[3Commas] Turtle StrategyTurtle Strategy
🔷 What it does: This indicator implements a modernized version of the Turtle Trading Strategy, designed for trend-following and automated trading with webhook integration. It identifies breakout opportunities using Donchian channels, providing entry and exit signals.
Channel 1: Detects short-term breakouts using the highest highs and lowest lows over a set period (default 20).
Channel 2: Acts as a confirmation filter by applying an offset to the same period, reducing false signals.
Exit Channel: Functions as a dynamic stop-loss (wait for candle close), adjusting based on market structure (default 10 periods).
Additionally, traders can enable a fixed Take Profit level, ensuring a systematic approach to profit-taking.
🔷 Who is it for:
Trend Traders: Those looking to capture long-term market moves.
Bot Users: Traders seeking to automate entries and exits with bot integration.
Rule-Based Traders: Operators who prefer a structured, systematic trading approach.
🔷 How does it work: The strategy generates buy and sell signals using a dual-channel confirmation system.
Long Entry: A buy signal is generated when the close price crosses above the previous high of Channel 1 and is confirmed by Channel 2.
Short Entry: A sell signal occurs when the close price falls below the previous low of Channel 1, with confirmation from Channel 2.
Exit Management: The Exit Channel acts as a trailing stop, dynamically adjusting to price movements. To exit the trade, wait for a full bar close.
Optional Take Profit (%): Closes trades at a predefined %.
🔷 Why it’s unique:
Modern Adaptation: Updates the classic Turtle Trading Strategy, with the possibility of using a second channel with an offset to filter the signals.
Dynamic Risk Management: Utilizes a trailing Exit Channel to help protect gains as trades move favorably.
Bot Integration: Automates trade execution through direct JSON signal communication with your DCA Bots.
🔷 Considerations Before Using the Indicator:
Market & Timeframe: Best suited for trending markets; higher timeframes (e.g., H4, D1) are recommended to minimize noise.
Sideways Markets: In choppy conditions, breakouts may lead to false signals—consider using additional filters.
Backtesting & Demo Testing: It is crucial to thoroughly backtest the strategy and run it on a demo account before risking real capital.
Parameter Adjustments: Ensure that commissions, slippage, and position sizes are set accurately to reflect real trading conditions.
🔷 STRATEGY PROPERTIES
Symbol: BINANCE:ETHUSDT (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.
Period Channel 1: 20.
Period Channel 2: 20.
Period Channel 2 Offset: 20.
Period Exit: 10.
Take Profit %: Disable.
Strategy: Long & Short.
🔷 STRATEGY RESULTS
⚠️Remember, past results do not guarantee future performance.
Net Profit: +516.87 USDT (+5.17%).
Max Drawdown: -100.28 USDT (-0.95%).
Total Closed Trades: 281.
Percent Profitable: 40.21%.
Profit Factor: 1.704.
Average Trade: +1.84 USDT (+1.80%).
Average # Bars in Trades: 29.
🔷 How to Use It:
🔸 Adjust Settings:
Select your asset and timeframe suited for trend trading.
Adjust the periods for Channel 1, Channel 2, and the Exit Channel to align with the asset’s historical behavior. You can visualize these channels by going to the Style tab and enabling them.
For example, if you set Channel 2 to 40 with an offset of 40, signals will take longer to appear but will aim for a more defined trend.
Experiment with different values, a possible exit configuration is using 20 as well. Compare the results and adjust accordingly.
Enable the Take Profit (%) option if needed.
🔸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 the option to receive long or short signals (Entry | TP | SL), copy and paste 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
Period Channel 1: Period of highs and lows to trigger signals
Period Channel 2: Period of highs and lows to filter signals
Offset: Move Channel 2 to the right x bars to try to filter out the favorable signals.
Period Exit: It is the period of the Donchian channel that is used as trailing for the exits.
Strategy: Order Type direction in which trades are executed.
Take Profit %: When activated, the entered value will be used as the Take Profit in percentage from the entry price level.
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: Check Messages: Enable this option to review the messages that will 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.
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.
在腳本中搜尋"如何用wind搜索股票的发行价和份数"
AntoQQE - BarsThis script is a variation on the QQE (Quantitative Qualitative Estimation) concept applied to RSI. It calculates a smoothed RSI line, then determines a “Dynamic Average Range” around that line. By tracking the RSI’s movement relative to these upper (shortBand) and lower (longBand) levels, it determines when price momentum shifts enough to suggest a possible trend flip. The script plots color-coded candles based on these momentum conditions:
• RSI Calculation and Smoothing
An RSI value is obtained over a specified period, then smoothed by an EMA. This smoothed RSI serves as the core measure of momentum.
• Dynamic Average Range (DAR)
The script computes the volatility of the smoothed RSI using two EMAs of its bar-to-bar movements. It multiplies this volatility factor by a QQE multiplier to create upper and lower bands that adapt to changes in RSI volatility.
• Trend Flips
When the smoothed RSI crosses above or below its previous band level (shortBand or longBand), the script interprets this as a shift in momentum and sets a trend state accordingly (long or short).
• Candle Coloring
Finally, the script colors each candle according to how far the smoothed RSI is from a neutral baseline of 50:
Candles turn green when the RSI is sufficiently above 50, suggesting bullish momentum.
Candles turn red when the RSI is sufficiently below 50, indicating bearish momentum.
Candles turn orange when they are near the 50 level, reflecting a more neutral or transitional phase.
Traders can use these colored candles to quickly see when the RSI’s momentum has moved into overbought/oversold zones—or is shifting between bullish and bearish conditions—without needing to consult a separate oscillator window. The adaptive nature of the band calculations can help in spotting significant shifts in market sentiment and volatility.
Bradley SiderographThis indicator functions as a Planetary Barometer, bringing the Bradley-Siderograph directly onto your TradingView chart. Designed for tracking the algebraic sum of planetary aspects and declination values in relation to market movements, it analyzes sidereal potential, long-term and mid-term planetary aspects, and the declination factor to provide insight into potential shifts in mass psychology. The built-in gauges act like a barometer, visually measuring the intensity and range of the components.
As Donald Bradley states in Stock Market Prediction:
" The siderograph is nothing more than a time chart showing a wavy line, which represents the algebraic total of the declination factor, the long terms, and the middle terms. It can be computed for any period—past or future—for which an ephemeris is available. Every aspect, whether long or middle term, is assigned a theoretical value of 10 at its peak. The value of the declination factor is half the algebraic sum of the given declinations of Venus and Mars, with northern declination considered positive and southern declination negative. "
How the Bradley-Siderograph Works:
The Siderograph assigns positive and negative valencies based on the transits of inner and outer planets, categorized into long-term and mid-term aspects.
Each aspect (15° orb) is given a theoretical value, with the peak set at ±10. The approach and separation phases influence the weighting of each aspect leading up to its peak.
The sign of the valency depends on the type of aspect:
Squares and oppositions are assigned negative values
Trines and sextiles are assigned positive values
Conjunctions can be either positive or negative, depending on the planetary combination
Formula Used:
The Siderograph is computed as follows:
𝑃 = 𝑋 (𝐿 + 𝐷) + 𝑀
Where:
P = Sidereal Potential (final computed value)
X = Multiplier (to weight long-term aspects)
L = Long-term aspects (10 aspect combinations)
D = Declination factor (half the sum of Venus and Mars declinations)
M = Mid-term aspects
The long-term component (L + D) can be multiplied by a chosen factor (X) to emphasize its influence relative to the mid-term aspects.
