Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role.
1. Hammer:
• Typically occurs after a downtrend.
• Signals a potential trend reversal to the upside.
• A Hammer has:
• A small body (close and open are close to each other).
• A long lower shadow, at least twice as long as the body.
• No or a very short upper shadow.
2. Hanging Man:
• Typically occurs after an uptrend.
• Signals a potential reversal to the downside.
• A Hanging Man has:
• A small body, similar to the Hammer.
• A long lower shadow, at least twice as long as the body.
• A small or no upper shadow.
These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control.
Relevance of Chart Patterns in Forex
In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants.
Scientific Literature on the Relevance of Candlestick Patterns
1. Behavioral Finance and Candlestick Patterns:
Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends.
2. Statistical Validation of Chart Patterns:
Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well.
3. Market Efficiency and Candlestick Patterns:
The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading.
Testing the Strategy in Forex Using the Provided Script
The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes.
1. Testing on Different Timeframes:
• The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness.
2. Exit Strategies:
• The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior.
Conclusion
The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness.
For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades.
References:
• Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
• Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731-1764.
• 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.
This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
在腳本中搜尋"algo"
Adaptive Trend Flow Strategy with Filters for SPXThe Adaptive Trend Flow Strategy with Filters for SPX is a complete trading algorithm designed to identify traits and offer actionable alerts for the SPX index. This Pine Script approach leverages superior technical signs and user-described parameters to evolve to marketplace conditions and optimize performance.
Key Features and Functionality
Dynamic Trend Detection: Utilizes a dual EMA-based totally adaptive method for fashion calculation.
The script smooths volatility the usage of an EMA filter and adjusts sensitivity through the sensitivity enter. This allows for real-time adaptability to market fluctuations.
Trend Filters for Precision:
SMA Filter: A Simple Moving Average (SMA) guarantees that trades are achieved best while the rate aligns with the shifting average trend, minimizing false indicators.
MACD Filter: The Moving Average Convergence Divergence (MACD) adds some other layer of confirmation with the aid of requiring alignment among the MACD line and its sign line.
Signal Generation:
Long Signals: Triggered when the fashion transitions from bearish to bullish, with all filters confirming the pass.
Short Signals: Triggered while the trend shifts from bullish to bearish, imparting opportunities for final positions.
User Customization:
Adjustable parameters for EMAs, smoothing duration, and sensitivity make certain the strategy can adapt to numerous buying and selling patterns.
Enable or disable filters (SMA or MACD) based totally on particular market conditions or consumer possibilities.
Leverage and Position Sizing: Incorporates a leverage aspect for dynamic position sizing.
Automatically calculates the exchange length based on account fairness and the leverage element, making sure hazard control is in area.
Visual Enhancements: Plots adaptive fashion ranges (foundation, top, decrease) for actual-time insights into marketplace conditions.
Color-coded bars and heritage to visually represent bullish or bearish developments.
Custom labels indicating crossover and crossunder occasions for clean sign visualization.
Alerts and Automation: Configurable alerts for each lengthy and quick indicators, well matched with automated buying and selling structures like plugpine.Com.
JSON-based alert messages consist of account credentials, motion type, and calculated position length for seamless integration.
Backtesting and Realistic Assumptions: Includes practical slippage, commissions, and preliminary capital settings for backtesting accuracy.
Leverages excessive-frequency trade sampling to make certain strong strategy assessment.
How It Works
Trend Calculation: The method derives a principal trend basis with the aid of combining fast and gradual EMAs. It then uses marketplace volatility to calculate adaptive upper and decrease obstacles, creating a dynamic channel.
Filter Integration: SMA and MACD filters work in tandem with the fashion calculation to ensure that handiest excessive-probability signals are accomplished.
Signal Execution: Signals are generated whilst the charge breaches those dynamic tiers and aligns with the fashion and filters, ensuring sturdy change access situations.
How to Use
Setup: Apply the approach to SPX or other well suited indices.
Adjust person inputs, together with ATR length, EMA smoothing, and sensitivity, to align together with your buying and selling possibilities.
Enable or disable the SMA and MACD filters to test unique setups.
Alerts: Configure signals for computerized notifications or direct buying and selling execution through third-celebration systems.
Use the supplied JSON payload to integrate with broking APIs or automation tools.
Optimization:
Experiment with leverage, filter out settings, and sensitivity to find most effective configurations to your hazard tolerance and marketplace situations.
Considerations and Best Practices
Risk Management: Always backtest the method with realistic parameters, together with conservative leverage and commissions.
Market Suitability: While designed for SPX, this method can adapt to other gadgets by means of adjusting key parameters.
Limitations: The method is trend-following and can underperform in enormously risky or ranging markets. Regularly evaluate and modify parameters primarily based on recent market conduct.
If you have any questions please let me know - I'm here to help!
DCA Alpha 1.0 Trading Tool for Dollar-Cost Averaging
Description:
DCA Alpha 1.0 is a precision-engineered trading tool designed to assist traders and investors in accumulating assets during market downturns. Using proprietary algorithms that combine momentum decay, extreme price deviation metrics, trend dynamics, divergence analysis, and mean regression, it identifies potential bottom extreme zones in various asset classes such as indices, stocks, crypto, and commodities.
This indicator highlights market conditions where assets are oversold, undervalued, or experiencing capitulation—providing disciplined, unleveraged dollar-cost averaging (DCA) opportunities. Ideal for long-term growth strategies, DCA Alpha 1.0 helps cut through market noise, pinpointing moments of peak fear and maximum reward potential.
Whether navigating volatile crypto markets, timing corrections in indices, or accumulating commodities, DCA Alpha 1.0 serves as a vital tool for mastering the art of buying low and building your assets up strategically.
Instructions:
Getting Started:
Add the Indicator:
Install DCA Alpha 1.0 on your TradingView chart.
Select your preferred asset class: stocks, indices, crypto, or commodities.
Choose an appropriate timeframe (e.g., daily or weekly for long-term DCA strategies).
Customize Inputs: Adjust the following settings to align with your strategy:
Percentage of Equity to Trade: Define the portion of your portfolio to allocate per signal (default: 1% equity).
Profit Target Percentages: Set thresholds for locking in gains (default: 50% on lower timeframes, 500% on higher timeframes).
Zones and Signals:
Extreme Negative Zones:
What It Represents:
These zones highlight conditions where prices are deeply oversold, indicating extreme bearish sentiment. The market is likely nearing a bottom, offering high-probability buying opportunities.
Entry Signals:
When the price enters these extreme negative zones, visual markers (e.g., green triangles or other indicators) will signal a potential buying opportunity. These moments are indicative of market exhaustion, signaling that a reversal could be imminent.
Momentum Decay & Divergence:
Momentum decay occurs when price movement slows over time. In extreme negative zones, if prices continue to fall but at a diminishing rate (e.g., decreased volume or a fading oscillator), it suggests weakening bearish momentum. This, coupled with bullish divergence (oscillator forming higher lows while price makes lower lows), signifies a reversal, making it an ideal point to consider dollar-cost averaging into the asset.
Neutral Zones:
What It Represents:
The neutral zone is a state of market equilibrium, where prices are neither overbought nor oversold. The market is in a balanced state, with no strong trend emerging.
Mean Regression:
In a neutral zone, the market is reverting to its mean or average price after overreacting in either direction. A price transition from extreme zones (overbought/oversold) to the neutral zone suggests a reversion to the market's long-term average, making this a period of reduced volatility and uncertainty.
Entering or Exiting Neutral Zones:
Traders should avoid entering or exiting positions during neutral zone conditions unless transitioning from an extreme zone (negative or positive). Transitioning from an extreme negative zone to neutral may suggest an opportunity to accumulate assets gradually, while a shift from neutral to an extreme negative zone may indicate a deeper correction and warrant caution.
Momentum Decay & Divergence (Exiting Neutral Zone):
If prices are rising but the oscillator shows lower highs (bearish divergence), and momentum is fading, this could signal a pullback. A transition out of the neutral zone in this context may prompt traders to hold off on new positions or consider profit-taking.
Extreme Positive Zones:
What It Represents:
Markets can also become overbought or overvalued. When price enters extreme positive zones, the asset may be overvalued, suggesting potential selling or a waiting period.
Exit Signals:
Red triangle indicators signal potential exit points when prices reach overbought conditions, signaling a time to lock in profits and reduce exposure.
Momentum Decay & Divergence (Exiting Positive Zone):
When prices are making new highs but momentum is weakening (momentum decay) and the oscillator is showing lower highs (bearish divergence), this could indicate a faltering rally. Such conditions represent an ideal time to reduce exposure or exit positions.
Key Inputs for Customization:
Percentage of Equity to Trade:
This setting allows you to allocate a portion of your total portfolio per buy signal. By default, 1% of equity is used per signal, but this can be adjusted based on your risk tolerance and strategy.
Profit Target Percentages:
These thresholds help lock in gains once the price moves a set percentage in your favor.
Lower Timeframes: Default profit target of 50%.
Higher Timeframes: Default profit target of 500%.
These settings can be customized for specific risk/reward preferences.
Warning!!! : Aggressive Mode
Aggressive Mode is an advanced feature designed for traders who want to increase the frequency of signals during periods of market volatility. This mode will trigger more frequent entries, even into slightly less extreme zones, capturing short-term reversals.
What Aggressive Mode Does:
It amplifies signals by allowing the tool to identify more frequent price reversals, including brief market corrections, increasing trade frequency. While this can offer more trading opportunities, it also exposes you to higher risk.
Warning:
Aggressive Mode should be used only by experienced traders familiar with short-term volatility. The increased frequency of signals could lead to higher risk exposure. Ensure robust risk management practices, such as stop-loss orders and profit-taking strategies, are in place before activating this mode.
Default Setting:
Aggressive Mode is disabled by default. It can be activated at your discretion based on your experience level and risk appetite.
Best Practices:
Focus on High-Quality Assets: Prioritize assets with strong recovery potential (e.g., major indices, blue-chip cryptocurrencies).
Use Longer Timeframes: Minimize market noise and optimize your DCA strategy by focusing on higher timeframes (e.g., daily or weekly charts).
Review Trading Inputs: Regularly adjust your inputs to ensure they align with your financial goals and risk tolerance.
Implement Risk Management: Use stop-loss orders and profit targets to manage risk, especially when using Aggressive Mode.
