting the stage for a mean reversion short entry.
The optional EMA filter helps align trades with broader market trends, potentially reducing false signals.
Backtesting is recommended to fine-tune the ATR multiplier, smoothing period, and EMA settings to match the volatility and behavior of specific markets.
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions.
Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach.
🔹 What Makes This Strategy Unique?
Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions.
🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts.
🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals.
🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure.
🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart.
📊 Core Strategy Components
1️⃣ Momentum-Based Trade Execution
The strategy generates long and short trade signals based on the following conditions:
✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum.
✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure.
Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline.
2️⃣ Position Sizing & Leverage Control
Risk management is a key focus of this strategy:
🔹 Dynamic Position Sizing – The script calculates position size based on:
Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations.
Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting.
🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears.
This design ensures maximum capital efficiency while adapting to market conditions in real time.
3️⃣ Performance Visualization & Tracking
Understanding historical performance is crucial for refining strategies. The script includes:
📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference.
📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time.
📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics.
📢 Why Use This Strategy?
✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals.
✔ Fully Automated Trading – Removes emotional bias and enforces discipline.
✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile.
✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades.
This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe.
📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
[SHORT ONLY] Consecutive Bars Above MA Strategy█ STRATEGY DESCRIPTION
The "Consecutive Bars Above MA Strategy" is a contrarian trading system aimed at exploiting overextended bullish moves in stocks and ETFs. It monitors the number of consecutive bars that close above a chosen short-term moving average (which can be either a Simple Moving Average or an Exponential Moving Average). Once the count reaches a preset threshold and the current bar’s close exceeds the previous bar’s high within a designated trading window, a short entry is initiated. An optional EMA filter further refines entries by requiring that the current close is below the 200-period EMA, helping to ensure that trades are taken in a bearish environment.
█ HOW ARE THE CONSECUTIVE BULLISH COUNTS CALCULATED?
The strategy utilizes a counter variable, `bullCount`, to track consecutive bullish bars based on their relation to the short-term moving average. Here’s how the count is determined:
Initialize the Counter
The counter is initialized at the start:
var int bullCount = na
Bullish Bar Detection
For each bar, if the close is above the selected moving average (either SMA or EMA, based on user input), the counter is incremented:
bullCount := close > signalMa ? (na(bullCount) ? 1 : bullCount + 1) : 0
Reset on Non-Bullish Condition
If the close does not exceed the moving average, the counter resets to zero, indicating a break in the consecutive bullish streak.
█ SIGNAL GENERATION
1. SHORT ENTRY
A short signal is generated when:
The number of consecutive bullish bars (i.e., bars closing above the short-term MA) meets or exceeds the defined threshold (default: 3).
The current bar’s close is higher than the previous bar’s high.
The signal occurs within the specified trading window (between Start Time and End Time).
Additionally, if the EMA filter is enabled, the entry is only executed when the current close is below the 200-period EMA.
2. EXIT CONDITION
An exit signal is triggered when the current close falls below the previous bar’s low, prompting the strategy to close the short position.
█ ADDITIONAL SETTINGS
Threshold: The number of consecutive bullish bars required to trigger a short entry (default is 3).
Trading Window: The Start Time and End Time inputs define when the strategy is active.
Moving Average Settings: Choose between SMA and EMA, and set the MA length (default is 5), which is used to assess each bar’s bullish condition.
EMA Filter (Optional): When enabled, this filter requires that the current close is below the 200-period EMA, supporting entries in a downtrend.
█ PERFORMANCE OVERVIEW
This strategy is designed for stocks and ETFs and can be applied across various timeframes.
It seeks to capture mean reversion by shorting after a series of bullish bars suggests an overextended move.
The approach employs a contrarian short entry by waiting for a breakout (close > previous high) following consecutive bullish bars.
The adjustable moving average settings and optional EMA filter allow for further optimization based on market conditions.
Comprehensive backtesting is recommended to fine-tune the threshold, moving average parameters, and filter settings for optimal performance.
[SHORT ONLY] Consecutive Close>High[1] Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "Consecutive Close > High " Mean Reversion Strategy is a contrarian daily trading system for stocks and ETFs. It identifies potential shorting opportunities by counting consecutive days where the closing price exceeds the previous day's high. When this consecutive day count reaches a predetermined threshold, and if the close is below a 200-period EMA (if enabled), a short entry is triggered, anticipating a corrective pullback.
█ HOW ARE THE CONSECUTIVE BULLISH COUNTS CALCULATED?
The strategy uses a counter variable called `bullCount` to track how many consecutive bars meet a bullish condition. Here’s a breakdown of the process:
Initialize the Counter
var int bullCount = 0
Bullish Bar Detection
Every time the close exceeds the previous bar's high, increment the counter:
if close > high
bullCount += 1
Reset on Bearish Bar
When there is a clear bearish reversal, the counter is reset to zero:
if close < low
bullCount := 0
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The count of consecutive bullish closes (where close > high ) reaches or exceeds the defined threshold (default: 3).
The signal occurs within the specified trading window (between Start Time and End Time).
2. EXIT CONDITION
An exit Signal is generated when the current close falls below the previous bar’s low (close < low ), prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Threshold: The number of consecutive bullish closes required to trigger a short entry (default is 3).
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
EMA Filter (Optional): When enabled, short entries are only triggered if the current close is below the 200-period EMA.
█ PERFORMANCE OVERVIEW
This strategy is designed for Stocks and ETFs on the Daily timeframe and targets overextended bullish moves.
It aims to capture mean reversion by entering short after a series of consecutive bullish closes.
Further optimization is possible with additional filters (e.g., EMA, volume, or volatility).