How to Use the Indicator:
Once applied, the Siderograph line overlays on the chart, using the left-side scale for reference.
The indicator provides separate plots for:
Sidereal potential
Long-term aspects
Mid-term aspects
Declination factor
Each component can be toggled on or off for deeper analysis.
Gauges "provided by @faiyaz7283 library" display the high and low range for each curve, allowing quick identification of extreme values.
The indicator also marks the yearly high and low of the current year’s sidereal potential, providing a reference for when the market is trading above or below key levels. This feature was inspired by an observation made by Bradley in his book, which I wanted to incorporate here.
Users can fully customize the indicator by:
Switching between geocentric and heliocentric views.
Adjusting the orb of planetary transits to refine aspect sensitivity.
Multiplier (to weight long-term aspects)
Explore the Bradley-Siderograph and experiment with its settings.
Main Use Case
The Siderograph can be thought of as a psychological wind sock, gauging shifts in mass sentiment in response to planetary influences. Rather than forecasting market direction outright, it serves as an early warning system, signaling when conditions may be primed for changes in collective psychology.
As Donald Bradley notes in Stock Market Prediction:
" A limitation of the siderograph is that it cannot be construed as a forecast of secular trend. In statistical terminology, 'lines of regression' fitted to the market course and to the potential should not be expected to completely agree, for reasons obvious to everybody with keen business sense or commercial training. However, the siderograph may be depended upon to reward its analyst with foreknowledge of coming conditions in general, so that the non-psychological factors may be evaluated accordingly. By this, we mean that the potential will afford one with clues as to how the mass mind will 'take' the other mechanical or governmental vicissitudes affecting high finance. The siderograph may be thought of as a principle 'symptom' in diagnosing current market circumstances and as a sounding-board for prognoses concerning further developments. "
Planned Improvement:
While Bradley did not construct the Siderograph for direct forecasting, an enhancement to this indicator would be the ability to project each curve forward in time, providing a clearer view of how upcoming planetary aspects.
This indicator is being released as open source with the hope of further refining and expanding its capabilities—particularly in developing future plots that improve visualization and analysis. Contributions and feedback are encouraged to enhance its usability and advance the study of planetary influences in market behavior.
Credits & Acknowledgments:
Inspired by Donald Bradley and his work in Stock Market Prediction: The Planetary Barometer and How to Use It.
Built using Astrolib, developed by @BarefootJoey
Built using Gauges, developed by @faiyaz7283
[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.
__
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.
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.
Schwarzman Custom ORB with Box DisplayIndicator Overview
The Schwarzman Custom ORB (Opening Range Breakout) Indicator is a fully self-developed script designed for traders who utilize opening range breakout strategies. This indicator allows users to customize their ORB settings, apply them to historical price data, and visually connect multiple ORBs to analyze past performance. The goal is to provide traders with a tool to backtest and refine their breakout strategies based on historical ORB data.
How the Indicator Works
1️⃣ User-Defined ORB Settings
• The user selects a custom start time (hour and minute) for the ORB.
• The user defines a duration (e.g., 15 minutes, 30 minutes, etc.) for the ORB period.
• A timezone offset is included to adjust for different market sessions.
2️⃣ ORB High and Low Calculation
• The script records the highest and lowest prices within the selected ORB time window.
• The recorded values remain static after the ORB period ends, ensuring accurate range plotting.
3️⃣ Historical ORB Visualization
• Instead of only showing a single ORB for the current session, this indicator connects multiple ORBs across past data.
• This allows traders to visually analyze previous breakout performance.
• The plotted ORBs remain fixed and do not repaint, ensuring an accurate backtesting experience.
4️⃣ Stepline Visualization & Range Filling
• The high and low ORB levels are displayed using stepline plots to maintain clear horizontal levels.
• A shaded box is applied between the ORB high and low for better visualization.
Use Cases & Strategy Application
📌 Backtesting Historical ORBs – See how past ORBs performed under different market conditions.
📌 Custom ORB Settings – Adjust the start time and duration for different trading sessions.
📌 Multi-ORB Analysis – Connect ORBs over multiple trading days to study trends and breakouts.
📌 Breakout Strategy Optimization – Use the historical ORB connections to refine entry and exit points.
This indicator is particularly useful for day traders, scalpers, and breakout traders looking for a data-driven approach to trading.
Indicator Development & Transparency Statement
As a trader, I have tested various ORB (Opening Range Breakout) indicators available in the TradingView community. Through these experiences, I aimed to develop a version that best fits my own trading needs and strategy.
This script is a self-developed ORB tool, created from scratch while drawing inspiration from the concept of opening range breakouts, which is widely used in trading. Since I initially coded in Pine Script v4, I used ChatGPT to help refine and migrate the script to Pine Script v6 to ensure compatibility with the latest TradingView features. However, the core logic, structure, and customization were entirely designed and implemented based on my own approach.
I am making this indicator public not to violate any TradingView guidelines but to share my work with the trading community and provide a tool that can help others analyze ORB-based strategies. If there are any compliance concerns, I am open to adjusting the script accordingly, but I want to clarify that this is not a copy of any existing ORB script—it is a custom-built indicator tailored to my own trading preferences.
I appreciate the opportunity to contribute to the community and would welcome any specific feedback from TradingView regarding rule compliance.
Best regards,
Janko S. (Schwarzman)
Appeal to TradingView
Dear TradingView Team,
This script is 100% self-developed and does not copy or replicate any third-party code. It is a customized ORB tool designed for traders who wish to backtest and analyze opening range breakout strategies over multiple sessions. We kindly request specific clarification regarding which exact line(s) of code violate TradingView’s guidelines. If there are any compliance concerns, we are happy to adjust the script accordingly.
Please let us know the precise rules or community guidelines that were violated so we can make the necessary modifications.
🚀 Summary
✔ Fully Custom & Self-Developed – No copied or third-party code.
✔ Innovative Feature – Connects past ORBs for strategy backtesting.
✔ Transparent & Compliant – Requesting exact details on any potential rule violations.
Naive Bayes Candlestick Pattern Classifier v1.1 BETAAn intermezzo on why i made this script publication..
A : Candlestick Pattern took hours to backtest, why not using Machine Learning techniques?
B : Machine Learning, no that's gonna be really heavy bro!
A : Not really, because we use Naive Bayes.
B : The simplest, yet powerful machine learning algorithm to separate (a.k.a classify) multivariate data.
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Hello, everyone!
After deep research in extracting meaningful information from the market, I ended up building this powerful machine learning indicator based on the evolution of Bayesian Statistics. This indicator not only leverages the simplicity of Naive Bayes but also extends its application to candlestick pattern analysis, making it an invaluable tool for traders who are looking to enhance their technical analysis without spending countless hours manually backtesting each pattern on each market!.
What most interesting part is actually after learning all of likely useless methods like fibonacci, supply and demand, volume profile, etc. We always ended up back to basic like support and resistance and candlestick patterns, but with a slight twist on strategy algorithm design and statistical approach. Thus, the only reason why i made this, because i exactly know that you guys will ended up in this position as time goes by.
The essence of this indicator lies in its ability to automate the recognition and statistical evaluation of various candlestick patterns. Traditionally, traders have relied on visual inspection and manual backtesting to determine the effectiveness of patterns like Bullish Engulfing, Bearish Engulfing, Harami variations, Hammer formations, and even more complex multi-candle patterns such as Three White Soldiers, Three Black Crows, Dark Cloud Cover, and Piercing Pattern. However, these conventional methods are both time-consuming and prone to subjective bias.