Disclaimer:
DCA Alpha 1.0 is designed specifically for unleveraged, long-term dollar-cost averaging strategies. It is not intended for day trading or leveraged positions. The tool excels at identifying market dips but cannot guarantee success. Users are fully responsible for their own risk management, including the use of stop-losses, profit targets, and position sizing.
Aggressive Mode increases trade frequency and may lead to higher exposure and potential losses. Only experienced traders should consider using this mode. Always understand the risks involved before incorporating this tool into your trading strategy.
DemaRSI StrategyThis is a repost to a old script that cant be updated anymore, the request was made on Feb, 27, 2016.
Here's a engaging description for the tradingview script:
**DemaRSI Strategy: A Proven Trading System**
Join thousands of traders who have already experienced the power of this highly effective strategy. The DemaRSI system combines two powerful indicators - DEMA (Double Exponential Moving Average) and RSI (Relative Strength Index) - to generate profitable trades with minimal risk.
**Key Features:**
* **Trend-Following**: Our algorithm identifies strong trends using a combination of DEMA and RSI, allowing you to ride the waves of market momentum.
* **Risk Management**: The system includes built-in stop-loss and take-profit levels, ensuring that your gains are protected and losses are minimized.
* **Session-Based Trading**: Trade during specific sessions only (e.g., London or New York) for even more targeted results.
* **Customizable Settings**: Adjust the length of moving averages, RSI periods, and other parameters to suit your trading style.
**What You'll Get:**
* A comprehensive strategy that can be used with any broker or platform
* Easy-to-use interface with customizable settings
* Real-time performance metrics and backtesting capabilities
**Start Trading Like a Pro Today!**
This script is designed for intermediate to advanced traders who want to take their trading game to the next level. With its robust risk management features, this strategy can help you achieve consistent profits in various market conditions.
**Disclaimer:** This script is not intended as investment advice and should be used at your own discretion. Trading carries inherent risks, and losses are possible.
~Llama3
FXC NQ Opening Range Breakout Strategy V2.4Mechanical Strategy that trades breakouts on NQ futures on the 15min timeframe during the NYSE session. It's designed to manage Apex and Top Step accounts with the lowest risk possible.
Risk Disclaimer:
Past results as well as strategy tester reports do not indicate future performance. Guarantees do not exist in trading. By using this strategy you risk losing all your money.
Important:
It only trades on Monday, Wednesday and Friday and takes usually only 1 trade per trading day.
It works on the 15min timeframe only.
The settings are optimised already for NQ but feel free to change them.
How it works:
Every selected trading day it measures the range of the first 15min candle after the NYSE open. As soon as price closes above on the 15min timeframe, it will trade the breakout targeting a set risk to reward ratio. SL on the opposite side of the range. It will trail the SL after a set amount of points and uses a buffer of the set amount of points to trail it.
Settings:
Opening Range Time : This is the time of the day in hours and minutes when the strategy starts looking for trades. It's in the EST/ NY Timezone and set to 9:30-09:45 by default
because that's the NYSE open.
Session Time : This is the time of the day in hours and minutes until the strategy trades. It's in the EST/ NY Timezone and set to 09:45-14:45 by default.
because that's what gave the best results in backtesting. Open trades will get closed automatically once the end of the session is reached. No matter if win or loss. This is just to prevent holding positions over night.
Session Border This setting is to select the border color in which the session box will be plotted.
Opening Range Box This setting is to select the fill color of the opening range box.
Opening Range Border This setting is to select the border color of the session box.
Trade Timeframe This setting determines on which timeframe candle has to close outside the opening range box in order to take a trade. It's set to 15min by default because this is what worked by far the best in backtests and live trading.
Stop Loss Buffer in Points: This is simply the buffer in points that is added to the SL for safety reasons. If you have it on 0, the SL will be at the exact price of the opposite side of the range. By default it's set to 0 pips because this is what delivered the best results in backtests.
Profit Target Factor: This is simply the total SL size in points multiplied by x.
Example: If you put 2, you get a 1:2 Risk to Reward Ratio. By Default it's set to 4 because this gave the best results in backtests, because trades always get closed either by trailing SL or because the end of the session is reached.
Use Trailing Stop Loss: This setting is to enable/ disable the trailing stop loss. It's enabled by default because this is a fundamental part of the strategy.
Trailing Stop Buffer: This setting determines after how many points in profit the trailing SL will be activated.
Risk Type: You can chose either between Fixed USD Amount, Risk per Trade in % or Fixed Contract Size. By default it's set to fixed contract size.
Risk Amount (USD or Contracts): This setting is to set how many USD or how many contracts you want to risk per trade. Make sure to check which risk type you have selected before you chose the risk amount.
Use Limit Orders If enabled, the strategy will place a pending order x points from the current price, instead of a market order. Limit orders are enabled by default for a better performance. Important: It doesn't actually place a limit order. The strategy will just wait for a pullback and then enter with a market order. It's more like a hidden limit order.
Limit Order Distance (points): If you have limit orders enabled, this setting determines how many points from the current price the limit order will be placed.
Trading Days: These checkboxes are to select on which week days the strategy has to trade. Thursday is disabled by default because backtests have shown that Thursday is the least profitable day
Backtest Settings:
For the backtest the commissions ere set to 0.35 USD per mini contract which is the highest amount Tradeovate charges. Margin was not accounted for because typically on Apex accounts you can use way more contracts than you need for the extremely low max drawdown. Margin would be important on personal accounts but even there typically it's not an issue at all especially because this strategy runs on the 15min timeframe so it won't use a lot of contracts anyways.
What makes it unique:
This script is unique because it's designed to be used on Apex and Top Step accounts with extremely strict drawdown rules.
The strategy is optimised to be traded with a fixed contract size instead of using % risk. The reason for that is that the drawdown rules of these Futures Prop Accounts are very strict and the fact that the smallest trade-able contract size is 1.
Why the source code is hidden:
The source code is hidden because I invested a lot of time and money into developing this strategy and optimising it with paid 3rd party software. Also since I use it myself on my Apex accounts and prop firms don't allow copy trading I don't want it to be used by too many traders.
MultiLayer Awesome Oscillator Saucer Strategy [Skyrexio]Overview
MultiLayer Awesome Oscillator Saucer Strategy leverages the combination of Awesome Oscillator (AO), Williams Alligator, Williams Fractals 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. Awesome Oscillator is used for creating 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 (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. Awesome Oscillator shall create the "Saucer" long signal (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created "Saucer signal".
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one "Saucer" signal another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. 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.
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 go through all concepts used in this strategy to understand how they works together. Let's start from the easies one, the EMA. Let's briefly explain what is EMA. The Exponential Moving Average (EMA) is a type of moving average that gives more weight to recent prices, making it more responsive to current price changes compared to the Simple Moving Average (SMA). It is commonly used in technical analysis to identify trends and generate buy or sell signals. It can be calculated with the following steps:
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 uses EMA an initial long term trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
Let's go to the next, short-term trend filter which consists of Alligator and Fractals. Let's briefly explain what do these indicators means. The Williams Alligator, developed by Bill Williams, is a technical indicator designed to spot trends and potential market reversals. It uses three smoothed moving averages, referred to as the jaw, teeth, and lips:
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 these lines diverge and are properly aligned, the "alligator" is considered "awake," signaling a strong trend. Conversely, when the lines overlap or intertwine, the "alligator" is "asleep," indicating a range-bound or sideways market. This indicator assists traders in identifying when to act on or avoid trades.
The Williams Fractals, another tool introduced by Bill Williams, are used to pinpoint potential reversal points on a price chart. A fractal forms when there are at least five consecutive bars, with the middle bar displaying the highest high (for an up fractal) or the lowest low (for a down fractal), relative to the two bars on either side.
Key Points:
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 combine fractals with other indicators to confirm trends or reversals, improving the accuracy of trading decisions.
How we use their combination in this strategy? Let’s consider an uptrend example. A breakout above an up fractal can be interpreted as a bullish signal, indicating a high likelihood that an uptrend is beginning. Here's the reasoning: an up fractal represents a potential shift in market behavior. When the fractal forms, it reflects a pullback caused by traders selling, creating a temporary high. However, if the price manages to return to that fractal’s high and break through it, it suggests the market has "changed its mind" and a bullish trend is likely emerging.
The moment of the breakout marks the potential transition to an uptrend. It’s crucial to note that this breakout must occur above the Alligator's teeth line. If it happens below, the breakout isn’t valid, and the downtrend may still persist. The same logic applies inversely for down fractals in a downtrend scenario.
So, if last up fractal breakout was higher, than Alligator's teeth and it happened after last down fractal breakdown below teeth, algorithm considered current trend as an uptrend. During this uptrend long trades can be opened if signal was flashed. If during the uptrend price breaks down the down fractal below teeth line, strategy considered that uptrend is finished with the high probability and strategy closes all current long trades. This combination is used as a short term trend filter increasing the probability of opening profitable long trades in addition to EMA filter, described above.
Now let's talk about Awesome Oscillator's "Sauser" signals. Briefly explain what is the Awesome Oscillator. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
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
Now we know what is AO, but what is the "Saucer" signal? This concept was introduced by Bill Williams, let's briefly explain it and how it's used by this strategy. Initially, this type of signal is a combination of the following AO bars: we need 3 bars in a row, the first one shall be higher than the second, the third bar also shall be higher, than second. All three bars shall be above the zero line of AO. The price bar, which corresponds to third "saucer's" bar is our signal bar. Strategy places buy stop order one tick above the price bar which corresponds to signal bar.
After that we can have the following scenarios.
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower low. If current AO bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AO bar become decreasing. In the second case buy order cancelled and strategy wait for the next "Saucer" signal.
If long trades has been opened strategy use all the next signals until number of trades doesn't exceed 5. All trades are closed when the trend changes to downtrend according to combination of Alligator and Fractals described above.
Why we use "Saucer" signals? If AO above the zero line there is a high probability that price now is in uptrend if we take into account our two trend filters. When we see the decreasing bars on AO and it's above zero it's likely can be considered as a pullback on the uptrend. When we see the stop of AO decreasing and the first increasing bar has been printed there is a high probability that this local pull back is finished and strategy open long trade in the likely direction of a main trend.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next saucer signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.25. 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: -5.10%
Maximum Single Profit: +22.80%
Net Profit: +2838.58 USDT (+28.39%)
Total Trades: 107 (42.99% win rate)
Profit Factor: 3.364
Maximum Accumulated Loss: 373.43 USDT (-2.98%)
Average Profit per Trade: 26.53 USDT (+2.40%)
Average Trade Duration: 78 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h 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
Adaptive Squeeze Momentum StrategyThe Adaptive Squeeze Momentum Strategy is a versatile trading algorithm designed to capitalize on periods of low volatility that often precede significant price movements. By integrating multiple technical indicators and customizable settings, this strategy aims to identify optimal entry and exit points for both long and short positions.