Backtesting should be used to fine-tune the threshold and filter settings for specific market conditions.
[SHORT ONLY] Internal Bar Strength (IBS) Mean Reversion Strategy█ STRATEGY DESCRIPTION
The "Internal Bar Strength (IBS) Strategy" is a mean-reversion strategy designed to identify trading opportunities based on the closing price's position within the daily price range. It enters a short position when the IBS indicates overbought conditions and exits when the IBS reaches oversold levels. This strategy is Short-Only and was designed to be used on the Daily timeframe for Stocks and ETFs.
█ WHAT IS INTERNAL BAR STRENGTH (IBS)?
Internal Bar Strength (IBS) measures where the closing price falls within the high-low range of a bar. It is calculated as:
IBS = (Close - Low) / (High - Low)
- Low IBS (≤ 0.2) : Indicates the close is near the bar's low, suggesting oversold conditions.
- High IBS (≥ 0.8) : Indicates the close is near the bar's high, suggesting overbought conditions.
█ SIGNAL GENERATION
1. SHORT ENTRY
A Short Signal is triggered when:
The IBS value rises to or above the Upper Threshold (default: 0.9).
The Closing price is greater than the previous bars High (close>high ).
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
An exit Signal is generated when the IBS value drops to or below the Lower Threshold (default: 0.3). This prompts the strategy to exit the position.
█ ADDITIONAL SETTINGS
Upper Threshold: The IBS level at which the strategy enters trades. Default is 0.9.
Lower Threshold: The IBS level at which the strategy exits short positions. Default is 0.3.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for Stocks and ETFs markets and performs best when prices frequently revert to the mean.
The strategy can be optimized further using additional conditions such as using volume or volatility filters.
It is sensitive to extreme IBS values, which help identify potential reversals.
Backtesting results should be analyzed to optimize the Upper/Lower Thresholds for specific instruments and market conditions.
CBC Strategy with Trend Confirmation & Separate Stop LossCBC Flip Strategy with Trend Confirmation and ATR-Based Targets
This strategy is based on the CBC Flip concept taught by MapleStax and inspired by the original CBC Flip indicator by AsiaRoo. It focuses on identifying potential reversals or trend continuation points using a combination of candlestick patterns (CBC Flips), trend filters, and a time-based entry window. This approach helps traders avoid false signals and increase trade accuracy.
What is a CBC Flip?
The CBC Flip is a candlestick-based pattern that identifies moments when the market is likely to change direction or strengthen its trend. It checks for a shift in price behavior between consecutive candles, signaling a bullish (upward) or bearish (downward) move.
However, not all flips are created equal! This strategy differentiates between Strong Flips and All Flips, allowing traders to choose between a more conservative or aggressive approach.
Strong Flips vs. All Flips
Strong Flips
A Strong Flip is a high-probability setup that occurs only after liquidity is swept from the previous candle’s high or low.
What is a liquidity sweep? This happens when the price briefly moves beyond the high or low of the previous candle, triggering stop-losses and trapping traders in the wrong direction. These sweeps often create fuel for the next move, making them powerful reversal signals.
Examples:
Long Setup: The price dips below the previous candle’s low (sweeping liquidity) and then closes higher, signaling a potential bullish move.
Short Setup: The price moves above the previous candle’s high and then closes lower, signaling a potential bearish move.
Why Use Strong Flips?
They provide fewer signals, but the accuracy is generally higher.
Ideal for trending markets where liquidity sweeps often mark key turning points.
All Flips
All Flips are less selective, offering both Strong Flips and additional signals without requiring a liquidity sweep.
This approach gives traders more frequent opportunities but comes with a higher risk of false signals, especially in sideways markets.
Examples:
Long Setup: A CBC flip occurs without sweeping the previous low, but the trend direction is confirmed (slow EMA is still above VWAP).
Short Setup: A CBC flip occurs without sweeping the previous high, but the trend is still bearish (slow EMA below VWAP).
Why Use All Flips?
Provides more frequent entries for active or aggressive traders.
Works well in trending markets but requires caution during consolidation periods.
How This Strategy Works
The strategy combines CBC Flips with multiple filters to ensure better trade quality:
Trend Confirmation: The slow EMA (20-period) must be positioned relative to the VWAP to confirm the overall trend direction.
Long Trades: Slow EMA must be above VWAP (upward trend).
Short Trades: Slow EMA must be below VWAP (downward trend).
Time-Based Filter: Traders can specify trading hours to limit entries to a particular time window, helping avoid low-volume or high-volatility periods.
Profit Target and Stop-Loss:
Profit Target: Defined as a multiple of the 14-period ATR (Average True Range). For example, if the ATR is 10 points and the profit target multiplier is set to 1.5, the strategy aims for a 15-point profit.
Stop-Loss: Uses a dynamic, candle-based stop-loss:
Long Trades: The trade closes if the market closes below the low of two candles ago.
Short Trades: The trade closes if the market closes above the high of two candles ago.
This approach adapts to recent price behavior and protects against unexpected reversals.
Customizable Settings
Strong Flips vs. All Flips: Choose between a more selective or aggressive entry style.
Profit Target Multiplier: Adjust the ATR multiplier to control the distance for profit targets.
Entry Time Range: Define specific trading hours for the strategy.
Indicators and Visuals
Fast EMA (10-Period) – Black Line
Slow EMA (20-Period) – Red Line
VWAP (Volume-Weighted Average Price) – Orange Line
Visual Labels:
▵ (Triangle Up) – Marks long entries (buy signals).
▿ (Triangle Down) – Marks short entries (sell signals).
Credits
CBC Flip Concept: Inspired by MapleStax, who teaches this concept.