To address these challenges, I employed Naive Bayes—a probabilistic classifier that, despite its simplicity, offers robust performance in various domains. Naive Bayes assumes that each feature is independent of the others given the class label, which, although a strong assumption, works remarkably well in practice, especially when the dataset is large like market data and the feature space is high-dimensional. In our case, each candlestick pattern acts as a feature that can be statistically evaluated based on its historical performance. The indicator calculates a probability that a given pattern will lead to a price reversal, by comparing the pattern’s close price to the highest or lowest price achieved in a lookahead window.
One of the standout features of this script is its flexibility. Each candlestick pattern is not only coded into the system but also comes with individual toggles to enable or disable them based on your trading strategy. This means you can choose to focus on single-candle patterns like Bullish Engulfing or more complex multi-candle formations such as Three White Soldiers, without modifying the core code. The built-in customization options allow you to adjust colors and labels for each pattern, giving you the freedom to tailor the visual output to your preference. This level of customization ensures that the indicator integrates seamlessly into your existing TradingView setup.
Moreover, the indicator isn’t just about pattern recognition—it also incorporates outcome-based learning. Every time a pattern is detected, it looks ahead a predefined number of bars to evaluate if the expected reversal actually materialized. This outcome is then stored in arrays, and over time, the script dynamically calculates the probability of success for each pattern. These probabilities are presented in a real-time updating table on your chart, which shows not only the percentage probability but also the count of historical occurrences. With this information at your fingertips, you can quickly gauge the reliability of each pattern in your chosen market and timeframe.
Another significant advantage of this approach is its speed and efficiency. While more complex machine learning models like neural networks might require heavy computational resources and longer training times, the Naive Bayes classifier in this script is lightweight, instantaneous and can be updated on the fly with each new bar. This real-time capability is essential for modern traders who need to make quick decisions in fast-paced markets.
Furthermore, by automating the process of backtesting, the indicator frees up your time to focus on other aspects of trading strategy development. Instead of manually analyzing hundreds or even thousands of candles, you can rely on the statistical power of Naive Bayes to provide you with insights on which patterns are most likely to result in profitable moves. This not only enhances your efficiency but also helps to eliminate the cognitive biases that often plague manual analysis.
In summary, this indicator represents a fusion of traditional candlestick analysis with modern machine learning techniques. It harnesses the simplicity and effectiveness of Naive Bayes to deliver a dynamic, real-time evaluation of various candlestick patterns. Whether you are a seasoned trader looking to refine your technical analysis or a beginner eager to understand market dynamics, this tool offers a powerful, customizable, and efficient solution. Welcome to a new era where advanced statistical methods meet practical trading insights—happy trading and may your patterns always be in your favor!
Note : On this current released beta version, you must manually adjust reversal percentage move based on each market. Further updates may include automated best range detection and probability.
Best Buffett Ratio w/ Std-Dev Offset + Conditional PlotSummary:
This script provides a visually clear way to track the so-called “Buffett Ratio,”
a popular market valuation gauge which compares the total US stock market cap
to the country’s GDP. In addition, it plots a “hardcoded” long-term trend line,
along with fixed standard-deviation bands (in log space), and uses background colors
to signal potentially overvalued or undervalued zones.
What Is the Buffett Ratio?
Often credited to Warren Buffett, the Buffett Ratio (or Buffett Indicator) measures:
(Total US Stock Market Capitalization) / (US GDP)
• A higher ratio typically means equities are more expensive relative to the size of the economy.
• A lower ratio suggests equities may be more attractively valued compared to GDP.
Historically, the ratio has tended to drift upward over many decades,
as the US economy and stock markets grow, but it still oscillates around some trend over time.
How to Use
1) Add to Chart:
- In TradingView, simply apply the indicator (it internally fetches CRSPTM1 & GDP data).
2) Tweak Inputs:
- Log Offset for 1σ: Adjust how wide the ±1σ/±2σ bands appear around the trend.
- Anchor Points: Edit startYear , endYear , startRatio , endRatio
if you want a different slope or different “fair value” anchors.
3) Interpretation:
- If the indicator is above +2σ (red line) , it’s historically “very expensive,”
often leading to lower future returns over the long term.
- If it’s below –2σ (green line) , it’s historically “deep undervaluation,”
often pointing to better future returns over time.
- The intermediate zones show degrees of mild over- or undervaluation.
How This Script Works
1) Buffett Ratio Calculation:
- The script requests data from TradingView’s built-in CRSPTM1 index (total US market cap).
- It also requests US GDP data via request.economic("US", "GDP") .
- If GDP data is missing, the ratio becomes na on that bar.
2) Hardcoded Trend Line:
- Rather than a rolling average, the script uses two “anchors” (e.g. 1950 → 0.30 ratio, 2024 → 1.25 ratio)
and solves for a single log-growth rate to produce a steady upward slope.
3) Fixed Standard Deviations in Log Space:
- The script takes the log of the trend line, then applies a fixed offset for ±1σ and ±2σ,
creating proportional bands that do not “expand/contract” from a rolling window.
4) Conditional Plotting:
- The script only begins plotting once the Buffett Ratio actually has data (around 2011).
5) Color-Coded Zones:
- Above +2σ: red background (historically very expensive)
- Between +1σ and +2σ: yellow background (moderately expensive)
- Between –1σ and +1σ: no background color (around normal)
- Between –2σ and –1σ: aqua background (moderately undervalued)
- Below –2σ: green background (historically deep undervaluation)
Final Notes
• Data Limitations: US GDP data and CRSPTM1 only go back so far, so this starts around 2011.
• Long-Term vs. Short-Term: Best viewed on monthly/quarterly charts and interpreted over years.
• Tuning: If you believe structural changes have shifted the ratio’s fair slope,
adjust the code’s anchors or log offsets.
Enjoy, and use responsibly!
STRX - Correlation DominationThis indicator displays the correlation among three selected assets (for example, Gold, Dollar Index, and Nasdaq) on a custom timeframe. A table positioned at the top-right corner of the chart lets you quickly see the correlation between:
Asset 1 vs Asset 2
Asset 1 vs Asset 3
Asset 2 vs Asset 3
Correlations are calculated using the Pearson correlation function (ta.correlation). If the correlation is greater than or equal to 0.4, the value appears in green (strong positive correlation). If it is less than or equal to -0.4, it appears in red (strong negative correlation). Otherwise, it is displayed in yellow (weak correlation).
Multi-asset and multi-timeframe: Compare up to three instruments at once on your chosen timeframe.
Customizable period: Use the “Correlation Period” setting to adjust the correlation calculation window.
Clear table format: The results are immediately visible in an easy-to-read table.
Disclaimer: This script is provided solely for educational and informational purposes. It does not constitute a recommendation or an invitation to invest. Use it as an additional resource and always conduct thorough market analysis before opening any trading positions. Past performance does not guarantee future results.
[blackcat] L3 Counter Peacock Spread█ OVERVIEW
The script titled " L3 Counter Peacock Spread" is an indicator designed for use in TradingView. It calculates and plots various moving averages, K lines derived from these moving averages, additional simple moving averages (SMAs), weighted moving averages (WMAs), and other technical indicators like slope calculations. The primary function of the script is to provide a comprehensive set of visual tools that traders can use to identify trends, potential support/resistance levels, and crossover signals.
█ LOGICAL FRAMEWORK
Input Parameters:
There are no explicit input parameters defined; all variables are hardcoded or calculated within the script.