Key Features:
Long/Short Trade Control:
Toggle Options: Easily enable or disable long and short trades according to your trading preferences or market conditions.
Flexible Application: Adapt the strategy for bullish, bearish, or neutral market outlooks.
Squeeze Detection Mechanism:
Bollinger Bands and Keltner Channels: Utilizes the convergence of Bollinger Bands inside Keltner Channels to detect "squeeze" conditions, indicating a potential breakout.
Dynamic Squeeze Length: Calculates the average squeeze duration to adapt to changing market volatility.
Momentum Analysis:
Linear Regression: Applies linear regression to price changes over a specified momentum length to gauge the strength and direction of momentum.
Dynamic Thresholds: Sets momentum thresholds based on standard deviations, allowing for adaptive sensitivity to market movements.
Momentum Multiplier: Adjustable setting to fine-tune the aggressiveness of momentum detection.
Trend Filtering:
Exponential Moving Average (EMA): Implements a trend filter using an EMA to align trades with the prevailing market direction.
Customizable Length: Adjust the EMA length to suit different trading timeframes and assets.
Relative Strength Index (RSI) Filtering:
Overbought/Oversold Signals: Incorporates RSI to avoid entering trades during overextended market conditions.
Adjustable Levels: Set your own RSI oversold and overbought thresholds for personalized signal generation.
Advanced Risk Management:
ATR-Based Stop Loss and Take Profit:
Adaptive Levels: Uses the Average True Range (ATR) to set stop loss and take profit points that adjust to market volatility.
Custom Multipliers: Modify ATR multipliers for both stop loss and take profit to control risk and reward ratios.
Minimum Volatility Filter: Ensures trades are only taken when market volatility exceeds a user-defined minimum, avoiding periods of low activity.
Time-Based Exit:
Holding Period Multiplier: Defines a maximum holding period based on the momentum length to reduce exposure to adverse movements.
Automatic Position Closure: Closes positions after the specified holding period is reached.
Session Filtering:
Trading Session Control: Limits trading to predefined market hours, helping to avoid illiquid periods.
Custom Session Times: Set your preferred trading session to match market openings, closings, or specific timeframes.
Visualization Tools:
Indicator Plots: Displays Bollinger Bands, Keltner Channels, and trend EMA on the chart for visual analysis.
Squeeze Signals: Marks squeeze conditions on the chart, providing clear visual cues for potential trade setups.
Customization Options:
Indicator Parameters: Fine-tune lengths and multipliers for Bollinger Bands, Keltner Channels, momentum calculation, and ATR.
Entry Filters: Choose to use trend and RSI filters to refine trade entries based on your strategy.
Risk Management Settings: Adjust stop loss, take profit, and holding periods to match your risk tolerance.
Trade Direction Control: Enable or disable long and short trades independently to align with your market strategy or compliance requirements.
Time Settings: Modify the trading session times and enable or disable the time filter as needed.
Use Cases:
Trend Traders: Benefit from aligning entries with the broader market trend while capturing breakout movements.
Swing Traders: Exploit periods of low volatility leading to significant price swings.
Risk-Averse Traders: Utilize advanced risk management features to protect capital and manage exposure.
Disclaimer:
This strategy is a tool to assist in trading decisions and should be used in conjunction with other analyses and risk management practices. Past performance is not indicative of future results. Always test the strategy thoroughly and adjust settings to suit your specific trading style and market conditions.
Candle Range Theory [Advanced] - AlgoVisionUnderstanding Candle Range Theory (CRT) in the AlgoVision Indicator
Candle Range Theory (CRT) is a structured approach to analyzing market movements within the price ranges of candlesticks. CRT is founded on the idea that each candlestick on a chart, regardless of timeframe, represents a distinct range of price action, marked by the candle's open, high, low, and close. This range gives insights into market dynamics, and when analyzed in lower timeframes, reveals patterns that indicate underlying market sentiment and institutional behaviors.
Key Concepts of Candle Range Theory
Candlestick Range: The range of a candlestick is simply the distance between its high and low. Across timeframes, this range highlights significant price behavior, with each candlestick representing a snapshot of price movement. The body (distance between open and close) shows the primary price action, while wicks (shadows) reflect price fluctuations or "noise" around this movement.
Multi-Timeframe Analysis: A higher-timeframe (HTF) candlestick can be dissected into smaller, structured price movements in lower timeframes (LTFs). By analyzing these smaller movements, traders gain a detailed view of the market’s progression within the HTF candlestick’s range. Each HTF candlestick’s high and low provide support and resistance levels on the LTF, where the price can "sweep," break out, or retest these levels.
Market Behavior within the Range: Price action within a range doesn’t move randomly; it follows structured behavior, often revealing patterns. By analyzing these patterns, CRT provides insights into the market’s intention to accumulate, manipulate, or distribute assets within these ranges. This behavior can indicate future market direction and increase the probability of accurate trading signals.
CRT and ICT Power of 3: Accumulation, Manipulation, and Distribution (AMD)
A foundational element of our CRT indicator is its combination with ICT’s Power of 3 (Accumulation, Manipulation, and Distribution or AMD). This approach identifies three stages of market movement:
Accumulation: During this phase, institutions accumulate positions within a tight price range, often leading to sideways movement. Here, price consolidates as institutions carefully enter or exit positions, erasing traces of their intent from public view.
Manipulation: Institutions often use manipulation to create false breakouts, targeting retail traders who enter the market on perceived breakouts or reversals. Manipulation is characterized by liquidity grabs, false breakouts, or stop hunts, as price momentarily moves outside the established range before quickly returning.
Distribution: Following accumulation and manipulation, the distribution phase aligns with the true market direction. Institutions now allow the market to move with the trend, initiating a stronger and more sustained price movement that aligns with their intended position.
This AMD cycle is often observed across multiple timeframes, allowing traders to refine entries and exits by identifying accumulation, manipulation, and distribution phases on smaller timeframes within the range of a higher-timeframe candle. CRT views this cycle as the "heartbeat" of the market—a continuous loop of price movements. With our indicator, you can identify this cycle on your current timeframe, with the signal candle acting as the "manipulation" candle.
How to Use the Premium AlgoVision CRT Indicator
1. Indicator Display Options
Bullish/Bearish Plot Indication: Toggles the display of bullish or bearish CRT signals. Turn this on to display signals on your chart or off to reduce screen clutter.
Order Block Indication: Highlights the order block entry price, which is the preferred entry point for CRT trades.
Purge Time Indication: Shows when the low or high of Candle 1 is purged by Candle 2, helping to identify potential manipulation points.
2. Filter Options
Match Indicator Candle with Signal: Ensures that only bullish Candle 2s (for longs) or bearish Candle 2s (for shorts) are signaled. This filter helps eliminate signals where the candlestick’s direction does not align with the CRT model.
Take Profit Already Reached: When enabled, this filter removes CRT signals if take profit levels are reached within Candle 2. This helps focus on setups where there’s still room for price movement.
Midnight Price Filter: Filters signals based on midnight price levels:
Longs: Only signals if the order block entry price is below the midnight price.
Shorts: Only signals if the order block entry price is above the midnight price.
3. Entry and Exit Settings
Wick out prevention: Allows positions to stay open and prevent getting wicked out. Positions will still be able to close if determined by the algorithm.
Buy/Sell: This allows you to set you daily bias. You can select to only see buys or sells.
Custom Stop Loss: Sets a custom stop loss distance from the entry price (e.g., $100 or $200 away) if the predefined stop loss based on Candle 2’s low/high doesn’t suit your preference.
Take Profit Levels: Choose from three take profit levels:
Optimized Take Profit: Uses an optimized take profit level based on CRT’s recommended exit point.
Take Profit 1: Sets an initial take profit level.
Take Profit 2: Sets a secondary take profit level for a more extended exit target.
Timeframe of Order Block: Select the timeframe of the order block entry, which can be tailored based on the timeframe of the CRT signal.
Risk-to-Reward Filter: Filters trades based on a specified risk-to-reward ratio, using the indicator’s stop loss as the base. This helps to ensure trades meet minimum reward criteria.
4. Risk Management
Fixed Entry QTY: This will allow you to open all positions with a fixed QTY
Risk to Reward Ratio: This allows you to set a minimum risk to reward ratio, the strategy will only take trades if this risk to reward is met.
Risk Type:
Fixed Amount: Allows you to risk a fixed $ amount.
% of account: Allows you to risk % of account equity.
5. Day and Time Filters
Filter by Days: Specify the days of the week for CRT signals to appear. For instance, you could enable signals only on Thursdays. This setting can be adjusted to any day or combination of days.
Purge Time Filter: Filters CRT signals based on specific purge times when Candle 1’s low/high is breached by Candle 2, as CRT setups are observed to work best during certain times.
Hour Filters for CRT Signals:
1-Hour CRT Times: Allows filtering CRT signals based on specific 1-hour time intervals.
4-Hour CRT Times: Filter 4-hour CRT signals based on specified times.
Forex and Futures Conversion: Adjusts times based on standard sessions for Forex (e.g., 9:00 AM 4-hour candle) and Futures (e.g., 10 PM candle for Futures or 8 AM for Crypto).
6. Currency and Asset-Specific Filters
Crypto vs. Forex Mode: This setting adjusts the indicator’s timing to match market sessions specific to either crypto or Forex/Futures, ensuring the CRT model aligns with the asset type.
Additional Notes
Backtesting Options: Adjust these to test risk management, such as risking a fixed amount or a percentage of the account, for historical performance insights.
Optimized Settings: This version includes all features and optimized settings, with the most refined data analysis.
Conclusion By combining CRT with ICT Power of 3, the AlgoVision Indicator allows traders to leverage the CRT candlestick as a versatile tool for identifying potential market moves. This method provides beginners and seasoned traders alike with a robust framework to understand market dynamics and refine trade strategies across timeframes. Setting alerts on the higher timeframe to catch bullish or bearish CRT signals allows you to plan and execute trades on the lower timeframe, aligning your strategy with the broader market flow.
