Ichimoku Crosses_RSI_AITIchimoku Crosser_RSI_AIT
Overview
The "Ichimoku Cloud Crosses_AIT" strategy is a technical trading strategy that combines the Ichimoku Cloud components with the Relative Strength Index (RSI) to generate trade signals. This strategy leverages the crossovers of the Tenkan-sen and Kijun-sen lines of the Ichimoku Cloud, along with RSI levels, to identify potential entry and exit points for long and short trades. This guide explains the strategy components, conditions, and how to use it effectively in your trading.
1. Strategy Parameters
User Inputs
Tenkan-sen Period (tenkanLength): Default value is 21. This is the period used to calculate the Tenkan-sen line (conversion line) of the Ichimoku Cloud.
Kijun-sen Period (kijunLength): Default value is 120. This is the period used to calculate the Kijun-sen line (base line) of the Ichimoku Cloud.
Senkou Span B Period (senkouBLength): Default value is 52. This is the period used to calculate the Senkou Span B line (leading span B) of the Ichimoku Cloud.
RSI Period (rsiLength): Default value is 14. This period is used to calculate the Relative Strength Index (RSI).
RSI Long Entry Level (rsiLongLevel): Default value is 60. This level indicates the minimum RSI value for a long entry signal.
RSI Short Entry Level (rsiShortLevel): Default value is 40. This level indicates the maximum RSI value for a short entry signal.
2. Strategy Components
Ichimoku Cloud
Tenkan-sen: A short-term trend indicator calculated as the simple moving average (SMA) of the highest high and the lowest low over the Tenkan-sen period.
Kijun-sen: A medium-term trend indicator calculated as the SMA of the highest high and the lowest low over the Kijun-sen period.
Senkou Span A: Calculated as the average of the Tenkan-sen and Kijun-sen, plotted 26 periods ahead.
Senkou Span B: Calculated as the SMA of the highest high and lowest low over the Senkou Span B period, plotted 26 periods ahead.
Chikou Span: The closing price plotted 26 periods behind.
Relative Strength Index (RSI)
RSI: A momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is used to identify overbought or oversold conditions.
3. Entry and Exit Conditions
Entry Conditions
Long Entry:
The Tenkan-sen crosses above the Kijun-sen (bullish crossover).
The RSI value is greater than or equal to the rsiLongLevel.
Short Entry:
The Tenkan-sen crosses below the Kijun-sen (bearish crossover).
The RSI value is less than or equal to the rsiShortLevel.
Exit Conditions
Exit Long Position: The Tenkan-sen crosses below the Kijun-sen.
Exit Short Position: The Tenkan-sen crosses above the Kijun-sen.
4. Visual Representation
Tenkan-sen Line: Plotted on the chart. The color changes based on its relation to the Kijun-sen (green if above, red if below) and is displayed with a line width of 2.
Kijun-sen Line: Plotted as a white line with a line width of 1.
Entry Arrows:
Long Entry: Displayed as a yellow triangle below the bar.
Short Entry: Displayed as a fuchsia triangle above the bar.
5. How to Use
Apply the Strategy: Apply the "Ichimoku Cloud Crosses_AIT" strategy to your chart in TradingView.
Configure Parameters: Adjust the strategy parameters (Tenkan-sen, Kijun-sen, Senkou Span B, and RSI settings) according to your trading preferences.
Interpret the Signals:
Long Entry: A yellow triangle appears below the bar when a long entry signal is generated.
Short Entry: A fuchsia triangle appears above the bar when a short entry signal is generated.
Monitor Open Positions: The strategy automatically exits positions based on the defined conditions.
Backtesting and Live Trading: Use the strategy for backtesting and live trading. Adjust risk management settings in the strategy properties as needed.
Conclusion
The "Ichimoku Cloud Crosses_AIT" strategy uses Ichimoku Cloud crossovers and RSI to generate trading signals. This strategy aims to capture market trends and potential reversals, providing a structured way to enter and exit trades. Make sure to backtest and optimize the strategy parameters to suit your trading style and market conditions before using it in a live trading environment.
Cari dalam skrip untuk "momentum"
ETH Signal 15m
This strategy uses the Supertrend indicator combined with RSI to generate buy and sell signals, with stop loss (SL) and take profit (TP) conditions based on ATR (Average True Range). Below is a detailed explanation of each part:
1. General Information BINANCE:ETHUSDT.P
Strategy Name: "ETH Signal 15m"
Designed for use on the 15-minute time frame for the ETH pair.
Default capital allocation is 15% of total equity for each trade.
2. Backtest Period
start_time and end_time: Define the start and end time of the backtest period.
start_time = 2024-08-01: Start date of the backtest.
end_time = 2054-01-01: End date of the backtest.
The strategy will only run when the current time falls within this specified range.