Original Indicator: Developed by AsiaRoo, this strategy builds on the CBC Flip framework with additional features for improved trade management.
Risks and Disclaimer
This strategy is for educational purposes only and does not constitute financial advice.
Trading involves significant risk and may result in the loss of capital. Past performance does not guarantee future results. Use this strategy in a simulated environment before applying it to live trading.
2xSPYTIPS Strategy by Fra public versionThis is a test strategy with S&P500, open source so everyone can suggest everything, I'm open to any advice.
Rules of the "2xSPYTIPS" Strategy :
This trading strategy is designed to operate on the S&P 500 index and the TIPS ETF. Here’s how it works:
1. Buy Conditions ("BUY"):
- The S&P 500 must be above its **200-day simple moving average (SMA 200)**.
- This condition is checked at the **end of each month**.
2. Position Management:
- If leverage is enabled (**2x leverage**), the purchase quantity is increased based on a configurable percentage.
3. Take Profit:
- A **Take Profit** is set at a fixed percentage above the entry price.
4. Visualization & Alerts:
- The **SMA 200** for both S&P 500 and TIPS is plotted on the chart.
- A **BUY signal** appears visually and an alert is triggered.
What This Strategy Does NOT Do
- It does not use a **Stop Loss** or **Trailing Stop**.
- It does not directly manage position exits except through Take Profit.
Smart MA Crossover BacktesterSmart MA Crossover Backtester - Strategy Overview
Strategy Name: Smart MA Crossover Backtester
Published on: TradingView
Applicable Markets: Works well on crypto (tested profitably on ETH)
Strategy Concept
The Smart MA Crossover Backtester is an improved Moving Average (MA) crossover strategy that incorporates a trend filter and an ATR-based stop loss & take profit mechanism for better risk management. It aims to capture trends efficiently while reducing false signals by only trading in the direction of the long-term trend.
Core Components & Logic
Moving Averages (MA) for Entry Signals
Fast Moving Average (9-period SMA)
Slow Moving Average (21-period SMA)
A trade signal is generated when the fast MA crosses the slow MA.
Trend Filter (200-period SMA)
Only enters long positions if price is above the 200-period SMA (bullish trend).
Only enters short positions if price is below the 200-period SMA (bearish trend).
This helps in avoiding counter-trend trades, reducing whipsaws.
ATR-Based Stop Loss & Take Profit
Uses the Average True Range (ATR) with a multiplier of 2 to calculate stop loss.
Risk-Reward Ratio = 1:2 (Take profit is set at 2x ATR).
This ensures dynamic stop loss and take profit levels based on market volatility.
Trading Rules
✅ Long Entry (Buy Signal):
Fast MA (9) crosses above Slow MA (21)
Price is above the 200 MA (bullish trend filter active)
Stop Loss: Below entry price by 2× ATR
Take Profit: Above entry price by 4× ATR
✅ Short Entry (Sell Signal):
Fast MA (9) crosses below Slow MA (21)
Price is below the 200 MA (bearish trend filter active)
Stop Loss: Above entry price by 2× ATR
Take Profit: Below entry price by 4× ATR
Why This Strategy Works Well for Crypto (ETH)?
🔹 Crypto markets are highly volatile – ATR-based stop loss adapts dynamically to market conditions.
🔹 Long-term trend filter (200 MA) ensures trading in the dominant direction, reducing false signals.
🔹 Risk-reward ratio of 1:2 allows for profitable trades even with a lower win rate.
This strategy has been tested on Ethereum (ETH) and has shown profitable performance, making it a strong choice for crypto traders looking for trend-following setups with solid risk management. 🚀
JMA Quantum Edge: Adaptive Precision Trading System JMA Quantum Edge: Adaptive Precision Trading System - Enhanced Visuals & Risk Management
Get ready to experience a groundbreaking trading strategy that adapts in real-time to market conditions! This powerful, open-source script combines advanced technical analysis with state-of-the-art risk management tools, designed to give you the edge you need in today's dynamic markets.
What It Does:
Adaptive JMA Indicator:
Utilizes a custom Jurik Moving Average (JMA) that adjusts its sensitivity based on market volatility, ensuring you get precise signals even in the most fluctuating environments.
Dynamic Risk Management:
Features built-in support for partial exits (scaling out) to secure profits, along with an optional Kelly Criterion-based position sizing that tailors your exposure based on historical performance metrics.
Robust Error Handling:
Incorporates market condition filters—like minimum volume and maximum allowed gap percentage—to ensure trades are only executed under favorable conditions.
Vivid Visual Enhancements:
Enjoy an animated background that reflects market momentum, dynamic pivot markers, and clearly drawn trend channels. Plus, interactive tables provide real-time performance analytics and detailed error metrics.
Fully Customizable:
With a comprehensive set of inputs, you can easily tailor the strategy to your personal trading style and market preferences. Adjust everything from JMA parameters to refresh intervals for tables and labels!
How to Use It:
Add the Script:
Copy and paste the script into the Pine Script Editor on TradingView and click “Add to Chart.”
Configure Your Settings:
Customize your risk management (capital, commission, position sizing, partial exits, etc.) and tweak the JMA settings to match your preferred trading style. Use the extensive input panel to adjust visuals, alerts, and more.
Backtest & Optimize:
Run the strategy in the Strategy Tester to analyze its historical performance. Monitor real-time analytics and error metrics via the interactive tables, and fine-tune your parameters for optimal performance.
Go Live with Confidence:
Once you're satisfied with the backtest results, use the generated signals for live trading, and let the system help you stay ahead in fast-paced markets!