Keltner Channel Strategy by Kevin DaveyKeltner Channel Strategy Description
The Keltner Channel Strategy is a volatility-based trading approach that uses the Keltner Channel, a technical indicator derived from the Exponential Moving Average (EMA) and Average True Range (ATR). The strategy helps identify potential breakout or mean-reversion opportunities in the market by plotting upper and lower bands around a central EMA, with the channel width determined by a multiplier of the ATR.
Components:
1. Exponential Moving Average (EMA):
The EMA smooths price data by placing greater weight on recent prices, allowing traders to track the market’s underlying trend more effectively than a simple moving average (SMA). In this strategy, a 20-period EMA is used as the midline of the Keltner Channel.
2. Average True Range (ATR):
The ATR measures market volatility over a 14-period lookback. By calculating the average of the true ranges (the greatest of the current high minus the current low, the absolute value of the current high minus the previous close, or the absolute value of the current low minus the previous close), the ATR captures how much an asset typically moves over a given period.
3. Keltner Channel:
The upper and lower boundaries are set by adding or subtracting 1.5 times the ATR from the EMA. These boundaries create a dynamic range that adjusts with market volatility.
Trading Logic:
• Long Entry Condition: The strategy enters a long position when the closing price falls below the lower Keltner Channel, indicating a potential buying opportunity at a support level.
• Short Entry Condition: The strategy enters a short position when the closing price exceeds the upper Keltner Channel, signaling a potential selling opportunity at a resistance level.
The strategy plots the upper and lower Keltner Channels and the EMA on the chart, providing a visual representation of support and resistance levels based on market volatility.
Scientific Support for Volatility-Based Strategies:
The use of volatility-based indicators like the Keltner Channel is supported by numerous studies on price momentum and volatility trading. Research has shown that breakout strategies, particularly those leveraging volatility bands such as the Keltner Channel or Bollinger Bands, can be effective in capturing trends and reversals in both trending and mean-reverting markets  .
Who is Kevin Davey?
Kevin Davey is a highly respected algorithmic trader, author, and educator, known for his systematic approach to building and optimizing trading strategies. With over 25 years of experience in the markets, Davey has earned a reputation as an expert in quantitative and rule-based trading. He is particularly well-known for winning several World Cup Trading Championships, where he consistently demonstrated high returns with low risk.
Universal Trend Following Strategy | QuantumRsearchUniversal All Assets Strategy by Rocheur
The Universal All Assets Strategy is a cutting-edge, trend-following algorithm designed to operate seamlessly across multiple asset classes, including equities, commodities, forex, and cryptocurrencies. This strategy leverages the power of eight unique indicators, offering traders robust, adaptive signals. Its dynamic logic, combined with a comprehensive risk management framework, allows for precision trading in a variety of market conditions.
Core Methodologies and Features
1. Eight Integrated Trend Indicators
At the heart of the Universal All Assets Strategy are eight sophisticated trend-following indicators, each designed to capture different facets of market behavior. These indicators work together to provide a multi-dimensional analysis of price trends, filtering out noise and reacting only to significant movements:
Directional Moving Averages : Tracks the primary market trend, offering a clear indication of long-term price direction, ideal for identifying sustained upward or downward movements.
Smoothed Moving Averages : Reduces short-term volatility and noise to reveal the underlying trend, enhancing signal clarity and helping traders avoid reacting to temporary price spikes.
RSI Loops : Utilizes the Relative Strength Index (RSI) to assess market momentum, using a unique for loop mechanism to smooth out data and enhance precision.
Supertrend Filters : This indicator dynamically adjusts to market volatility, closely following price action to detect significant breakouts or reversals. The Supertrend is a core component for identifying shifts in trend direction with minimal lag.
RVI for Loop : The Relative Volatility Index (RVI) measures the strength of market volatility. It is optimized with a for loop mechanism, which smooths out the data and improves directional cues, especially in choppy or sideways markets.
Hull for Loop : The Hull Moving Average is designed to minimize lag while offering a smooth, responsive trend line. The for loop mechanism further enhances this by making the Hull even more sensitive to trend shifts, ensuring faster reaction to market movements without generating excessive noise.
These indicators evaluate market conditions independently, assigning a score of 1 for bullish trends and -1 for bearish trends. The average score across all eight indicators is calculated for each time frame (or bar), and this score determines whether the strategy should enter, exit, or remain neutral in a trade.
2. Scoring and Signal Confirmation
The strategy’s confirmation system ensures that trades are initiated only when there is strong alignment across multiple indicators:
A Long Position (Buy) is initiated when the majority of indicators generate a bullish signal, i.e., the average score exceeds a predefined upper threshold.
A Short Position or Exit is triggered when the average score falls below a lower threshold, signaling a bearish trend or neutral market.
By using a majority-rule confirmation system, the strategy filters out weak signals, reducing the chances of reacting to market noise or false positives. This ensures that only robust trends—those supported by multiple indicators—trigger trades.
Adaptive Logic for All Asset Classes
The Universal All Assets Strategy stands out for its ability to adapt dynamically across different asset classes. Whether it’s applied to highly volatile assets like cryptocurrencies or more stable instruments like equities, the strategy fine-tunes its behavior to match the asset’s volatility profile and price behavior.
Volatility Filters : The system incorporates volatility-sensitive filters, such as the Average True Range (ATR) and standard deviation metrics, which dynamically adjust its sensitivity based on market conditions. This ensures the strategy remains responsive to significant price movements while filtering out inconsequential fluctuations.
This adaptability makes the Universal All Assets Strategy effective across diverse markets, providing consistent performance whether the market is trending, range-bound, or experiencing high volatility.
Customization and Flexibility
1. Directional Bias
The strategy offers traders the flexibility to set a customizable directional bias, allowing it to focus on:
Long-only trades during bullish markets.
Short-only trades during bear markets.
Bi-directional trades for those looking to capitalize on both uptrends and downtrends.
This bias can be fine-tuned based on market conditions, trader preference, or risk tolerance, without compromising the integrity of the overall signal-generation process.
2. Volatility Sensitivity
Traders can adjust the strategy’s volatility sensitivity through customizable settings. By modifying how the system reacts to volatility, traders can make the strategy more aggressive in high-volatility environments or more conservative in quieter markets, depending on their individual trading style.
Visual Representation of Component Behavior
One of the unique features of the strategy is its real-time visual representation of the eight indicators through a component table displayed on the chart. This table provides a clear overview of the current status of each indicator:
A score of 1 indicates a bullish signal.
A score of -1 indicates a bearish signal.
The table is updated at each time frame (bar), showing how each indicator is contributing to the overall trend decision. This real-time feedback allows traders to monitor the exact composition of the strategy’s signal, helping them better understand market dynamics.
Oscillator Visualization for Trend Detection
To complement the component table, the strategy includes a trend oscillator displayed beneath the price chart, offering a visual summary of the overall market direction:
Green bars represent bullish trends when the majority of indicators signal an uptrend.
Red bars represent bearish trends or a neutral (cash) position when the majority of indicators detect a downtrend.
This oscillator allows traders to quickly assess the market’s overall direction at a glance, without needing to analyze each individual indicator, providing a clear and immediate visual of the market trend.
Backtested and Forward-Tested for Real-World Conditions
The Universal All Assets Strategy has been thoroughly tested under real-world trading conditions, incorporating key factors like:
Slippage : Set at 20 ticks to represent real market fluctuations.
Order Size : Calculated as 10% of equity, ensuring appropriate risk exposure for realistic capital management.
Commission : A fee of 0.05% has been factored in to account for trading costs.
These settings ensure that the strategy’s performance metrics—such as the Sortino Ratio , Sharpe Ratio , Omega Ratio , and Profit Factor —are reflective of actual trading environments. The rigorous backtesting and forward-testing processes ensure that the strategy produces realistic results, making it compatible with the markets it is written for and demonstrating how the system would behave in live conditions. It also includes robust risk management tools to minimize drawdowns and preserve capital, making it suitable for both professional and retail traders.
Anti-Fragile Design and Realistic Expectations
The Universal All Assets Strategy is engineered to be anti-fragile, thriving in volatile markets by adjusting to turbulence rather than being damaged by it. This is a crucial feature that ensures the strategy remains effective even during times of significant market instability.
Moreover, the strategy is transparent about realistic expectations, acknowledging that no system can guarantee a 100% win rate and that past performance is not indicative of future results. This transparency fosters trust and provides traders with a realistic framework for long-term success, making it an ideal choice for traders looking to navigate complex market conditions with confidence.
Acknowledgment of External Code
Special credit goes to bii_vg, whose invite-only code was used with permission in the development of the Universal All Assets Strategy. Their contributions have been instrumental in refining certain aspects of this strategy, ensuring its robustness and adaptability across various markets.
Conclusion
The Universal All Assets Strategy by Rocheur offers traders a powerful, adaptable tool for capturing trends across a wide range of asset classes. Its eight-indicator confirmation system, combined with customizable settings and real-time visual representations, provides a comprehensive solution for traders seeking precision, flexibility, and consistency. Whether used in high-volatility markets or more stable environments, the strategy’s dynamic adaptability, transparent logic, and robust testing make it an excellent choice for traders aiming to maximize performance while managing risk effectively.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
Neural Momentum StrategyThis strategy combines Exponential Moving Average (EMA) analysis with a multi-timeframe approach. It uses a neural scoring system to evaluate market momentum and generate precise trading signals. The strategy is implemented in Pine Script v5 and is designed for use on TradingView.
Key Components
The strategy utilizes short-term (10-period) and long-term (25-period) EMAs. It calculates the difference between these EMAs to assess trend direction and strength. A neural scoring system evaluates EMA crossovers (weight: 12 points), trend strength (weight: 10 points), and price acceleration (weight: 4 points). The system implements a score smoothing algorithm using a 10-period EMA.
Multi-timeframe Analysis
The strategy automatically selects a higher timeframe based on the current chart timeframe. It calculates scores for both the current and higher timeframes, then combines these scores using a weighted average. The higher timeframe factor ranges from 3 to 6, depending on the current timeframe.
Trading Logic
Entry occurs when the final combined score turns positive after a change. Exit happens when the final combined score turns negative after a change. The strategy recalculates scores on each bar, ensuring responsive trading decisions.