3. Supertrend Indicator
Supertrend is a trend-following indicator that provides buy or sell signals based on the direction of price changes.
factor = 2.76: The multiplier used in the Supertrend calculation (increasing this value makes the Supertrend less sensitive to price movements).
atrPeriod = 12: Number of periods used to calculate ATR.
Output:
direction: Determines the buy/sell direction based on Supertrend.
If direction decreases, it signals a buy (Long).
If direction increases, it signals a sell (Short).
4. RSI Indicator
RSI (Relative Strength Index) is a momentum indicator, often used to identify overbought or oversold conditions.
rsiLength = 12: Number of periods used to calculate RSI.
rsiOverbought = 70: RSI level considered overbought.
rsiOversold = 30: RSI level considered oversold.
5. Entry Conditions
Long Entry:
Supertrend gives a buy signal (ta.change(direction) < 0).
RSI must be below the overbought level (rsi < rsiOverbought).
Short Entry:
Supertrend gives a sell signal (ta.change(direction) > 0).
RSI must be above the oversold level (rsi > rsiOversold).
The strategy will only execute trades if the current time is within the backtest period (in_date_range).
6. Stop Loss (SL) and Take Profit (TP) Conditions
ATR (Average True Range) is used to calculate the distance for Stop Loss and Take Profit based on price volatility.
atr = ta.atr(atrPeriod): ATR is calculated using 12 periods.
Stop Loss and Take Profit are calculated as follows:
Long Trade:
Stop Loss: Set at close - 4 * atr (current price minus 4 times the ATR).
Take Profit: Set at close + 2 * atr (current price plus 2 times the ATR).
Short Trade:
Stop Loss: Set at close + 4 * atr (current price plus 4 times the ATR).
Take Profit: Set at close - 2.237 * atr (current price minus 2.237 times the ATR).
Summary:
This strategy enters a Long trade when the Supertrend indicates an upward trend and RSI is not in the overbought region. Conversely, a Short trade is entered when Supertrend signals a downtrend, and RSI is not oversold.
The trade is exited when the price reaches the Stop Loss or Take Profit levels, which are determined based on price volatility (ATR).
Disclaimer:
The content provided in this strategy is for informational and educational purposes only. It is not intended as financial, investment, or trading advice. Trading in cryptocurrency, stocks, or any financial markets involves significant risk, and you may lose more than your initial investment. Past performance is not indicative of future results, and no guarantee of profit can be made. You should consult with a professional financial advisor before making any investment decisions. The creator of this strategy is not responsible for any financial losses or damages incurred as a result of following this strategy. All trades are executed at your own risk.
Connors VIX Reversal III invented by Dave LandryThis strategy is based on trading signals derived from the behavior of the Volatility Index (VIX) relative to its 10-day moving average. The rules are split into buying and selling conditions:
Buy Conditions:
The VIX low must be above its 10-day moving average.
The VIX must close at least 10% above its 10-day moving average.
If both conditions are met, a buy signal is generated at the market's close.
Sell Conditions:
The VIX high must be below its 10-day moving average.
The VIX must close at least 10% below its 10-day moving average.
If both conditions are met, a sell signal is generated at the market's close.
Exit Conditions:
For long positions, the strategy exits when the VIX trades intraday below its previous day’s 10-day moving average.
For short positions, the strategy exits when the VIX trades intraday above its previous day’s 10-day moving average.
This strategy is primarily a mean-reversion strategy, where the market is expected to revert to a more normal state after the VIX exhibits extreme behavior (i.e., large deviations from its moving average).
About Dave Landry
Dave Landry is a well-known figure in the world of trading, particularly in technical analysis. He is an author, trader, and educator, best known for his work on swing trading strategies. Landry focuses on trend-following and momentum-based techniques, teaching traders how to capitalize on shorter-term price swings in the market. He has written books like "Dave Landry on Swing Trading" and "The Layman's Guide to Trading Stocks," which emphasize practical, actionable trading strategies.
About Connors Research
Connors Research is a financial research firm known for its quantitative research in financial markets. Founded by Larry Connors, the firm specializes in developing high-probability trading systems based on historical market behavior. Connors’ work is widely respected for its data-driven approach, including systems like the RSI(2) strategy, which focuses on short-term mean reversion. The firm also provides trading education and tools for institutional and retail traders alike, emphasizing strategies that can be backtested and quantified.
Risks of the Strategy
While this strategy may appear to offer promising opportunities to exploit extreme VIX movements, it carries several risks:
Market Volatility: The VIX itself is a measure of market volatility, meaning the strategy can be exposed to sudden and unpredictable market swings. This can result in whipsaws, where positions are opened and closed in rapid succession due to sharp reversals in the VIX.
Overfitting: Strategies based on specific conditions like the VIX closing 10% above or below its moving average can be subject to overfitting, meaning they work well in historical tests but may underperform in live markets. This is a common issue in quantitative trading systems that are not adaptable to changing market conditions .