How to use the imputs:
This cutting-edge strategy is designed to adapt to changing market conditions and offers you complete control over your trading parameters. Here’s a breakdown of what each group of inputs does and how you should use them:
Risk Management & Trade Settings
Recalculate on Every Tick:
What it does: When enabled, the strategy recalculates on every price update.
Recommendation: Leave it true for fast charts.
Initial Capital:
What it does: Sets your starting capital for backtesting, which influences position sizing and performance metrics.
Recommendation: Start with $10,000 (or adjust according to your trading capital).
Commission (%):
What it does: Simulates the cost per trade.
Recommendation: Use a realistic rate (e.g., 0.04%).
Position Size & Quantity Type:
What they do: Define how large each trade will be. Choose between a fixed unit amount or a percentage of equity.
Recommendation: For beginners, the default fixed value is a good start. Experiment later with percentage-based sizing if needed.
Order Comment:
What it does: Adds a label to your orders for easier tracking.
Allow Reverse Orders:
What it does: If disabled, the strategy will close opposing positions before entering a new trade, reducing conflicts.
Enable Dynamic Position Sizing:
What it does: Adjusts trade size based on current volatility.
Recommendation: Beginners may start with this disabled until they understand basic sizing.
Partial Exit Inputs:
What they do:
Enable Partial Exits: When turned on, you can scale out of your position to lock in profits.
Partial Exit Profit (%): The profit percentage that triggers a partial exit.
Partial Exit Percentage: The percentage of your current position to exit. Recommendation: Use defaults (e.g., 5% profit, 50% exit) to secure profits gradually.
Kelly Criterion Option:
What it does: When enabled, adjusts your position sizing using historical performance (win rate and profit factor).
Recommendation: Beginners might leave this disabled until comfortable with backtest performance metrics.
Market Condition Filters:
What they do:
Minimum Volume: Ensures trades occur only when there’s sufficient market activity.
Maximum Gap (%): Prevents trading if there’s an unusually large gap between the previous close and current open. Recommendation: Defaults work well for most markets. If trades seem erratic, consider tightening these limits.
JMA Settings
Price Source:
What it does: The input series for the JMA calculation, typically set to the closing price.
JMA Length:
What it does: Controls the smoothing period of the JMA. Lower values are more sensitive; higher values smooth out the noise. Recommendation: Start with 21.
JMA Phase & Power:
What they do: Adjust how responsive the JMA is. Phase controls timing; power adjusts the intensity. Recommendation: Default settings (63 phase and 3 power) are a balanced starting point.
Visual Settings & Style
Show JMA Line, Pivot Lines, and Pivot Labels:
What they do: Toggle visual elements on your chart for easier signal identification.
Pivot History Count:
What it does: Limits how many historical pivot markers are displayed.
Color Settings (Up/Down Neon Colors):
What they do: Set the visual cues for buy and sell signals.
Pivot Marker & Line Style:
What they do: Choose the style and thickness of your pivot markers and lines.
Show Stats Panel:
What it does: Displays real-time performance and error metrics.
Dynamic Background & Visual Enhancements
Animate Background:
What it does: Changes the background color based on market momentum.
Show Trend Channels & Volume Zones:
What they do: Draw trend channels and highlight areas of high volatility/volume.
Show Data-Rich Labels:
What it does: Displays key metrics like volume, error percentage, and momentum on the chart.
High Volatility Threshold:
What it does: Determines the multiplier for when the chart background should change due to high volatility.
Multi-Timeframe Settings
Higher Timeframe:
What it does: Uses a higher timeframe’s JMA for trend confirmation. Recommendation: Use Daily ('D') or Weekly ('W') for broader trend analysis.
Show HTF Trend Zone & Opacity:
What they do: Display a visual zone from the higher timeframe to help confirm trends.
6. Trailing Stop Settings
Trailing Stop ATR Factor & Offset Multiplier:
What they do: Calculate trailing stops based on the Average True Range (ATR), adjusting stop distances dynamically. Recommendation: Default settings are a good balance but can be fine-tuned based on asset volatility.
Alerts & Notifications
Alerts on Pivot Formation & JMA Crossover:
What they do: Notify you when key events occur.
Dynamic Power Threshold:
What it does: Sets the sensitivity for dynamic alerts.
8. Static Stop Loss / Take Profit
Static Stop Loss (%) & Take Profit (%):
What they do: Allow you to set fixed stop loss or take profit levels. Recommendation: Leave them at 0 to disable if you prefer dynamic risk management, or set them if you have strict risk/reward preferences.
Advanced Settings
ATR Length:
What it does: Determines the period for ATR calculation, impacting trailing stop sensitivity. Recommendation: Start with 14.
Optimization Feedback & Enhanced Error Analysis
Error Metric Length & Error Threshold (%):
What they do: Calculate error metrics (like average error, skewness, and kurtosis) to help you fine-tune the JMA. Recommendation: Use the defaults and adjust if the error metrics seem off during backtesting.
UI - User-Driven Tweaking & Table Customization
Parameter Tweaker Panel, Debug/Performance Table Settings:
What they do: Provide interactive tables that display real-time performance, error metrics, and allow you to monitor strategy parameters.
Refresh Frequency Options (Table & Label Refresh Intervals):
What they do: Set how often the tables and labels update.
Recommendation: Start with an interval of 1 bar; increase it if your chart is too busy.
Important for Beginners:
Default Settings:
All default values have been chosen for balanced performance across different markets. If you ever experience unexpected behavior, start by resetting the inputs to their defaults.
Step-by-Step Adjustments:
Experiment by changing one setting at a time while observing how the strategy’s signals and performance metrics change. This will help you understand the impact of each parameter.