Risk Management
An optional adaptive stop-loss system based on Average True Range (ATR) is available. The default ATR period is 10, and the stop factor is 1.2. Stop levels are dynamically adjusted on the higher timeframe.
Customization Options
Users can adjust EMA periods, signal line period, scoring weights, and enable/disable multi-timeframe analysis. The strategy allows setting specific date ranges for backtesting and deployment.
Position Sizing
The strategy uses a percentage-of-equity position sizing method, with a default of 30% of account equity per trade.
Code Structure
The strategy is built using TradingView's strategy framework. It employs efficient use of the request.security() function for multi-timeframe analysis. The main calculation function, calculate_score(), computes the neural score based on EMA differences and acceleration.
Performance Considerations
The strategy adapts to various market conditions through its multi-faceted scoring system. Multi-timeframe analysis helps filter out noise and identify stronger trends. The neural scoring approach aims to capture subtle market dynamics often missed by traditional indicators.
Limitations
Performance may vary across different markets and timeframes. The strategy's effectiveness relies on proper calibration of its numerous parameters. Users should thoroughly backtest and forward test before live implementation.
To summarize, the Neural Momentum Strategy represents a sophisticated approach to market analysis. It combines traditional technical indicators with advanced scoring techniques and multi-timeframe analysis. This strategy is designed for traders seeking a data-driven and adaptive method. It aims to identify high-probability trading opportunities across various market conditions.
This Neural Momentum Strategy is for informational and educational purposes only. It should not be considered financial advice. The strategy may exhibit slight repainting behavior due to the nature of multi-timeframe analysis and the use of the request.security() function. Historical values might change as new data becomes available.
Trading carries a high level of risk, and may not be suitable for all investors. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment. Therefore, you should not invest money that you cannot afford to lose.
Past performance is not indicative of future results. The author and TradingView are not responsible for any losses incurred as a result of using this strategy. Always exercise caution when using this or any trading strategy, and thoroughly test it before implementing in live trading scenarios.
Users are solely responsible for any trading decisions they make based on this strategy. It is strongly recommended that you seek advice from an independent financial advisor if you have any doubts.
Larry Connors RSI 3 StrategyThe Larry Connors RSI 3 Strategy is a short-term mean-reversion trading strategy. It combines a moving average filter and a modified version of the Relative Strength Index (RSI) to identify potential buying opportunities in an uptrend. The strategy assumes that a short-term pullback within a long-term uptrend is an opportunity to buy at a discount before the trend resumes.
Components of the Strategy:
200-Day Simple Moving Average (SMA): The price must be above the 200-day SMA, indicating a long-term uptrend.
2-Period RSI: This is a very short-term RSI, used to measure the speed and magnitude of recent price changes. The standard RSI is typically calculated over 14 periods, but Connors uses just 2 periods to capture extreme overbought and oversold conditions.
Three-Day RSI Drop: The RSI must decline for three consecutive days, with the first drop occurring from an RSI reading above 60.
RSI Below 10: After the three-day drop, the RSI must reach a level below 10, indicating a highly oversold condition.
Buy Condition: All the above conditions must be satisfied to trigger a buy order.
Sell Condition: The strategy closes the position when the RSI rises above 70, signaling that the asset is overbought.
Who Was Larry Connors?
Larry Connors is a trader, author, and founder of Connors Research, a firm specializing in quantitative trading research. He is best known for developing strategies that focus on short-term market movements. Connors co-authored several popular books, including "Street Smarts: High Probability Short-Term Trading Strategies" with Linda Raschke, which has become a staple among traders seeking reliable, rule-based strategies. His research often emphasizes simplicity and robust testing, which appeals to both retail and institutional traders.
Scientific Foundations
The Relative Strength Index (RSI), originally developed by J. Welles Wilder in 1978, is a momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is typically used to identify overbought or oversold conditions in an asset. However, the use of a 2-period RSI in Connors' strategy is unconventional, as most traders rely on longer periods, such as 14. Connors' research showed that using a shorter period like 2 can better capture short-term reversals, particularly when combined with a longer-term trend filter such as the 200-day SMA.
Connors' strategies, including this one, are built on empirical research using historical data. For example, in a study of over 1,000 signals generated by this strategy, Connors found that it performed consistently well across various markets, especially when trading ETFs and large-cap stocks (Connors & Alvarez, 2009).
Risks and Considerations
While the Larry Connors RSI 3 Strategy is backed by empirical research, it is not without risks:
Mean-Reversion Assumption: The strategy is based on the premise that markets revert to the mean. However, in strong trending markets, the strategy may underperform as prices can remain oversold or overbought for extended periods.
Short-Term Nature: The strategy focuses on very short-term movements, which can result in frequent trading. High trading frequency can lead to increased transaction costs, which may erode profits.
Market Conditions: The strategy performs best in certain market environments, particularly in stable uptrends. In highly volatile or strongly trending markets, the strategy's performance can deteriorate.
Data and Backtesting Limitations: While backtests may show positive results, they rely on historical data and do not account for future market conditions, slippage, or liquidity issues.
Scientific literature suggests that while technical analysis strategies like this can be effective in certain market conditions, they are not foolproof. According to Lo et al. (2000), technical strategies may show patterns that are statistically significant, but these patterns often diminish once they are widely adopted by traders.
References
Connors, L., & Alvarez, C. (2009). Short-Term Trading Strategies That Work. TradingMarkets Publishing Group.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research
Vix Trading System (VTS)Introduction
The Vix Trading System (VTS) is an algorithm designed specifically for trading the VIX index CFD. The system combines price action and trend analysis to identify optimal entry and exit points for trades. The system is designed to maintain a single position at any given time, ensuring focused and controlled trading activity.
The VIX
The VIX, also known as the "Fear Index," is a popular measure of market volatility. It reflects the market's expectations for volatility over the coming 30 days and is often used as a gauge of investor sentiment. The VIX index is not directly tradable, but there are various financial vehicles, such as VIX futures, options, and CFDs, that allow traders to capitalise on its movements. This strategy is designed to trade the VIX index CFD, a derivative product available through brokers like Capital (used in this backtest). CFDs allow traders to speculate on the price movements of the VIX without owning the underlying asset, offering the potential for profit in both rising and falling markets. The VTS is tailored to leverage the unique characteristics of the VIX, providing traders with a structured approach to navigating the often volatile and unpredictable nature of this index.
Design
The Vix Trading System employs a balanced approach with six long strategies and one short strategy. The long strategies are designed to capitalise on price action patterns that indicate potential price increases, while the short strategy focuses on patterns where the VIX is likely to decline.
While I cannot give you the exact patterns I used to protect my IP, I can give you an example of a price pattern.
Long Entry: close > close and high < low and close >= sma200
These price patterns occur regularly to be traded but not too often to prevent overtrading. By using the price patterns to gauge price action, while using the moving averages to gauge the trend, the system is able to find entry and exit conditions for trading. This blend of price action and trend analysis ensures that the system is robust and adaptable, capable of responding to both short-term fluctuations and longer-term trends in the VIX.
How to Use It
The Vix Trading System is designed with notifications coded into all orders. Traders should set up alerts to notify them of long and short entries, as well as for take profit and stop loss orders for risk management and control. Since the strategy only holds one position at a time, traders can enter trades as soon as an alert is received. This system allows for efficient and timely execution, reducing the need for constant market monitoring.
Backtest
The backtest results for the Vix Trading System provide a valuable guide but should not be taken as a guarantee of future performance. To ensure realistic expectations, a starting capital of $200 was used, which produced a net profit of $18,000 over twelve years. The backtest included a commission of 1.05% of the order size and slippage of 3 ticks to model transaction costs. While these results are encouraging, traders should be aware that real-world trading conditions may differ, and ongoing risk management is essential.
Zero-lag TEMA Crosses Strategy[Pakun]Here's the adjusted strategy description in English, aligned with the house rules:
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### Strategy Name: Zero-lag TEMA Cross Strategy
**Purpose:** This strategy aims to identify entry and exit points in the market using Zero-lag Triple Exponential Moving Averages (TEMA). It focuses on minimizing lag and improving the accuracy of trend-following signals.
### Uniqueness and Usefulness
**Uniqueness:** This strategy employs the less commonly used Zero-lag TEMA, compared to standard moving averages. This unique approach reduces lag and provides more timely signals.
**Usefulness:** This strategy is valuable for traders looking to capture trend reversals or continuations with reduced lag. It has the potential to enhance the profitability and accuracy of trades.
### Entry Conditions
**Long Entry:**
- **Condition:** A crossover occurs where the short-term Zero-lag TEMA surpasses the long-term Zero-lag TEMA.
- **Signal:** A buy signal is generated, indicating a potential uptrend.
**Short Entry:**
- **Condition:** A crossunder occurs where the short-term Zero-lag TEMA falls below the long-term Zero-lag TEMA.
- **Signal:** A sell signal is generated, indicating a potential downtrend.
### Exit Conditions
**Exit Strategy:**
- **Stop Loss:** Positions are closed if the price moves against the trade and hits the predefined stop loss level. The stop loss is set based on recent highs/lows.
- **Take Profit:** Positions are closed when the price reaches the profit target. The profit target is calculated as 1.5 times the distance between the entry price and the stop loss level.
### Risk Management
**Risk Management Rules:**
- This strategy incorporates a dynamic stop loss mechanism based on recent highs/lows over a specified period.
- The take profit level ensures a reward-to-risk ratio of 1.5 times the stop loss distance.
- These measures aim to manage risk and protect capital.
**Account Size:** ¥500,000
**Commissions and Slippage:** 94 pips per trade and 1 pip slippage
**Risk per Trade:** 1% of account equity
### Configurable Options
**Configurable Options:**
- Lookback Period: The number of bars to calculate recent highs/lows.
- Fast Period: Length of the short-term Zero-lag TEMA (69).
- Slow Period: Length of the long-term Zero-lag TEMA (130).
- Signal Display: Option to display buy/sell signals on the chart.
- Bar Color: Option to change bar colors based on trend direction.
### Adequate Sample Size
**Sample Size Justification:**
- To ensure the robustness and reliability of the strategy, it should be tested with a sufficiently long period of historical data.
- It is recommended to backtest across multiple market cycles to adapt to different market conditions.
- This strategy was backtested using 10 days of historical data, including 184 trades.
### Notes
**Additional Considerations:**
- This strategy is designed for educational purposes and should be thoroughly tested in a demo environment before live trading.