Calculations:
• Moving Averages: Calculates Simple Moving Averages (SMA) using ta.sma.
• Slope Calculation: Computes the slope of a given series over a specified period using linear regression (ta.linreg).
• K Lines: Defines multiple exponentially adjusted SMAs based on a 30-period MA and a 1-period MA.
• Weighted Moving Average (WMA): Custom function to compute WMAs by iterating through price data points.
• Other Indicators: Includes Exponential Moving Average (EMA) for momentum calculation.
Plotting:
Various elements such as MAs, K lines, conditional bands, additional SMAs, and WMAs are plotted on the chart overlaying the main price action.
No loops control the behavior beyond those used in custom functions for calculating WMAs. Conditional statements determine the coloring of certain plot lines based on specific criteria.
█ CUSTOM FUNCTIONS
calculate_slope(src, length) :
• Purpose: To calculate the slope of a time-series data point over a specified number of periods.
• Functionality: Uses linear regression to find the current and previous slopes and computes their difference scaled by the timeframe multiplier.
• Parameters:
– src: Source of the input data (e.g., closing prices).
– length: Periodicity of the linreg calculation.
• Return Value: Computed slope value.
calculate_ma(source, length) :
• Purpose: To calculate the Simple Moving Average (SMA) of a given source over a specified period.
• Functionality: Utilizes TradingView’s built-in ta.sma function.
• Parameters:
– source: Input data series (e.g., closing prices).
– length: Number of bars considered for the SMA calculation.
• Return Value: Calculated SMA value.
calculate_k_lines(ma30, ma1) :
• Purpose: Generates multiple exponentially adjusted versions of a 30-period MA relative to a 1-period MA.
• Functionality: Multiplies the 30-period MA by coefficients ranging from 1.1 to 3 and subtracts multiples of the 1-period MA accordingly.
• Parameters:
– ma30: 30-period Simple Moving Average.
– ma1: 1-period Simple Moving Average.
• Return Value: Returns an array containing ten different \u2003\u2022 "K line" values.
calculate_wma(source, length) :
• Purpose: Computes the Weighted Moving Average (WMA) of a provided series over a defined period.
• Functionality: Iterates backward through the last 'n' bars, weights each bar according to its position, sums them up, and divides by the total weight.
• Parameters:
– source: Price series to average.
– length: Length of the lookback window.
• Return Value: Calculated WMA value.
█ KEY POINTS AND TECHNIQUES
• Advanced Pine Script Features: Utilization of custom functions for encapsulating complex logic, leveraging TradingView’s library functions (ta.sma, ta.linreg, ta.ema) for efficient computations.
• Optimization Techniques: Efficient computation of K lines via pre-calculated components (multiples of MA30 and MA1). Use of arrays to store intermediate results which simplifies plotting.
• Best Practices: Clear separation between calculation and visualization sections enhances readability and maintainability. Usage of color.new() allows dynamic adjustments without hardcoding colors directly into plot commands.
• Unique Approaches: Introduction of K lines provides an alternative representation of trend strength compared to traditional MAs. Implementation of conditional band coloring adds real-time context to existing visual cues.
█ EXTENDED KNOWLEDGE AND APPLICATIONS
Potential Modifications/Extensions:
• Adding more user-defined inputs for lengths of MAs, K lines, etc., would make the script more flexible.
• Incorporating alert conditions based on crossovers between key lines could enhance automated trading strategies.
Application Scenarios:
• Useful for both intraday and swing trading due to the combination of short-term and long-term MAs along with trend analysis via slopes and K lines.
• Can be integrated into larger systems combining this indicator with others like oscillators or volume-based metrics.
Related Concepts:
• Understanding how linear regression works internally aids in grasping the slope calculation.
• Familiarity with WMA versus SMA helps appreciate why different types of averaging might be necessary depending on market dynamics.
• Knowledge of candlestick patterns can complement insights gained from this indicator.
MFS-3 Bars Pattern Strategy3 Bar Pattern Strategy
Detects an Ignite Candle followed by a Pullback Candle followed by a Confirmation Candle.
A Box will be drawn around the setup and three arrows will identify I, P, C (Ignite, Pullback, Confirmation) the setup.
The strategy will calculate a Stop Loss below the Low Price of the Ignite candle and a Take Profit at 2 times the Stop Loss giving a Risk to Reward Ratio of 1:2.
Extra conditions are included to reduce false triggers:
- A down trend must be detected using 3 SMA (Long, Medium, Short) that should be aligned from Long to Short one above the other.
- The Ignite Candle's body must be BELOW the Short SMA
An input form is available to adjust some strategy parameters.
Performance Note
----------------------
Trading conditions are very strict, so most of the time, no signals will be detected in the Strategy window.
This strategy should only be one of many strategies used for trade setups.
Hope you enjoy it.
Kalman PredictorThe **Kalman Predictor** indicator is a powerful tool designed for traders looking to enhance their market analysis by smoothing price data and projecting future price movements. This script implements a Kalman filter, a statistical method for noise reduction, to dynamically estimate price trends and velocity. Combined with ATR-based confidence bands, it provides actionable insights into potential price movement, while offering clear trend and momentum visualization.
---
#### **Key Features**:
1. **Kalman Filter Smoothing**:
- Dynamically estimates the current price state and velocity to filter out market noise.
- Projects three future price levels (`Next Bar`, `Next +2`, `Next +3`) based on velocity.
2. **Dynamic Confidence Bands**:
- Confidence bands are calculated using ATR (Average True Range) to reflect market volatility.
- Visualizes potential price deviation from projected levels.
3. **Trend Visualization**:
- Color-coded prediction dots:
- **Green**: Indicates an upward trend (positive velocity).
- **Red**: Indicates a downward trend (negative velocity).
- Dynamically updated label displaying the current trend and velocity value.
4. **User Customization**:
- Inputs to adjust the process and measurement noise for the Kalman filter (`q` and `r`).
- Configurable ATR multiplier for confidence bands.
- Toggleable trend label with adjustable positioning.
---
#### **How It Works**:
1. **Kalman Filter Core**:
- The Kalman filter continuously updates the estimated price state and velocity based on real-time price changes.
- Projections are based on the current price trend (velocity) and extend into the future (Next Bar, +2, +3).
2. **Confidence Bands**:
- Calculated using ATR to provide a dynamic range around the projected future prices.
- Indicates potential volatility and helps traders assess risk-reward scenarios.
3. **Trend Label**:
- Updates dynamically on the last bar to show:
- Current trend direction (Up/Down).
- Velocity value, providing insight into the expected magnitude of the price movement.
---
#### **How to Use**:
- **Trend Analysis**:
- Observe the direction and spacing of the prediction dots relative to current candles.
- Larger spacing indicates a potential strong move, while clustering suggests consolidation.
- **Risk Management**:
- Use the confidence bands to gauge potential price volatility and set stop-loss or take-profit levels accordingly.
- **Pullback Detection**:
- Look for flattening or clustering of dots during trends as a signal of potential pullbacks or reversals.
---
#### **Customizable Inputs**:
- **Kalman Filter Parameters**:
- `lookback`: Adjusts the smoothing window.
- `q`: Process noise (higher values make the filter more reactive to changes).
- `r`: Measurement noise (controls sensitivity to price deviations).
- **Confidence Bands**:
- `band_multiplier`: Multiplies ATR to define the range of confidence bands.
- **Visualization**:
- `show_label`: Option to toggle the trend label.