Resetting to Defaults:
If things seem off or you’re not getting the expected results, you can always reset the indicator. Either reload the script or use the “Reset Inputs” option (if available) to revert to the default settings.
Jump in, experiment, and enjoy the power of adaptive precision trading. This strategy is built to grow with your skills—have fun exploring and refining your trading edge!
Happy trading!
highs&lowsone of my first strategy: highs&lows
This strategy takes the highest high and the lowest low of a specified timeframe and specified bar count.
It will then takes the average between these two extremes to create a center line.
This creates a range of high middle and low.
Then the strategy takes the current market movement
which is the direct average(no specified timeframe and specified bar count) of the current high and low.
Using this "current market movement" within the range of high middle and low it determins when to buy and then sell the asset.
*********note***************
-this strategy is (bullish)
-works good with most futures assets that have volatility/ decent movement
(might add more details if I forget any)
(work in progress)
TrinityBar**TrinityBar Strategy Description**
The TrinityBar strategy is a price‐action based trading model that leverages Bill Williams’ bar thirds concept to generate entry signals and execute market orders automatically. Here’s how it works:
1. **Bar Thirds Calculation:**
The strategy calculates the range of both the current fully formed bar and the previous fully formed bar. It then divides each bar’s range into three equal parts (thirds).
- For the current bar, the lower third and upper third levels are computed.
- The same is done for the previous bar.
2. **Bar Type Classification:**
Each bar is classified into one of several types based on where its open and close fall relative to its thirds:
- **Bullish Patterns:**
- *1‑3 Bar:* Opens in the lower third and closes in the upper third.
- *2‑3 Bar:* Opens in the middle third and closes in the upper third.
- *3‑3 Bar:* Both open and close are in the upper third.
- **Bearish Patterns:**
- *3‑1 Bar:* Opens in the upper third and closes in the lower third.
- *2‑1 Bar:* Opens in the middle third and closes in the lower third.
- *1‑1 Bar:* Both open and close are in the lower third.
3. **Signal Generation:**
- **Bullish Signal:** A valid buy is generated when the previous bar exhibits any bullish pattern (1‑3, 2‑3, or 3‑3) and the current bar is either a 1‑3 or a 3‑3 bar.
- **Bearish Signal:** A valid sell is generated when the previous bar shows any bearish pattern (1‑1, 2‑1, or 3‑1) and the current bar is either a 1‑1 or a 3‑1 bar.
4. **Visual Alerts:**
When a valid signal is identified, the strategy plots a small triangle below the bar for a buy signal (labeled “B” in green) and a triangle above the bar for a sell signal (labeled “S” in red).
5. **Trade Execution:**
Once a signal is confirmed:
- If a bullish signal is generated, any short positions are closed, and if there is no existing long position, a market long order is entered.
- Conversely, if a bearish signal occurs, any long positions are closed, and a market short order is entered if not already in a short position.
This strategy is designed to capture significant price expansions by relying solely on price action and bar structure, without relying on lagging indicators. It provides a mechanical, systematic approach that removes emotional bias from trading decisions.
Iron Bot Statistical Trend Filter📌 Iron Bot Statistical Trend Filter
📌 Overview
Iron Bot Statistical Trend Filter is an advanced trend filtering strategy that combines statistical methods with technical analysis.
By leveraging Z-score and Fibonacci levels, this strategy quantitatively analyzes market trends to provide high-precision entry signals.
Additionally, it includes an optional EMA filter to enhance trend reliability.
Risk management is reinforced with Stop Loss (SL) and four Take Profit (TP) levels, ensuring a balanced approach to risk and reward.
📌 Key Features
🔹 1. Statistical Trend Filtering with Z-Score
This strategy calculates the Z-score to measure how much the price deviates from its historical mean.
Positive Z-score: Indicates a statistically high price, suggesting a strong uptrend.
Negative Z-score: Indicates a statistically low price, signaling a potential downtrend.
Z-score near zero: Suggests a ranging market with no strong trend.
By using the Z-score as a filter, market noise is reduced, leading to more reliable entry signals.
🔹 2. Fibonacci Levels for Trend Reversal Detection
The strategy integrates Fibonacci retracement levels to identify potential reversal points in the market.
High Trend Level (Fibo 23.6%): When the price surpasses this level, an uptrend is likely.
Low Trend Level (Fibo 78.6%): When the price falls below this level, a downtrend is expected.
Trend Line (Fibo 50%): Acts as a midpoint, helping to assess market balance.
This allows traders to visually confirm trend strength and turning points, improving entry accuracy.
🔹 3. EMA Filter for Trend Confirmation (Optional)
The strategy includes an optional 200 EMA (Exponential Moving Average) filter for trend validation.
Price above 200 EMA: Indicates a bullish trend (long entries preferred).
Price below 200 EMA: Indicates a bearish trend (short entries preferred).
Enabling this filter reduces false signals and improves trend-following accuracy.
🔹 4. Multi-Level Take Profit (TP) and Stop Loss (SL) Management
To ensure effective risk management, the strategy includes four Take Profit levels and a Stop Loss:
Stop Loss (SL): Automatically closes trades when the price moves against the position by a certain percentage.
TP1 (+0.75%): First profit-taking level.
TP2 (+1.1%): A higher probability profit target.
TP3 (+1.5%): Aiming for a stronger trend move.
TP4 (+2.0%): Maximum profit target.
This system secures profits at different stages and optimizes risk-reward balance.
🔹 5. Automated Long & Short Trading Logic
The strategy is built using Pine Script®’s strategy.entry() and strategy.exit(), allowing fully automated trading.
Long Entry:
Price is above the trend line & high trend level.
Z-score is positive (indicating an uptrend).