- Settings should be adjusted based on the asset being traded and current market conditions.
### Credits
**Acknowledgments:**
- The concept and implementation of Zero-lag TEMA are based on contributions from technical analysts and the trading community.
- Special thanks to John Doe for the TEMA concept.
- Thanks to Zero-lag TEMA Crosses .
- This strategy has been enhanced by adding new filtering algorithms and risk management rules to the original TEMA code.
### Clean Chart Description
**Chart Appearance:**
- This strategy provides a clean and informative chart by plotting Zero-lag TEMA lines and optional entry/exit signals.
- The display of signals and color bars can be toggled to declutter the chart, improving readability and analysis.
Gann Swing Strategy [1 Bar - Multi Layer]Use this Strategy to Fine-tune inputs for your Gann swing strategy.
Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data.
MEANINGFUL DESCRIPTION:
The Gann Swing Chart using the One-Bar type, also known as the Minor Trend Chart, is designed to follow single-bar movements in the market. It helps identify trends by tracking price movements. When the market makes a higher high than the previous bar from a low price, the One-Bar trend line moves up, indicating a new high and establishing the previous low as a One-Bar bottom. Conversely, when the market makes a lower low than the previous bar from a high price, the One-Bar swing line moves down, marking a new low and setting the previous high as a One-Bar top. The crossing of these swing tops and bottoms indicates a change in trend direction.
HOW TO USE THE INDICATOR / Gann-swing Strategy:
The indicator shows 1, 2, and 3-bar swings. The strategy triggers a buy when the price crosses the previously determined high.
HOW TO USE THE STRATEGY:
Strategy to Fine-Tune Inputs for Your Gann Swing Strategy
This strategy allows for the fine-tuning of indicators for one timeframe at a time. Cross-timeframe input fine-tuning is done manually after exporting the chart data.
Meaningful Description:
The Gann Swing Chart using the One-Bar type, also known as the Minor Trend Chart, is designed to follow single-bar movements in the market. It helps identify trends by tracking price movements. When the market makes a higher high than the previous bar from a low price, the One-Bar trend line moves up, indicating a new high and establishing the previous low as a One-Bar bottom. Conversely, when the market makes a lower low than the previous bar from a high price, the One-Bar swing line moves down, marking a new low and setting the previous high as a One-Bar top. The crossing of these swing tops and bottoms indicates a change in trend direction.
How to Use the Indicator / Gann-Swing Strategy:
The indicator shows 1, 2, and 3-bar swings. The strategy triggers a buy when the price crosses the previously determined high.
How to Use the Strategy:
The strategy initiates a buy if the price breaks 1, 2, or 3-bar highs, or any combination thereof. Use the inputs to determine which highs or lows need to be crossed for the strategy to go long or short.
ORIGINALITY & USEFULNESS:
The One-Bar Swing Chart stands out for its simplicity and effectiveness in capturing minor market trends. Developed by meomeo105, this Gann high and low algorithm forms the basis of the strategy. I used my approach to creating strategy out of Gann swing indicator.
DETAILED DESCRIPTION:
What is a Swing Chart?
Swing charts help traders visualize price movements and identify trends by focusing on price highs and lows. They are instrumental in spotting trend reversals and continuations.
What is the One-Bar Swing Chart?
The One-Bar Swing Chart, also known as the Minor Trend Chart, follows single-bar price movements. It plots upward swings from a low price when a higher high is made, and downward swings from a high price when a lower low is made.
Key Features:
Trend Identification : Highlights minor trends by plotting swing highs and lows based on one-bar movements.
Simple Interpretation : Crossing a swing top indicates an uptrend, while crossing a swing bottom signals a downtrend.
Customizable Periods : Users can adjust the period to fine-tune the sensitivity of the swing chart to market movements.
Practical Application:
Bullish Trend : When the One-Bar Swing line moves above a previous swing top, it indicates a bullish trend.
Bearish Trend : When the One-Bar Swing line moves below a previous swing bottom, it signals a bearish trend.
Trend Reversal : Watch for crossings of swing tops and bottoms to detect potential trend reversals.
The One-Bar Swing Chart is a powerful tool for traders looking to capture and understand market trends. By following the simple rules of swing highs and lows, it provides clear and actionable insights into market direction.
Why the Strategy Uses 100% Allocation of a Portfolio:
This strategy allocates 100% of the portfolio to trading this specific pair, which does not mean 100% of all capital but 100% of the allocated trading capital for this pair. The strategy is swing-based and does not use take profit (TP) or stop losses.
TradeBuilderOverview
TradeBuilder is an ever-growing toolbox that lets you combine and compound any number of bundled indicators and algorithms to create a compound strategy. At launch, we're including two Moving Averages (SMA, EMA), RSI, and Stochastic Oscillator, with many more to come. You can use any combination of indicators, be it just one, two, or all.
Key Concepts
Indicator Integration: Tradebuilder allows the use of Moving Averages, RSI, and Stochastic Oscillators, with customizable parameters for each. More indicators to come.
Mode Selection : Choose between Confirm Trend Mode (using indicators to confirm trends) and Momentum Mode (using indicators to spot reversals).
Trade Flexibility : Offers options for both long and short trades, enabling diverse trading strategies.
Customizable Inputs : Easily toggle indicators on or off and adjust specific settings like periods and thresholds.
Signal Generation : Combines multiple conditions to generate entry and exit signals.
Input Parameters:
Moving Average (MA):
use_ma : Enable this to include the Moving Average in your strategy.
ma_cross_type : Choose between "Close/MA" (price crossing the MA) or "MA/MA" (one MA crossing another).
ma_length : Set the period for the primary MA.
ma_type : Choose between "SMA" (Simple Moving Average) or "EMA" (Exponential Moving Average).
ma_length2 : Set the period for the secondary MA if using the "MA/MA" cross type.
ma_type2 : Set the type for the secondary MA.
Relative Strength Index (RSI):
use_rsi : Enable this to include RSI in your strategy.
rsi_length : Set the period for RSI calculation.
rsi_overbought : Define the overbought level.
rsi_oversold : Define the oversold level.
Stochastic Oscillator:
use_stoch : Enable this to include the Stochastic Oscillator in your strategy.
stoch_k : Set the %K period.
stoch_d : Set the %D period.
stoch_smooth : Define the smoothing factor.
stoch_overbought : Set the overbought level.
stoch_oversold : Set the oversold level.
Confirmation or Momentum Mode:
confirm_trend : Set this to true to use RSI and Stochastic Oscillator to confirm trends (long when above overbought, short when below oversold). Set to false to trade on momentum (short when above overbought, long when below oversold).
Tip: When set to false and used with just momentum oscillators like Stochastic or RSI, it's geared toward scalping as it essentially becomes momentum trading.
Trade Directions:
trade_long : Enable to allow long trades.
trade_short : Enable to allow short trades.
Example Strategy on E-mini S&P 500 Index Futures ( CME_MINI:ES1! ), 1-minute Chart
Let’s say you want to create a strategy to go long when:
A 5-period SMA crosses above a 100-period EMA.
RSI is above 20.
The Stochastic Oscillator is above 95.
Trend Confirmation Mode is on.
For short:
A 5-period SMA crosses below a 100-period EMA.
RSI is below 45.
The Stochastic Oscillator is below 5.
Trend Confirmation Mode is on.
Here’s how you would set it up in Tradebuilder:
use_ma = true
ma_cross_type = "MA/MA"
ma_length = 5
ma_type = "SMA"
ma_length2 = 100
ma_type2 = "EMA"
use_rsi = true
rsi_length = 14
rsi_overbought = 20
rsi_oversold = 45
use_stoch = true
stoch_k = 8
stoch_d = 1
stoch_smooth = 1
stoch_overbought = 95
stoch_oversold = 5
confirm_trend = true
trade_long = true
trade_short = false
Alerts
Here is how to set TradeBuilder alerts: open a TradingView chart, attach TradeBuilder, right-click on chart -> Add Alert. Condition: Symbol (e.g. NQ) >> TradeBuilder >> Open-Ended Alert >> Once Per Bar Close.
Development Roadmap
We plan to add many more compoundable indicators to TradeBuilder over the coming months from all walks of technical analysis, including Volume, Volatility, Trend Detection/Validation, Momentum, Divergences, Chart Patterns, Support/Resistance Analysis. etc.
AlgoBuilder [Mean-Reversion] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely and trade based on historical and backtested data using automation.
The main goal is to build profitable mean-reversion strategies that outperform the underlying asset in terms of returns while minimizing drawdown.
For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based moving averages and bands mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability function for traders who want to implement probabilities right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, profit factor, average trade, average risk-reward ratio (RR), and more.
This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading:
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on mean-reversion and risk per trade approach.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 10% of equity to buy the asset)
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What's is FRMA? How does the triple bands work? What are the underlying calculations?
Middle Band (FRMA):
The middle band is the core of the FRMA system. It represents the Fractalyst Moving Average, calculated by identifying the most recent external swing highs and lows in the market structure.
By determining these external swing pivot points, which act as significant highs and lows within the market range, the FRMA provides a unique moving average that adapts to market structure changes.
Upper Band:
The upper band shows the average price of the most recent external swing highs.
External swing highs are identified as the highest points between pivot points in the market structure.
This band helps traders identify potential overbought conditions when prices approach or exceed this upper band.
Lower Band:
The lower band shows the average price of the most recent external swing lows.
External swing lows are identified as the lowest points between pivot points in the market structure.
The script utilizes this band to identify potential oversold conditions, triggering entry signals as prices approach or drop below the lower band.
Adjustments Based on User Inputs:
Users can adjust how the upper and lower bands are calculated based on their preferences:
Upper/Lower: This method calculates the average bands using the prices of external swing highs and lows identified in the market.
Percentage Deviation from FRMA: Alternatively, users can opt to calculate the bands based on a percentage deviation from the middle FRMA. This approach provides flexibility to adjust the width of the bands relative to market conditions and volatility.
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
2. Hunt Entries :
- The strategy identifies a candle that wicks through the lower FRMA band.
- It waits for the next candle to close above the low of the wick candle.
- When this condition is met and the bar is closed, the strategy takes the buy entry.
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 2
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (20) * 2
⍺: ADR period | Σ: ADR Multiplier
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
Application in Strategy (ATR/ADR):
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
Example:
If the entry price is $100, the initial risk is $10, and the RR ratio is 2, the break-even level is $100 + ($10 * 2) = $120.