- `label_offset`: Adjusts the label’s distance from the price for better visibility.
---
#### **Examples of Use**:
- **Scalping**: Use on lower timeframes (e.g., 1-minute, 5-minute) to detect short-term price trends and reversals.
- **Swing Trading**: Identify pullbacks or continuations on higher timeframes (e.g., 4-hour, daily) by observing the prediction dots and confidence bands.
- **Risk Assessment**: Confidence bands help visualize potential price volatility, aiding in the placement of stops and targets.
---
#### **Notes for Traders**:
- The **Kalman Predictor** does not predict the future with certainty but provides a statistically informed estimate of price movement.
- Confidence bands are based on historical volatility and should be used as guidelines, not guarantees.
- Always combine this tool with other analysis techniques for optimal results.
---
This script is open-source, and the Kalman filter logic has been implemented uniquely to integrate noise reduction with dynamic confidence band visualization. If you find this indicator useful, feel free to share your feedback and experiences!
---
#### **Credits**:
This script was developed leveraging the statistical principles of Kalman filtering and is entirely original. It incorporates ATR for dynamic confidence band calculations to enhance trader usability and market adaptability.
Volatility-Adjusted Trend Deviation Statistics (C-Ratios)The Pine Script logic provided generates and displays a table with key information derived from VWMA, EMA, and ATR-based "C Ratios," alongside stochastic oscillators, correlation coefficients, Z-scores, and bias indicators. Here’s an explanation of the logic and what the output in the table informs:
Key Calculations and Their Purpose
VWMA and EMA (Smoothing Lengths):
Multiple EMAs are calculated using VWMA as the source, with lengths spanning short-term (13) to long-term (233).
These EMAs provide a hierarchy of smoothed price levels to assess trends over various time horizons.
ATR-Based "C Ratios":
The C Ratios measure deviations of smoothed prices (a_1 to a_7) from the source price relative to ATR at corresponding lengths.
These values normalize deviations, giving insight into the price's relative movement strength and direction over various periods.
Stochastic Oscillator for C Ratios:
Calculates normalized stochastic values for each C Ratio to assess overbought/oversold conditions dynamically over a rolling window.
Helps identify short-term momentum trends within the broader context of C Ratios.
Displays the average stochastic value derived from all C Ratios.
Text: Shows overbought/oversold conditions (Overbought, Oversold, or ---).
Color: Green for strong upward momentum, red for downward, and white for neutral.
Weighted and Mean C Ratio:
The script computes both an arithmetic mean (c_mean) and a weighted mean (c_mean_w) for all C Ratios.
Weighted mean emphasizes short-term values using predefined weights.
Trend Bias and Reversal Detection:
The script calculates Z-scores for c_mean to identify statistically significant deviations.
It combines Z-scores and weighted C Ratio values to determine:
Bias (Bullish/Bearish based on Z-score thresholds and mean values).
Reversals (Based on relative positioning and how the weighted c_mean and un-weighted C_mean move. ).
Correlation Coefficient:
Correlation of mean C Ratios (c_mean) with bar indices over the short-term length (sl) assesses the strength and direction of trend consistency.
Table Output and Its Meaning
Stochastic Strength:
Long-term Correlation:
List of Lengths: Define the list of lengths for EMA and ATR explicitly (e.g., ).
Calculate Mean C Ratios: For each length in the list, calculate the mean C Ratio
Average these values over the entire dataset.
Store Lengths and Mean C Ratios: Maintain arrays for lengths and their corresponding mean C Ratios.
Correlation: compute the Pearson correlation between the list of lengths and the mean C Ratios.
Text: Indicates Uptrend, Downtrend, or neutral (---).
Color: Green for positive (uptrend), red for negative (downtrend), and white for neutral.
Z-Score Bias:
Assesses the statistical deviation of C Ratios from their historical mean.
Text: Bullish Bias, Bearish Bias, or --- (neutral).
Color: Green or red based on the direction and significance of the Z-score.
C-Ratio Mean:
Displays the weighted average C Ratio (c_mean_w) or a reversal condition.
Text: If no reversal is detected, shows c_mean_w; otherwise, a reversal condition (Bullish Reversal, Bearish Reversal).
Color: Indicates the strength and direction of the bias or reversal.
Practical Insights
Trend Identification: Correlation coefficients, Z-scores, and stochastic values collectively highlight whether the market is trending and the trend's direction.
Momentum and Volatility: Stochastic and ATR-normalized C Ratios provide insights into the momentum and price movement consistency across different timeframes.
Bias and Reversal Detection: The script highlights potential shifts in market sentiment or direction (bias or reversal) using statistical measures.
Customization: Users can toggle plots and analyze specific EMA lengths or focus on combined metrics like the weighted C Ratio.
Bayesian Price Projection Model [Pinescriptlabs]📊 Dynamic Price Projection Algorithm 📈
This algorithm combines **statistical calculations**, **technical analysis**, and **Bayesian theory** to forecast a future price while providing **uncertainty ranges** that represent upper and lower bounds. The calculations are designed to adjust projections by considering market **trends**, **volatility**, and the historical probabilities of reaching new highs or lows.
Here’s how it works:
🚀 Future Price Projection
A dynamic calculation estimates the future price based on three key elements:
1. **Trend**: Defines whether the market is predisposed to move up or down.
2. **Volatility**: Quantifies the magnitude of the expected change based on historical fluctuations.
3. **Time Factor**: Uses the logarithm of the projected period (`proyeccion_dias`) to adjust how time impacts the estimate.
🧠 **Bayesian Probabilistic Adjustment**
- Conditional probabilities are calculated using **Bayes' formula**:
\
This models future events using conditional information:
- **Probability of reaching a new all-time high** if the price is trending upward.
- **Probability of reaching a new all-time low** if the price is trending downward.
- These probabilities refine the future price estimate by considering:
- **Higher volatility** increases the likelihood of hitting extreme levels (highs/lows).
- **Market trends** influence the expected price movement direction.
🌟 **Volatility Calculation**
- Volatility is measured using the **ATR (Average True Range)** indicator with a 14-period window. This reflects the average amplitude of price fluctuations.
- To express volatility as a percentage, the ATR is normalized by dividing it by the closing price and multiplying it by 200.
- Volatility is then categorized into descriptive levels (e.g., **Very Low**, **Low**, **Moderate**, etc.) for better interpretation.
---
🎯 **Deviation Limits (Upper and Lower)**
- The upper and lower limits form a **projected range** around the estimated future price, providing a framework for uncertainty.
- These limits are calculated by adjusting the ATR using:
- A user-defined **multiplier** (`factor_desviacion`).
- **Bayesian probabilities** calculated earlier.
- The **square root of the projected period** (`proyeccion_dias`), incorporating the principle that uncertainty grows over time.
🔍 **Interpreting the Model**
This can be seen as a **dynamic probabilistic model** that:
- Combines **technical analysis** (trends and ATR).
- Refines probabilities using **Bayesian theory**.
- Provides a **visual projection range** to help you understand potential future price movements and associated uncertainties.
⚡ Whether you're analyzing **volatile markets** or confirming **bullish/bearish scenarios**, this tool equips you with a robust, data-driven approach! 🚀
Español :
📊 Algoritmo de Proyección de Precio Dinámico 📈
Este algoritmo combina **cálculos estadísticos**, **análisis técnico** y **la teoría de Bayes** para proyectar un precio futuro, junto con rangos de **incertidumbre** que representan los límites superior e inferior. Los cálculos están diseñados para ajustar las proyecciones considerando la **tendencia del mercado**, **volatilidad** y las probabilidades históricas de alcanzar nuevos máximos o mínimos.