(Optional) Price is also above the EMA for stronger confirmation.
Short Entry:
Price is below the trend line & low trend level.
Z-score is negative (indicating a downtrend).
(Optional) Price is also below the EMA for stronger confirmation.
This logic helps filter out unnecessary trades and focus only on high-probability entries.
📌 Trading Parameters
This strategy is designed for flexible capital management and risk control.
💰 Account Size: $5000
📉 Commissions and Slippage: Assumes 94 pips commission per trade and 1 pip slippage.
⚖️ Risk per Trade: Adjustable, with a default setting of 1% of equity.
These parameters help preserve capital while optimizing the risk-reward balance.
📌 Visual Aids for Clarity
To enhance usability, the strategy includes clear visual elements for easy market analysis.
✅ Trend Line (Blue): Indicates market midpoint and helps with entry decisions.
✅ Fibonacci Levels (Yellow): Highlights high and low trend levels.
✅ EMA Line (Green, Optional): Confirms long-term trend direction.
✅ Entry Signals (Green for Long, Red for Short): Clearly marked buy and sell signals.
These features allow traders to quickly interpret market conditions, even without advanced technical analysis skills.
📌 Originality & Enhancements
This strategy is developed based on the IronXtreme and BigBeluga indicators,
combining a unique Z-score statistical method with Fibonacci trend analysis.
Compared to conventional trend-following strategies, it leverages statistical techniques
to provide higher-precision entry signals, reducing false trades and improving overall reliability.
📌 Summary
Iron Bot Statistical Trend Filter is a statistically-driven trend strategy that utilizes Z-score and Fibonacci levels.
High-precision trend analysis
Enhanced accuracy with an optional EMA filter
Optimized risk management with multiple TP & SL levels
Visually intuitive chart design
Fully customizable parameters & leverage support
This strategy reduces false signals and helps traders ride the trend with confidence.
Try it out and take your trading to the next level! 🚀
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Pure Price Action Breakout with 1:5 RR
Description of the Price Action Trading Script (Pine Script v6)
Overview
This script is a pure price action-based breakout strategy designed for TradingView. It identifies key breakout levels and executes long and short trades based on market structure. The strategy ensures a minimum risk-to-reward ratio (RR) of 1:5, aiming for high profitability with well-defined stop-loss and take-profit levels.
How the Script Works
1️⃣ Breakout Identification
The script uses a lookback period to find the highest high and lowest low over the last n bars.
A bullish breakout occurs when the price closes above the previous highest high.
A bearish breakout happens when the price closes below the previous lowest low.
2️⃣ Entry & Exit Strategy
Long Entry: If a bullish breakout is detected, the script enters a long position.
Short Entry: If a bearish breakout is detected, the script enters a short position.
The stop-loss is placed at the recent swing low (for long trades) or recent swing high (for short trades).
The target price is calculated based on a risk-to-reward ratio of 1:5, ensuring profitable trades.
3️⃣ Risk Management
The stop-loss prevents excessive losses by exiting trades when the market moves unfavorably.
The strategy ensures that each trade has a reward potential at least 5 times the risk.
Positions are executed based on price action only, without indicators like moving averages or RSI.
4️⃣ Visual Representation
The script plots breakout levels to help traders visualize potential trade setups.
Entry points, stop-loss, and take-profit levels are labeled on the chart for easy tracking.
Key Features & Benefits
✔ Pure Price Action – No lagging indicators, only real-time price movements.
✔ High Risk-to-Reward Ratio (1:5) – Ensures high-profit potential trades.
✔ Real-time Entry & Exit Signals – Provides accurate trade setups.
✔ Dynamic Stop-loss Calculation – Adjusts based on recent market structure.
✔ Customizable Parameters – Lookback periods and risk ratios can be modified.
ICT NY Kill Zone Auto Trading### **ICT NY Kill Zone Auto Trading Strategy (5-Min Chart)**
#### **Overview:**
This strategy is based on Inner Circle Trader (ICT) concepts, focusing on the **New York Kill Zone**. It is designed for trading GBP/USD exclusively on the **5-minute chart**, automatically entering and exiting trades during the US session.
#### **Key Components:**
1. **Time Filter**
- The strategy only operates during the **New York Kill Zone (9:30 AM - 11:00 AM NY Time)**.
- It ensures execution only on the **5-minute timeframe**.
2. **Fair Value Gaps (FVGs) Detection**
- The script identifies areas where price action left an imbalance, known as Fair Value Gaps (FVGs).
- These gaps indicate potential liquidity zones where price may return before continuing in the original direction.
3. **Order Blocks (OBs) Identification**
- **Bullish Order Block:** Occurs when price forms a strong bullish pattern, suggesting further upside movement.
- **Bearish Order Block:** Identified when a strong bearish formation signals potential downside continuation.
4. **Trade Execution**
- **Long Trade:** Entered when a bullish order block forms within the NY Kill Zone and aligns with an FVG.
- **Short Trade:** Entered when a bearish order block forms within the Kill Zone and aligns with an FVG.
5. **Risk Management**
- **Stop Loss:** Fixed at **30 pips** to limit downside risk.
- **Take Profit:** Set at **60 pips**, providing a **2:1 risk-reward ratio**.
6. **Visual Aids**
- The **Kill Zone is highlighted in blue** to help traders visually confirm the active session.
**Objective:**
This script aims to **capitalize on institutional price movements** within the New York session by leveraging ICT concepts such as FVGs and Order Blocks. By automating trade entries and exits, it eliminates emotions and ensures a disciplined trading approach.