FRMA Based:
Moves the stop-loss to break-even when the price hits the FRMA level at which the entry was taken.
Calculation:
Break-even level = FRMA level at the entry
Example:
If the FRMA level at entry is $102, the break-even level is set to $102 when the price reaches $102.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
When Both Percentage (%) Based and RR Based Take Profit Levels Are Off:
The script will adjust the take profit level to the higher FRMA band set within user inputs.
Calculation:
Take profit level = Higher FRMA band length/timeframe specified by the user.
This ensures that when neither percentage-based nor risk-to-reward-based take profit methods are enabled, the strategy defaults to using the higher FRMA band as the take profit level, providing a consistent and structured approach to profit-taking.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 55%
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 1%
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Utilizing built-in market structure-based moving averages across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
HMA Crossover 1H with RSI, Stochastic RSI, and Trailing StopThe strategy script provided is a trading algorithm designed to help traders make informed buy and sell decisions based on certain technical indicators. Here’s a breakdown of what each part of the script does and how the strategy works:
Key Components:
Hull Moving Averages (HMA):
HMA 5: This is a Hull Moving Average calculated over 5 periods. HMAs are used to smooth out price data and identify trends more quickly than traditional moving averages.
HMA 20: This is another HMA but calculated over 20 periods, providing a broader view of the trend.
Relative Strength Index (RSI):
RSI 14: This is a momentum oscillator that measures the speed and change of price movements over a 14-period timeframe. It helps identify overbought or oversold conditions in the market.
Stochastic RSI:
%K: This is the main line of the Stochastic RSI, which combines the RSI and the Stochastic Oscillator to provide a more sensitive measure of overbought and oversold conditions. It is smoothed with a 3-period simple moving average.
Trading Signals:
Buy Signal:
Generated when the 5-period HMA crosses above the 20-period HMA, indicating a potential upward trend.
Additionally, the RSI must be below 45, suggesting that the market is not overbought.
The Stochastic RSI %K must also be below 39, confirming the oversold condition.
Sell Signal:
Generated when the 5-period HMA crosses below the 20-period HMA, indicating a potential downward trend.
The RSI must be above 60, suggesting that the market is not oversold.
The Stochastic RSI %K must also be above 63, confirming the overbought condition.
Trailing Stop Loss:
This feature helps protect profits by automatically selling the position if the price moves against the trade by 5%.
For sell positions, an additional trailing stop of 100 points is included.
MA MACD BB BackTesterOverview:
This Pine Script™ code provides a comprehensive backtesting tool that combines Moving Average (MA), Moving Average Convergence Divergence (MACD), and Bollinger Bands (BB). It is designed to help traders analyze market trends and make informed trading decisions by testing various strategies over historical data.
Key Features:
1. Customizable Indicators:
Moving Average (MA): Smooths out price data for clearer trend direction.
MACD: Measures trend momentum through MACD Line, Signal Line, and Histogram.
Bollinger Bands (BB): Identifies overbought or oversold conditions with upper and lower bands.
2. Flexible Trading Direction: Choose between long or short positions to adapt to different market conditions.
3. Risk Management: Efficiently allocate your capital with customizable position sizes.
4. Signal Generation:
Buy Signals: Triggered by crossovers for MACD, MA, and BB.
Sell Signals: Triggered by crossunders for MACD, MA, and BB.
5. Automated Trading: Automatically enter and exit trades based on signal conditions and strategy parameters.
How It Works:
1. Indicator Selection: Select your preferred indicator (MA, MACD, BB) and trading direction (Long/Short).
2. Risk Management Configuration: Set the percentage of capital to allocate per position to manage risk effectively.
3.Signal Detection: The algorithm identifies and plots buy/sell signals directly on the chart based on the chosen indicator.
4. Trade Execution: The strategy automatically enters and exits trades based on signal conditions and configured strategy parameters.
Use Cases:
- Backtesting: Evaluate the effectiveness of trading strategies using historical data to understand potential performance.
- Strategy Development: Customize and expand the strategy to incorporate additional indicators or conditions to fit specific trading styles.
ADDONS That Affect Strategy:
1. Indicator Parameters:
Adjustments to the settings of MACD (e.g., fast length, slow length), MA (e.g., length), and BB (e.g., length, multiplier) will directly impact the detection of signals and the strategy's performance.
2. Trading Direction:
Changing the trading direction (Long/Short) will alter the entry and exit conditions based on the detected signals.
3. Risk Management Settings:
Modifying the position size percentage affects capital allocation and overall risk exposure per trade.
ADDONS That Do Not Affect Strategy:
1. Visual Customizations:
Changes to the color, shape, and style of the plotted lines and signals do not impact the core functionality of the strategy but enhance visual clarity.
2. Text and Labels:
Modifying text labels for the signals (such as renaming "Buy MACD" to "MACD Buy Signal") is purely cosmetic and does not influence the strategy’s logic or outcomes.
Notes:
- Customization: The indicator is highly customizable to fit various trading styles and market conditions.
- Risk Management: Adjust position sizes and risk parameters according to your risk tolerance and account size.
- Optimization: Regularly backtest and optimize parameters to adapt to changing market dynamics for better performance.
Getting Started:
-Add the script to your chart.
-Adjust the input parameters to suit your analysis preferences.
-Observe the marked buy and sell signals on your chart to make informed trading decisions.
RunRox - Backtesting System (ASMC)Introducing RunRox - Backtesting System (ASMC), a specially designed backtesting system built on the robust structure of our Advanced SMC indicator. This innovative tool evaluates various Smart Money Concept (SMC) trading setups and serves as an automatic optimizer, displaying which entry and exit points have historically shown the best results. With cutting-edge technology, RunRox - Backtesting System (ASMC) provides you with effective strategies, maximizing your trading potential and taking your trading to the next level
🟠 HOW OUR BACKTESTING SYSTEM WORKS
Our backtesting system for the Advanced SMC (ASMC) indicator is meticulously designed to provide traders with a thorough analysis of their Smart Money Concept (SMC) strategies. Here’s an overview of how it works:
🔸 Advanced SMC Structure
Our ASMC indicator is built upon an enhanced SMC structure that integrates the Institutional Distribution Model (IDM), precise retracements, and five types of order blocks (CHoCH OB, IDM OB, Local OB, BOS OB, Extreme OB). These components allow for a detailed understanding of market dynamics and the identification of key trading opportunities.
🔸 Data Integration and Analysis
1. Historical Data Testing:
Our system tests various entry and exit points using historical market data.
The ASMC indicator is used to simulate trades based on predefined SMC setups, evaluating their effectiveness over a specified time period.
Traders can select different parameters such as entry points, stop-loss, and take-profit levels to see how these setups would have performed historically.
2. Entry and Exit Events:
The backtester can simulate trades based on 12 different entry events, 14 target events, and 14 stop-loss events, providing a comprehensive testing framework.
It allows for testing with multiple combinations of entry and exit strategies, ensuring a robust evaluation of trading setups.
3. Order Block Sensitivity:
The system uses the sensitivity settings from the ASMC indicator to determine the most relevant order blocks and fair value gaps (FVGs) for entry and exit points.
It distinguishes between different types of order blocks, helping traders identify strong institutional zones versus local zones.
🔸 Optimization Capabilities
1. Auto-Optimizer:
The backtester includes an auto-optimizer feature that evaluates various setups to find those with the best historical performance.
It automatically adjusts parameters to identify the most effective strategies for both trend-following and counter-trend trading.
2. Stop Loss and Take Profit Optimization:
It optimizes stop-loss and take-profit levels by testing different settings and identifying those that provided the best historical results.
This helps traders refine their risk management and maximize potential returns.
3. Trailing Stop Optimization:
The system also optimizes trailing stops, ensuring that traders can maximize their profits by adjusting their stops dynamically as the market moves.
🔸 Comprehensive Reporting
1. Performance Metrics:
The backtesting system provides detailed reports, including key performance metrics such as Net Profit, Win Rate, Profit Factor, and Max Drawdown.
These metrics help traders understand the historical performance of their strategies and make data-driven decisions.
2. Flexible Settings:
Traders can adjust initial balance, commission rates, and risk per trade settings to simulate real-world trading conditions.
The system supports testing with different leverage settings, allowing for realistic assessments even with tight stop-loss levels.
🔸 Conclusion
The RunRox Backtesting System (ASMC) is a powerful tool for traders seeking to validate and optimize their SMC strategies. By leveraging historical data and sophisticated optimization algorithms, it provides insights into the most effective setups, enhancing trading performance and decision-making.
🟠 HERE ARE THE AVAILABLE FEATURES
Historical backtesting for any setup – Select any entry point, exit point, and various stop-loss options to see the results of your setup on historical data.
Auto-optimizer for finding the best setups – The indicator displays settings that have shown the best results historically, providing valuable insights.
Auto-optimizer for counter-trend setups – Discover entry and exit points for counter-trend trading based on historical performance.
Auto-optimizer for stop-loss – The indicator shows stop-loss points that have been most effective historically.
Auto-optimizer for take-profit – The indicator identifies take-profit points that have performed well in historical trading data.
Auto-optimizer for trailing stop – The indicator presents trailing stop settings that have shown the best historical results.
And much more within our indicator, all of which we will cover in this post. Next, we will showcase the possible entry points, targets, and stop-loss options available for testing your strategies
🟠 ENTRY SETTINGS
12 Event Triggers for Trade Entry
Extr. ChoCh OB
Extr. ChoCh FVG
ChoCh
ChoCh OB
ChoCh FVG
IDM OB
IDM FVG
BoS FVG
BoS OB
BoS
Extr. BoS FVG
Extr. BoS OB
3 Trade Direction Options
Long Only: Enter long positions only
Short Only: Enter short positions only
Long and Short: Enter both long and short positions based on trend
3 Levels for Order Block/FVG Entries
Beginning: Enter the trade at the first touch of the Order Block/FVG
Middle: Enter the trade when the middle of the Order Block/FVG is reached
End: Enter the trade upon full filling of the Order Block/FVG
*Three levels work only for Order Blocks and FVG. For trade entries based on BOS or CHoCH, these settings do not apply as these parameters are not available for these types of entries
You can choose any combination of trade entries imaginable.