Aquí se explica su funcionamiento:
🚀 **Proyección de Precio Futuro**
Se realiza un cálculo dinámico del precio futuro estimado basado en tres elementos clave:
1. **Tendencia**: Define si el mercado tiene predisposición a subir o bajar.
2. **Volatilidad**: Determina la magnitud del cambio esperado en función de las fluctuaciones históricas.
3. **Factor de Tiempo**: Usa el logaritmo del período proyectado (`proyeccion_dias`) para ajustar cómo el tiempo afecta la estimación.
🧠 **Ajuste Probabilístico con la Teoría de Bayes**
- Se calculan probabilidades condicionales mediante la fórmula de **Bayes**:
\
Esto permite modelar eventos futuros considerando información condicional:
- **Probabilidad de alcanzar un nuevo máximo histórico** si el precio sube.
- **Probabilidad de alcanzar un nuevo mínimo histórico** si el precio baja.
- Estas probabilidades ajustan la estimación del precio futuro considerando:
- **Mayor volatilidad** aumenta la probabilidad de alcanzar niveles extremos (máximos/mínimos).
- **La tendencia del mercado** afecta la dirección esperada del movimiento del precio.
🌟 **Cálculo de Volatilidad**
- La volatilidad se mide usando el indicador **ATR (Average True Range)** con un período de 14 velas. Este indicador refleja la amplitud promedio de las fluctuaciones del precio.
- Para obtener un valor porcentual, el ATR se normaliza dividiéndolo por el precio de cierre y multiplicándolo por 200.
- Además, se clasifica esta volatilidad en categorías descriptivas (e.g., **Muy Baja**, **Baja**, **Moderada**, etc.) para facilitar su interpretación.
🎯 **Límites de Desviación (Superior e Inferior)**
- Los límites superior e inferior representan un **rango proyectado** en torno al precio futuro estimado, proporcionando un marco para la incertidumbre.
- Estos límites se calculan ajustando el ATR según:
- Un **multiplicador** definido por el usuario (`factor_desviacion`).
- Las **probabilidades condicionales** calculadas previamente.
- La **raíz cuadrada del período proyectado** (`proyeccion_dias`), lo que incorpora el principio de que la incertidumbre aumenta con el tiempo.
---
🔍 **Interpretación del Modelo**
Este modelo se puede interpretar como un **modelo probabilístico dinámico** que:
- Integra **análisis técnico** (tendencias y ATR).
- Ajusta probabilidades utilizando **la teoría de Bayes**.
- Proporciona un **rango de proyección visual** para ayudarte a entender los posibles movimientos futuros del precio y su incertidumbre.
⚡ Ya sea que estés analizando **mercados volátiles** o confirmando **escenarios alcistas/bajistas**, ¡esta herramienta te ofrece un enfoque robusto y basado en datos! 🚀
Global Index Spread RSI StrategyThis strategy leverages the relative strength index (RSI) to monitor the price spread between a global benchmark index (such as AMEX) and the currently opened asset in the chart window. By calculating the spread between these two, the strategy uses RSI to identify oversold and overbought conditions to trigger buy and sell signals.
Key Components:
Global Benchmark Index: The strategy compares the current asset with a predefined global index (e.g., AMEX) to measure relative performance. The choice of a global benchmark allows the trader to analyze the current asset's movement in the context of broader market trends.
Spread Calculation:
The spread is calculated as the percentage difference between the current asset's closing price and the global benchmark index's closing price:
Spread=Current Asset Close−Global Index CloseGlobal Index Close×100
Spread=Global Index CloseCurrent Asset Close−Global Index Close×100
This metric provides a measure of how the current asset is performing relative to the global index. A positive spread indicates the asset is outperforming the benchmark, while a negative spread signals underperformance.
RSI of the Spread: The RSI is then calculated on the spread values. The RSI is a momentum oscillator that ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions in asset prices. An RSI below 30 is considered oversold, indicating a potential buying opportunity, while an RSI above 70 is overbought, suggesting that the asset may be due for a pullback.
Strategy Logic:
Entry Condition: The strategy enters a long position when the RSI of the spread falls below the oversold threshold (default 30). This suggests that the asset may have been oversold relative to the global benchmark and might be due for a reversal.
Exit Condition: The strategy exits the long position when the RSI of the spread rises above the overbought threshold (default 70), indicating that the asset may have become overbought and a price correction is likely.
Visual Reference:
The RSI of the spread is plotted on the chart for visual reference, making it easier for traders to monitor the relative strength of the asset in relation to the global benchmark.
Overbought and oversold levels are also drawn as horizontal reference lines (70 and 30), along with a neutral level at 50 to show market equilibrium.
Theoretical Basis:
The strategy is built on the mean reversion principle, which suggests that asset prices tend to revert to a long-term average over time. When prices move too far from this mean—either being overbought or oversold—they are likely to correct back toward equilibrium. By using RSI to identify these extremes, the strategy aims to profit from price reversals.
Mean Reversion: According to financial theory, asset prices oscillate around a long-term average, and any extreme deviation (overbought or oversold conditions) presents opportunities for price corrections (Poterba & Summers, 1988).
Momentum Indicators (RSI): The RSI is widely used in technical analysis to measure the momentum of an asset. Its application to the spread between the asset and a global benchmark allows for a more nuanced view of relative performance and potential turning points in the asset's price trajectory.
Practical Application:
This strategy works best in markets where relative strength is a key factor in decision-making, such as in equity indices, commodities, or forex markets. By assessing the performance of the asset relative to a global benchmark and utilizing RSI to identify extremes in price movements, the strategy helps traders to make more informed decisions based on potential mean reversion points.
While the "Global Index Spread RSI Strategy" offers a method for identifying potential price reversals based on relative strength and oversold/overbought conditions, it is important to recognize that no strategy is foolproof. The strategy assumes that the historical relationship between the asset and the global benchmark will hold in the future, but financial markets are subject to a wide array of unpredictable factors that can lead to sudden changes in price behavior.
Risk of False Signals:
The strategy relies heavily on the RSI to trigger buy and sell signals. However, like any momentum-based indicator, RSI can generate false signals, particularly in highly volatile or trending markets. In such conditions, the strategy may enter positions too early or exit too late, leading to potential losses.
Market Context:
The strategy may not account for macroeconomic events, news, or other market forces that could cause sudden shifts in asset prices. External factors, such as geopolitical developments, monetary policy changes, or financial crises, can cause a divergence between the asset and the global benchmark, leading to incorrect conclusions from the strategy.
Overfitting Risk:
As with any strategy that uses historical data to make decisions, there is a risk of overfitting the model to past performance. This could result in a strategy that works well on historical data but performs poorly in live trading conditions due to changes in market dynamics.
Execution Risks:
The strategy does not account for slippage, transaction costs, or liquidity issues, which can impact the execution of trades in real-market conditions. In fast-moving markets, prices may move significantly between order placement and execution, leading to worse-than-expected entry or exit prices.
No Guarantee of Profit:
Past performance is not necessarily indicative of future results. The strategy should be used with caution, and risk management techniques (such as stop losses and position sizing) should always be implemented to protect against significant losses.
Traders should thoroughly test and adapt the strategy in a simulated environment before applying it to live trades, and consider seeking professional advice to ensure that their trading activities align with their risk tolerance and financial goals.