Enhanced Bollinger Bands Strategy with SL/TP// Title: Enhanced Bollinger Bands Strategy with SL/TP
// Description:
// This strategy is based on the classic Bollinger Bands indicator and incorporates Stop Loss (SL) and Take Profit (TP) levels for automated trading. It identifies potential long and short entry points based on price crossing the lower and upper Bollinger Bands, respectively. The strategy allows users to customize several parameters to suit different market conditions and risk tolerances.
// Key Features:
// * **Bollinger Bands:** Uses Simple Moving Average (SMA) as the basis and calculates upper and lower bands based on a user-defined standard deviation multiplier.
// * **Customizable Parameters:** Offers extensive customization, including SMA length, standard deviation multiplier, Stop Loss (SL) in pips, and Take Profit (TP) in pips.
// * **Long/Short Position Control:** Allows users to independently enable or disable long and short positions.
// * **Stop Loss and Take Profit:** Implements Stop Loss and Take Profit levels based on pip values to manage risk and secure profits. Entry prices are set to the band levels on signals.
// * **Visualizations:** Provides options to display Bollinger Bands and entry signals on the chart for easy analysis.
// Strategy Logic:
// 1. **Bollinger Bands Calculation:** The strategy calculates the Bollinger Bands using the specified SMA length and standard deviation multiplier.
// 2. **Entry Conditions:**
// * **Long Entry:** Enters a long position when the closing price crosses above the lower Bollinger Band and the `Enable Long Positions` setting is enabled.
// * **Short Entry:** Enters a short position when the closing price crosses below the upper Bollinger Band and the `Enable Short Positions` setting is enabled.
// 3. **Exit Conditions:**
// * **Stop Loss:** Exits the position if the price reaches the Stop Loss level, calculated based on the input `Stop Loss (Pips)`.
// * **Take Profit:** Exits the position if the price reaches the Take Profit level, calculated based on the input `Take Profit (Pips)`.
// Input Parameters:
// * **SMA Length (length):** The length of the Simple Moving Average used to calculate the Bollinger Bands (default: 20).
// * **Standard Deviation Multiplier (mult):** The multiplier applied to the standard deviation to determine the width of the Bollinger Bands (default: 2.0).
// * **Enable Long Positions (enableLong):** A boolean value to enable or disable long positions (default: true).
// * **Enable Short Positions (enableShort):** A boolean value to enable or disable short positions (default: true).
// * **Pip Value (pipValue):** The value of a pip for the traded instrument. This is crucial for accurate Stop Loss and Take Profit calculations (default: 0.0001 for most currency pairs). **Important: Adjust this value to match the specific instrument you are trading.**
// * **Stop Loss (Pips) (slPips):** The Stop Loss level in pips (default: 10).
// * **Take Profit (Pips) (tpPips):** The Take Profit level in pips (default: 20).
// * **Show Bollinger Bands (showBands):** A boolean value to show or hide the Bollinger Bands on the chart (default: true).
// * **Show Entry Signals (showSignals):** A boolean value to show or hide entry signals on the chart (default: true).
// How to Use:
// 1. Add the strategy to your TradingView chart.
// 2. Adjust the input parameters to optimize the strategy for your chosen instrument and timeframe. Pay close attention to the `Pip Value`.
// 3. Backtest the strategy over different periods to evaluate its performance.
// 4. Use the `Enable Long Positions` and `Enable Short Positions` settings to customize the strategy for specific market conditions (e.g., only long positions in an uptrend).
// Important Notes and Disclaimers:
// * **Backtesting Results:** Past performance is not indicative of future results. Backtesting results can be affected by various factors, including market volatility, slippage, and transaction costs.
// * **Risk Management:** This strategy is provided for informational and educational purposes only and should not be considered financial advice. Always use proper risk management techniques when trading. Adjust Stop Loss and Take Profit levels according to your risk tolerance.
// * **Slippage:** The strategy takes into account slippage by specifying a slippage parameter on the `strategy` declaration. However, real-world slippage may vary.
// * **Market Conditions:** The performance of this strategy can vary significantly depending on market conditions. It may perform well in trending markets but poorly in ranging or choppy markets.
// * **Pip Value Accuracy:** **Ensure the `Pip Value` is correctly set for the specific instrument you are trading. Incorrect pip value will result in incorrect stop loss and take profit placement.** This is critical.
// * **Broker Compatibility:** The strategy's performance may vary depending on your broker's execution policies and fees.
// * **Disclaimer:** I am not a financial advisor, and this script is not financial advice. Use this strategy at your own risk. I am not responsible for any losses incurred while using this strategy.
BTC Future Gamma-Weighted Momentum Model (BGMM)The BTC Future Gamma-Weighted Momentum Model (BGMM) is a quantitative trading strategy that utilizes the Gamma-weighted average price (GWAP) in conjunction with a momentum-based approach to predict price movements in the Bitcoin futures market. The model combines the concept of weighted price movements with trend identification, where the Gamma factor amplifies the weight assigned to recent prices. It leverages the idea that historical price trends and weighting mechanisms can be utilized to forecast future price behavior.
Theoretical Background:
1. Momentum in Financial Markets:
Momentum is a well-established concept in financial market theory, referring to the tendency of assets to continue moving in the same direction after initiating a trend. Any observed market return over a given time period is likely to continue in the same direction, a phenomenon known as the “momentum effect.” Deviations from a mean or trend provide potential trading opportunities, particularly in highly volatile assets like Bitcoin.
Numerous empirical studies have demonstrated that momentum strategies, based on price movements, especially those correlating long-term and short-term trends, can yield significant returns (Jegadeesh & Titman, 1993). Given Bitcoin’s volatile nature, it is an ideal candidate for momentum-based strategies.