🟠 TARGET SETTINGS
14 Target Events, Including Fixed % and Fixed RR (Risk/Reward):
Fixed - % change in price
Fixed RR - Risk Reward per trade
Extr. ChoCh OB
Extr. ChoCh FVG
ChoCh
ChoCh OB
ChoCh FVG
IDM OB
IDM FVG
BoS FVG
BoS OB
BoS
Extr. BoS FVG
Extr. BoS OB
3 Levels of Order Block/FVG for Target
Beginning: Close the trade at the first touch of your target.
Middle: Close the trade at the midpoint of your chosen target.
End: Close the trade when your target is fully filled.
Customizable Parameters
Easily set your Fixed % and Fixed RR targets with a user-friendly input field. This field works only for the Fixed and Fixed RR entry parameters. When selecting a different entry point, this field is ignored
Choose any combination of target events to suit your trading strategy.
🟠 STOPLOSS SETTINGS
14 Possible StopLoss Events Including Entry Orderblock/FVG
Fixed - Fix the loss on the trade when the price moves by N%
Entry Block
Extr. ChoCh OB
Extr. ChoCh FVG
ChoCh
ChoCh OB
ChoCh FVG
IDM OB
IDM FVG
BoS FVG
BoS OB
BoS
Extr. BoS FVG
Extr. BoS OB
3 Levels for Order Blocks/FVG Exits
Beginning: Exit the trade at the first touch of the order block/FVG.
Middle: Exit the trade at the middle of the order block/FVG.
End: Exit the trade at the full completion of the order block/FVG.
Dedicated Field for Setting Fixed % Value
Set a fixed % value in a dedicated field for the Fixed parameter. This field works only for the Fixed parameter. When selecting other exit parameters, this field is ignored.
🟠 ADDITIONAL SETTINGS
Trailing Stop, %
Set a Trailing Stop as a percentage of your trade to potentially increase profit based on historical data.
Move SL to Breakeven, bars
Move your StopLoss to breakeven after exiting the entry zone for a specified number of bars. This can enhance your potential WinRate based on historical performance.
Skip trade if RR less than
This feature allows you to skip trades where the potential Risk-to-Reward ratio is less than the number set in this field.
🟠 EXAMPLE OF MANUAL SETUP
For example, let me show you how it works on the chart. You select entry parameters, stop loss parameters, and take profit parameters for your trades, and the strategy automatically tests this setup on historical data, allowing you to see the results of this strategy.
In the screenshot above, the parameters were as follows:
Trade Entry: CHoCH OB (Beginning)
Stop Loss: Entry Block
Take Profit: Break of BOS
The indicator will automatically test all possible trades on the chart and display the results for this setup.
🟠 AUTO OPTIMIZATION SETTINGS
In the screenshot above, you can see the optimization table displaying various entry points, exits, and stop-loss settings, along with their historical performance results and other parameters. This feature allows you to identify trading setups that have shown the best historical outcomes.
This functionality will enhance your trading approach, providing you with valuable insights based on historical data. You’ll be aware of the Smart Money Concept settings that have historically worked best for any specific chart and timeframe.
Our indicator includes various optimization options designed to help you find the most effective settings based on historical data. There are 5 optimization modes, each offering unique benefits for every trader
Trend Entry - Optimization of the best settings for trend-following trades. The strategy will enter trades only in the direction of the trend. If the trend is upward, it will look for long entry points and vice versa.
Counter Trend Entry - Finding setups against the trend. If the trend is upward, the script will search for short entry points. This is the opposite of trend entry optimization.
Stop Loss - Identifying stop-loss points that showed the best historical performance for the specific setup you have configured. This helps in finding effective exit points to minimize losses.
Take Profit - Determining targets for the configured setup based on historical performance, helping to identify potentially profitable take profit levels.
Trailing Stop - Finding optimal percentages for the trailing stop function based on historical data, which can potentially increase the profit of your trades.
Ability to set parameters for auto-optimization within a specified range. For example, if you choose FixRR TP from 1 to 10, the indicator will automatically test all possible Risk Reward Take Profit variations from 1 to 10 and display the results for each parameter individually.
Ability to set initial deposit parameters, position commissions, and risk per trade as a fixed percentage or fixed amount. Additionally, you can set the maximum leverage for a trade.
There are times when the stop loss is very close to the entry point, and adhering to the risk per trade values set in the settings may not allow for such a loss in any situation. That’s why we added the ability to set the maximum possible leverage, allowing you to test your trading strategy even with very tight stop losses.
Duplicated Smart Money Structure settings from our Advanced SMC indicator that you can adjust to match your trading style flexibly. All these settings will be taken into account during the optimization process or when manually calculating settings.
Additionally, you can test your strategy based on higher timeframe order blocks. For example, you can test a strategy on a 1-minute chart while displaying order blocks from a 15-minute timeframe. The auto-optimizer will consider all these parameters, including higher timeframe order blocks, and will enter trades based on these order blocks.
Highly flexible dashboard and results optimization settings allow you to display the tables you need and sort results by six different criteria: Profit Factor, Profit, Winrate, Max Drawdown, Wins, and Trades. This enables you to find the exact setup you desire, based on these comprehensive data points.
🟠 ALERT CUSTOMIZATION
With this indicator, you can set up buy and sell alerts based on the test results, allowing you to create a comprehensive trading strategy. This feature enables you to receive real-time signals, making it a powerful tool for implementing your trading strategies.
🟠 STRATEGY PROPERTIES
For backtesting, we used realistic initial data for entering trades, such as:
Starting balance: $1000
Commission: 0.01%
Risk per trade: 1%
To ensure realistic data, we used the above settings. We offer two methods for calculating your order size, and in our case, we used a 1% risk per trade. Here’s what it means:
Risk per trade: This is the maximum loss from your deposit if the trade goes against you. The trade volume can change depending on your stop-loss distance from the entry point. Here’s the formula we use to calculate the possible volume for a single trade:
1. quantity = percentage_risk * balance / loss_per_1_contract (incl. fee)
Then, we calculate the maximum allowed volume based on the specified maximum leverage:
2. max_quantity = maxLeverage * balance / entry_price
3. If quantity < max_quantity, meaning the leverage is less than the maximum allowed, we keep quantity. If quantity > max_quantity, we use max_quantity (the maximum allowed volume according to the set leverage).
This way, depending on the stop-loss distance, the position size can vary and be up to 100% of your deposit, but the loss in each trade will not exceed the set percentage, which in our case is 1% for this backtest. This is a standard risk calculation method based on your stop-loss distance.
🔸 Statistical Significance of Trade Data
In our strategy, you may notice there weren’t enough trades to form statistically significant data. This is inherent to the Smart Money Concept (SMC) strategy, where the focus is not on the number of trades but rather on the risk-to-reward ratio per trade. In SMC strategies, it’s crucial to avoid taking numerous uncertain setups and instead perform a comprehensive analysis of the market situation.
Therefore, our strategy results show fewer than 100 trades. It’s important to understand that this small sample size isn’t statistically significant and shouldn’t be relied upon for strategy analysis. Backtesting with a small number of trades should not be used to draw conclusions about the effectiveness of a strategy.
🔸 Versatile Use Cases
The methods of using this indicator are numerous, ranging from identifying potentially the best-performing order blocks on the chart to creating a comprehensive trading strategy based on the data provided by our indicator. We believe that every trader will find a valuable application for this tool, enhancing their entry and exit points in trades.
Disclaimer
Past performance is not indicative of future results. The results shown by this indicator do not guarantee similar outcomes in the future. Use this tool as part of a comprehensive trading strategy, considering all market conditions and risks.
How to access
For access to this indicator, please read the author’s instructions below this post
Support and Resistance RoboTBINANCE:ETHUSDT
Algorithmic Trader
Coded by Pinescript V5
Best strategy you can find in trading cryptocurrencies
With the ability to adjust settings
Profitable each year
This strategy uses supports and resistances combined with ichimoku
This automated strategy trades on ETH/USD
(ranks second in cryptocurrency marketcap).
We have had real trade results for more than 10
months and backtesting for more than 8 years.
The results are for mid-risk
settings. If the settings are changed, you can
potentially achieve more profit or a lower
drawdown.
Default settings : EMA,EMA,14,1.5,1.5,23,0.5,31,10,W
“An investment in knowledge pays the best interest”
Benjamin Franklin
If you're interested, we can
provide you with access to
examine the strategy.
Thanks!
Price Based Z-Trend - Strategy [presentTrading]█ Introduction and How it is Different
Z-score: a statistical measurement of a score's relationship to the mean in a group of scores.
Simple but effective approach.
The "Price Based Z-Trend - Strategy " leverages the Z-score, a statistical measure that gauges the deviation of a price from its moving average, normalized against its standard deviation. This strategy stands out due to its simplicity and effectiveness, particularly in markets where price movements often revert to a mean. Unlike more complex systems that might rely on a multitude of indicators, the Z-Trend strategy focuses on clear, statistically significant price movements, making it ideal for traders who prefer a streamlined, data-driven approach.
BTCUSD 6h LS Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Z-score
"Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. A Z-score of 1.0 would indicate a value that is one standard deviation from the mean. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean."
The Z-score is central to this strategy. It is calculated by taking the difference between the current price and the Exponential Moving Average (EMA) of the price over a user-defined length, then dividing this by the standard deviation of the price over the same length:
z = (x - μ) /σ
Local
🔶 Trading Signals
Trading signals are generated based on the Z-score crossing predefined thresholds:
- Long Entry: When the Z-score crosses above the positive threshold.
- Long Exit: When the Z-score falls below the negative threshold.
- Short Entry: When the Z-score falls below the negative threshold.
- Short Exit: When the Z-score rises above the positive threshold.
█ Trade Direction
The strategy allows users to select their preferred trading direction through an input option.
█ Usage
To use this strategy effectively, traders should first configure the Z-score thresholds according to their risk tolerance and market volatility. It's also crucial to adjust the length for the EMA and standard deviation calculations based on historical performance and the expected "noise" in price data.
The strategy is designed to be flexible, allowing traders to refine settings to better capture profitable opportunities in specific market conditions.
█ Default Settings
- Trade Direction: Both
- Standard Deviation Length: 100
- Average Length: 100
- Threshold for Z-score: 1.0
- Bar Color Indicator: Enabled
These settings offer a balanced starting point but can be customized to suit various trading styles and market environments. The strategy's parameters are designed to be adjusted as traders gain experience and refine their approach based on ongoing market analysis.
Z-score is a must-learn approach for every algorithmic trader.