References:
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
[ AlgoChart ] - Compare MarketIndicator Description:
This indicator allows you to display a second asset, selectable from the input panel, in a separate window. Plotted on the same time scale as the first asset but with a distinct price scale, the indicator enables analysis of the relationships and relative movements of two financial instruments. It’s an ideal tool for understanding whether two assets move in a correlated or divergent manner.
Key Features:
Multi-Asset Comparison: Display two assets simultaneously to compare their trends.
Custom Scale: Each asset uses its own price scale, making comparative analysis easier.
Intuitive Interface: Easily select the second asset through the input panel.
Operational Applications:
Spread Trading: Identify optimal moments to execute spread trades when two highly correlated instruments move in opposite directions.
Supply & Demand: Pinpoint zones of interest on both assets, increasing the validity of support and resistance areas.
Exposure Reduction: Monitor instruments that move similarly to avoid exposing the portfolio in identical directions, thereby reducing the risk of double losses.
Additional Features:
Candle Color Change: When a directional divergence occurs between the two assets, the candles change color to highlight the event.
Customizable Notifications: Receive instant alerts when a divergence occurs, allowing you to act promptly.
Ido strategy RSI Oversold with MACD Buy Signal Indicator
This indicator combines the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) to help identify potential buy signals based on oversold conditions and trend reversals. This script is designed for traders looking to identify entry points when an asset is likely undervalued (oversold) and showing bullish momentum.
How It Works
RSI Oversold Detection: The RSI measures the speed and change of price movements. This indicator flags when the RSI falls below 30, signaling that the asset may be oversold. The user can customize the RSI lookback period and the timeframe within which oversold conditions are considered relevant.
MACD Crossover: The MACD line crossing above the Signal line often indicates a shift to bullish momentum. In this script, a buy signal is generated when a MACD bullish crossover occurs after an RSI oversold condition has been met within a user-defined lookback window.
Buy Signal: A green triangle appears below the price chart each time both conditions are met—when the RSI has recently been in oversold territory and the MACD line crosses above the Signal line. This signal suggests that the asset may be positioned for a potential upward trend, providing a visual cue for entry points.
Customizable Settings
RSI Settings: Adjust the RSI source and period length.
MACD Settings: Customize the fast, slow, and signal lengths of the MACD to suit different market conditions.
Lookback Period: Define how many bars back to check for an RSI oversold condition before confirming a MACD crossover.
Visual Elements
Oversold Background Color: The background on the price chart is shaded red whenever the RSI is below 30.
Buy Signal: A green triangle is displayed on the chart to indicate a potential entry point when both conditions are met.
Alerts
This indicator includes optional alerts, allowing traders to receive notifications whenever the conditions for a buy signal are met, making it easier to monitor multiple assets and stay informed of trading opportunities.
This indicator is ideal for traders using a combination of momentum and trend reversal strategies, especially in volatile markets where oversold conditions often precede a trend change.
Customizable BTC Seasonality StrategyThis strategy leverages intraday seasonality effects in Bitcoin, specifically targeting hours of statistically significant returns during periods when traditional financial markets are closed. Padysak and Vojtko (2022) demonstrate that Bitcoin exhibits higher-than-average returns from 21:00 UTC to 23:00 UTC, a period in which all major global exchanges, such as the New York Stock Exchange (NYSE), Tokyo Stock Exchange, and London Stock Exchange, are closed. The absence of competing trading activity from traditional markets during these hours appears to contribute to these statistically significant returns.
The strategy proceeds as follows:
Entry Time: A long position in Bitcoin is opened at a user-specified time, which defaults to 21:00 UTC, aligning with the beginning of the identified high-return window.
Holding Period: The position is held for two hours, capturing the positive returns typically observed during this period.
Exit Time: The position is closed at a user-defined time, defaulting to 23:00 UTC, allowing the strategy to exit as the favorable period concludes.
This simple seasonality strategy aims to achieve a 33% annualized return with a notably reduced volatility of 20.93% and maximum drawdown of -22.45%. The results suggest that investing only during these high-return hours is more stable and less risky than a passive holding strategy (Padysak & Vojtko, 2022).
References
Padysak, M., & Vojtko, R. (2022). Seasonality, Trend-following, and Mean reversion in Bitcoin.
Dema Percentile Standard DeviationDema Percentile Standard Deviation
The Dema Percentile Standard Deviation indicator is a robust tool designed to identify and follow trends in financial markets.
How it works?
This code is straightforward and simple:
The price is smoothed using a DEMA (Double Exponential Moving Average).
Percentiles are then calculated on that DEMA.
When the closing price is below the lower percentile, it signals a potential short.
When the closing price is above the upper percentile and the Standard Deviation of the lower percentile, it signals a potential long.
Settings
Dema/Percentile/SD/EMA Length's: Defines the period over which calculations are made.
Dema Source: The source of the price data used in calculations.
Percentiles: Selects the type of percentile used in calculations (options include 60/40, 60/45, 55/40, 55/45). In these settings, 60 and 55 determine percentile for long signals, while 45 and 40 determine percentile for short signals.
Features
Fully Customizable
Fully Customizable: Customize colors to display for long/short signals.
Display Options: Choose to show long/short signals as a background color, as a line on price action, or as trend momentum in a separate window.
EMA for Confluence: An EMA can be used for early entries/exits for added signal confirmation, but it may introduce noise—use with caution!
Built-in Alerts.
Indicator on Diffrent Assets
INDEX:BTCUSD 1D Chart (6 high 56 27 60/45 14)
CRYPTO:SOLUSD 1D Chart (24 open 31 20 60/40 14)
CRYPTO:RUNEUSD 1D Chart (10 close 56 14 60/40 14)
Remember no indicator would on all assets with default setting so FAFO with setting to get your desired signal.
Relative Measured Volatility (RMV) – Spot Tight Entry ZonesTitle: Relative Measured Volatility (RMV) – Spot Tight Entry Zones
Introduction
The Relative Measured Volatility (RMV) indicator is designed to highlight tight price consolidation zones , making it an ideal tool for traders seeking optimal entry points before potential breakouts. By focusing on tightness rather than general volatility, RMV offers traders a practical way to detect consolidation phases that often precede significant market moves.
How RMV Works
The RMV calculates short-term tightness by averaging three ATR (Average True Range) values over different lookback periods and then normalizing them within a specified lookback window. The result is a percentage-based scale from 0 to 100, indicating how tight the current price range is compared to recent history.
Here’s the breakdown:
Three ATR values are computed using user-defined short lookback periods to represent short-term price movements. An average of the ATRs provides a smoothed measure of current tightness. The RMV normalizes this average against the highest and lowest values over the defined lookback period, scaling it from 0 to 100.
This approach helps traders identify consolidation zones that are more likely to lead to breakouts.
Key Features of RMV
Multi-Period ATR Calculation : Uses three ATR values to effectively capture market tightness over the short term. Normalization : Converts the tightness measure to a 0-100 scale for easy interpretation. Dynamic Histogram and Background Colors : The RMV indicator uses a color-coded system for clarity.
How to Use the RMV Indicator
Identify Tight Consolidation Zones:
a - RMV values between 0-10 indicate very tight price ranges, making this the most optimal zone for potential entries before breakouts.
b - RMV values between 11-20 suggest moderate tightness, still favorable for entries.
Monitor Potential Breakout Areas:
As RMV moves from 21-30 , tightness reduces, signaling expanding volatility that may require wider stops or more flexible entry strategies.