2. Gamma-Weighted Price Strategies:
Gamma weighting is an advanced method of applying weights to price data, where past price movements are weighted by a Gamma factor. This weighting allows for the reinforcement or reduction of the influence of historical prices based on an exponential function. The Gamma factor (ranging from 0.5 to 1.5) controls how much emphasis is placed on recent data: a value closer to 1 applies an even weighting across periods, while a value closer to 0 diminishes the influence of past prices.
Gamma-based models are used in financial analysis and modeling to enhance a model’s adaptability to changing market dynamics. This weighting mechanism is particularly advantageous in volatile markets such as Bitcoin futures, as it facilitates quick adaptation to changing market conditions (Black-Scholes, 1973).
Strategy Mechanism:
The BTC Future Gamma-Weighted Momentum Model (BGMM) utilizes an adaptive weighting strategy, where the Bitcoin futures prices are weighted according to the Gamma factor to calculate the Gamma-Weighted Average Price (GWAP). The GWAP is derived as a weighted average of prices over a specific number of periods, with more weight assigned to recent periods. The calculated GWAP serves as a reference value, and trading decisions are based on whether the current market price is above or below this level.
1. Long Position Conditions:
A long position is initiated when the Bitcoin price is above the GWAP and a positive price movement is observed over the last three periods. This indicates that an upward trend is in place, and the market is likely to continue in the direction of the momentum.
2. Short Position Conditions:
A short position is initiated when the Bitcoin price is below the GWAP and a negative price movement is observed over the last three periods. This suggests that a downtrend is occurring, and a continuation of the negative price movement is expected.
Backtesting and Application to Bitcoin Futures:
The model has been tested exclusively on the Bitcoin futures market due to Bitcoin’s high volatility and strong trend behavior. These characteristics make the market particularly suitable for momentum strategies, as strong upward or downward movements are often followed by persistent trends that can be captured by a momentum-based approach.
Backtests of the BGMM on the Bitcoin futures market indicate that the model achieves above-average returns during periods of strong momentum, especially when the Gamma factor is optimized to suit the specific dynamics of the Bitcoin market. The high volatility of Bitcoin, combined with adaptive weighting, allows the model to respond quickly to price changes and maximize trading opportunities.
Scientific Citations and Sources:
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637–654.
• Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427–465.
Moving Average Crossover StrategyCertainly! Below is an example of a professional trading strategy implemented in Pine Script for TradingView. This strategy is a simple moving average crossover strategy, which is a common approach used by many traders. It uses two moving averages (a short-term and a long-term) to generate buy and sell signals.
Input Parameters:
shortLength: The length of the short-term moving average.
longLength: The length of the long-term moving average.
Moving Averages:
shortMA: The short-term simple moving average (SMA).
longMA: The long-term simple moving average (SMA).
Conditions:
longCondition: A buy signal is generated when the short-term MA crosses above the long-term MA.
shortCondition: A sell signal is generated when the short-term MA crosses below the long-term MA.
Trade Execution:
The strategy enters a long position when the longCondition is met.
The strategy enters a short position when the shortCondition is met.
Plotting:
The moving averages are plotted on the chart.
Buy and sell signals are plotted as labels on the chart.
How to Use:
Copy the script into TradingView's Pine Script editor.
Adjust the shortLength and longLength parameters to fit your trading style.
Add the script to your chart and apply it to your desired timeframe.
Backtest the strategy to see how it performs on historical data.
This is a basic example, and professional traders often enhance such strategies with additional filters, risk management rules, and other indicators to improve performance.
Volatility Arbitrage Spread Oscillator Model (VASOM)The Volatility Arbitrage Spread Oscillator Model (VASOM) is a systematic approach to capitalizing on price inefficiencies in the VIX futures term structure. By analyzing the differential between front-month and second-month VIX futures contracts, we employ a momentum-based oscillator (Relative Strength Index, RSI) to signal potential market reversion opportunities. Our research builds upon existing financial literature on volatility risk premia and contango/backwardation dynamics in the volatility markets (Zhang & Zhu, 2006; Alexander & Korovilas, 2012).
Volatility derivatives have become essential tools for managing risk and engaging in speculative trades (Whaley, 2009). The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) measures the market’s expectation of 30-day forward-looking volatility derived from S&P 500 option prices (CBOE, 2018). Term structures in VIX futures often exhibit contango or backwardation, depending on macroeconomic and market conditions (Alexander & Korovilas, 2012).
This strategy seeks to exploit the spread between the front-month and second-month VIX futures as a proxy for term structure dynamics. The spread’s momentum, quantified by the RSI, serves as a signal for entry and exit points, aligning with empirical findings on mean reversion in volatility markets (Zhang & Zhu, 2006).
• Entry Signal: When RSI_t falls below the user-defined threshold (e.g., 30), indicating a potential undervaluation in the spread.
• Exit Signal: When RSI_t exceeds a threshold (e.g., 70), suggesting mean reversion has occurred.
Empirical Justification
The strategy aligns with findings that suggest predictable patterns in volatility futures spreads (Alexander & Korovilas, 2012). Furthermore, the use of RSI leverages insights from momentum-based trading models, which have demonstrated efficacy in various asset classes, including commodities and derivatives (Jegadeesh & Titman, 1993).
References
• Alexander, C., & Korovilas, D. (2012). The Hazards of Volatility Investing. Journal of Alternative Investments, 15(2), 92-104.
• CBOE. (2018). The VIX White Paper. Chicago Board Options Exchange.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
• Zhang, C., & Zhu, Y. (2006). Exploiting Predictability in Volatility Futures Spreads. Financial Analysts Journal, 62(6), 62-72.
• Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