Adjust Trading Strategies:
Use RMV values to identify tight zones for entering trades, especially in trending markets or at key support/resistance levels.
Customize the Indicator:
a - Adjust the short-term ATR lookback periods to control sensitivity.
b - Modify the lookback period to match your trading horizon, whether short-term or long-term.
Color-Coding Guide for RMV
ibb.co
How to Add RMV to Your Chart
Open your chart on TradingView.
Go to the “Indicators” section.
Search for "Relative Measured Volatility (RMV)" in the Community Scripts section.
Click on the indicator to add it to your chart.
Customize the input parameters to fit your trading strategy.
Input Parameters
Lookback Period : Defines the period over which tightness is measured and normalized.
Short-term ATR Lookbacks (1, 2, 3) : Control sensitivity to short-term tightness.
Histogram Threshold : Sets the threshold for differentiating between bright (tight) and dim (less tight) histogram colors.
Conclusion
The Relative Measured Volatility (RMV) is a versatile tool designed to help traders identify tight entry zones by focusing on market consolidation. By highlighting narrow price ranges, the RMV guides traders toward potential breakout setups while providing clear visual cues for better decision-making. Add RMV to your trading toolkit today and enhance your ability to identify optimal entry points!
4AM-5AM BRT HighlighterThe 4AM-5AM BRT Highlighter is a simple yet effective tool designed to visually mark your preferred trading time on the chart. It highlights the period between 4:00 AM and 5:00 AM Brazilian Time (BRT/UTC-3) by default, helping you stay focused and aware of your prime trading window.
Key Features:
Clear Visual Highlight: Colors the background of your chart during the chosen timeframe, making it easy to see when your trading session starts and ends.
Customizable Colors: Easily adjust the highlight color and transparency to suit your visual preferences.
Accurate Time Conversion: Automatically accounts for Brazilian Time (BRT), ensuring the highlight appears correctly no matter your chart’s default timezone.
Whether you're trading currencies, metals, indexes, or cryptocurrencies, this indicator helps you maintain focus during your dedicated trading hour by clearly marking your active period on the chart.
Macro Timeframes with Opening PriceDescription: Macro Timeframe Horizontal Line Indicator
This indicator highlights macro periods on the chart by drawing a horizontal line at the opening price of each macro period. The macro timeframe is defined as the last 10 minutes of an hour (from :50 to :00) and the first 10 minutes of the following hour (from :00 to :10).
A horizontal black line is plotted at the opening price of the macro period, starting at :50 and extending through the duration of the macro window. However, you can customize it however you see fit.
The background of the macro period is highlighted with a customizable color to visually distinguish the timeframe.
The horizontal line updates at each macro period, ensuring that the opening price for every macro session is accurately reflected on the chart.
This tool is useful for traders who want to track the behavior of price within key macro intervals and visually assess price movement and volatility during these periods.
Cosine-Weighted MA ATR [InvestorUnknown]The Cosine-Weighted Moving Average (CWMA) ATR (Average True Range) indicator is designed to enhance the analysis of price movements in financial markets. By incorporating a cosine-based weighting mechanism , this indicator provides a unique approach to smoothing price data and measuring volatility, making it a valuable tool for traders and investors.
Cosine-Weighted Moving Average (CWMA)
The CWMA is calculated using weights derived from the cosine function, which emphasizes different data points in a distinctive manner. Unlike traditional moving averages that assign equal weight to all data points, the cosine weighting allocates more significance to values at the edges of the data window. This can help capture significant price movements while mitigating the impact of outlier values.
The weights are shifted to ensure they remain non-negative, which helps in maintaining a stable calculation throughout the data series. The normalization of these weights ensures they sum to one, providing a proportional contribution to the average.
// Function to calculate the Cosine-Weighted Moving Average with shifted weights
f_Cosine_Weighted_MA(series float src, simple int length) =>
var float cosine_weights = array.new_float(0)
array.clear(cosine_weights) // Clear the array before recalculating weights
for i = 0 to length - 1
weight = math.cos((math.pi * (i + 1)) / length) + 1 // Shift by adding 1
array.push(cosine_weights, weight)
// Normalize the weights
sum_weights = array.sum(cosine_weights)
for i = 0 to length - 1
norm_weight = array.get(cosine_weights, i) / sum_weights
array.set(cosine_weights, i, norm_weight)
// Calculate Cosine-Weighted Moving Average
cwma = 0.0
if bar_index >= length
for i = 0 to length - 1
cwma := cwma + array.get(cosine_weights, i) * close
cwma
Cosine-Weighted ATR Calculation
The ATR is an essential measure of volatility, reflecting the average range of price movement over a specified period. The Cosine-Weighted ATR uses a similar weighting scheme to that of the CWMA, allowing for a more nuanced understanding of volatility. By emphasizing more recent price movements while retaining sensitivity to broader trends, this ATR variant offers traders enhanced insight into potential price fluctuations.
// Function to calculate the Cosine-Weighted ATR with shifted weights
f_Cosine_Weighted_ATR(simple int length) =>
var float cosine_weights_atr = array.new_float(0)
array.clear(cosine_weights_atr)
for i = 0 to length - 1
weight = math.cos((math.pi * (i + 1)) / length) + 1 // Shift by adding 1
array.push(cosine_weights_atr, weight)
// Normalize the weights
sum_weights_atr = array.sum(cosine_weights_atr)
for i = 0 to length - 1
norm_weight_atr = array.get(cosine_weights_atr, i) / sum_weights_atr
array.set(cosine_weights_atr, i, norm_weight_atr)
// Calculate Cosine-Weighted ATR using true ranges
cwatr = 0.0
tr = ta.tr(true) // True Range
if bar_index >= length
for i = 0 to length - 1
cwatr := cwatr + array.get(cosine_weights_atr, i) * tr
cwatr
Signal Generation
The indicator generates long and short signals based on the relationship between the price (user input) and the calculated upper and lower bands, derived from the CWMA and the Cosine-Weighted ATR. Crossover conditions are used to identify potential entry points, providing a systematic approach to trading decisions.
// - - - - - CALCULATIONS - - - - - //{
bar b = bar.new()
float src = b.calc_src(cwma_src)
float cwma = f_Cosine_Weighted_MA(src, ma_length)
// Use normal ATR or Cosine-Weighted ATR based on input
float atr = atr_type == "Normal ATR" ? ta.atr(atr_len) : f_Cosine_Weighted_ATR(atr_len)
// Calculate upper and lower bands using ATR
float cwma_up = cwma + (atr * atr_mult)
float cwma_dn = cwma - (atr * atr_mult)
float src_l = b.calc_src(src_long)
float src_s = b.calc_src(src_short)
// Signal logic for crossovers and crossunders
var int signal = 0
if ta.crossover(src_l, cwma_up)
signal := 1
if ta.crossunder(src_s, cwma_dn)
signal := -1
//}
Backtest Mode and Equity Calculation
To evaluate its effectiveness, the indicator includes a backtest mode, allowing users to test its performance on historical data:
Backtest Equity: A detailed equity curve is calculated based on the generated signals over a user-defined period (startDate to endDate).
Buy and Hold Comparison: Alongside the strategy’s equity, a Buy-and-Hold equity curve is plotted for performance comparison.
Visualization and Alerts
The indicator features customizable plots, allowing users to visualize the CWMA, ATR bands, and signals effectively. The colors change dynamically based on market conditions, with clear distinctions between long and short signals.
Alerts can be configured to notify users of crossover events, providing timely information for potential trading opportunities.