Mean-Reversion Assumption: The core assumption behind this strategy is that markets will revert to their mean after extreme movements. However, during periods of sustained trends (e.g., market crashes or rallies), this assumption may break down, leading to prolonged drawdowns.
Liquidity and Slippage: Depending on the asset being traded (e.g., S&P 500 futures, ETFs), liquidity issues or slippage could occur when executing trades at market close, particularly in volatile conditions. This could increase costs or worsen trade execution.
Scientific Explanation of the Strategy
The VIX is often referred to as the "fear gauge" because it measures the market's expectations of volatility based on options prices. Research has shown that the VIX tends to spike during periods of market stress and revert to lower levels when conditions stabilize . Mean reversion strategies like this one assume that extreme VIX levels are unsustainable in the long run, which aligns with findings from academic literature on volatility and market behavior.
Studies have found that the VIX is inversely correlated with stock market returns, meaning that higher VIX levels often correspond to lower stock prices and vice versa . By using the VIX’s relationship with its 10-day moving average, this strategy aims to capture reversals in market sentiment. The 10% threshold is designed to identify moments when the VIX is significantly deviating from its norm, signaling a potential reversal.
However, academic research also highlights the limitations of relying on the VIX alone for trading signals. The VIX does not predict market direction, only volatility, meaning that it cannot indicate the magnitude of price movements . Furthermore, extreme VIX levels can persist longer than expected, particularly during financial crises.
In conclusion, while the strategy is grounded in well-established financial principles (e.g., mean reversion and the relationship between volatility and market performance), it carries inherent risks and should be used with caution. Backtesting and careful risk management are essential before applying this strategy in live markets.
Bidirectional Trend Reversal StrategyBidirectional Trend Reversal Strategy
This strategy aims to identify potential trend reversals and execute trades accordingly, focusing on both long and short positions. It uses a crossover of the Simple Moving Average (SMA) with price action as a key signal. When the price crosses above the SMA and the previous period was bearish (closed lower than it opened), the script opens a long position ("o-Long"). The exit ("e-Long") occurs when the target or stop-loss levels are hit, which are dynamically set using the ATR (Average True Range).
For short trades, when the price crosses below the SMA and the previous period was bullish (closed higher than it opened), the script opens a short position ("o-Short"). The exit ("e-Short") follows the same ATR-based logic for stop-loss and take-profit.
All settings, including SMA and ATR parameters, are fully customizable, allowing users to adapt the strategy to different market conditions and personal trading preferences.
This approach provides a systematic way to capture trend reversals and manage trades with clear entry and exit signals based on market momentum and volatility.
Example Setup:
Market: Forex
Pair: USD/GBP
Order size: 100,000 Contracts (1 Lot)
Timeframe: 15 minutes
SMA: 93
ATR Length: 15
Stop-Loss (ATR Multiplier): 7
Take-Profit Multiplier: 2
Experiment with different settings to achieve the best results for your trading style and market conditions.
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
Black-Scholes option price model & delta hedge strategyBlack-Scholes Option Pricing Model Strategy
The strategy is based on the Black-Scholes option pricing model and allows the calculation of option prices, various option metrics (the Greeks), and the creation of synthetic positions through delta hedging.
ATTENTION!
Trading derivative financial instruments involves high risks. The author of the strategy is not responsible for your financial results! The strategy is not self-sufficient for generating profit! It is created exclusively for constructing a synthetic derivative financial instrument. Also, there might be errors in the script, so use it at your own risk! I would appreciate it if you point out any mistakes in the comments! I would be even more grateful if you send the corrected code!
Application Scope
This strategy can be used for delta hedging short positions in sold options. For example, suppose you sold a call option on Bitcoin on the Deribit exchange with a strike price of $60,000 and an expiration date of September 27, 2024. Using this script, you can create a delta hedge to protect against the risk of loss in the option position if the price of Bitcoin rises.
Another example: Suppose you use staking of altcoins in your strategies, for which options are not available. By using this strategy, you can hedge the risk of a price drop (Put option). In this case, you won't lose money if the underlying asset price increases, unlike with a short futures position.
Another example: You received an airdrop, but your tokens will not be fully unlocked soon. Using this script, you can fully hedge your position and preserve their dollar value by the time the tokens are fully unlocked. And you won't fear the underlying asset price increasing, as the loss in the event of a price rise is limited to the option premium you will pay if you rebalance the portfolio.
Of course, this script can also be used for simple directional trading of momentum and mean reversion strategies!
Key Features and Input Parameters
1. Option settings:
- Style of option: "European vanilla", "Binary", "Asian geometric".
- Type of option: "Call" (bet on the rise) or "Put" (bet on the fall).
- Strike price: the option contract price.
- Expiration: the expiry date and time of the option contract.
2. Market statistic settings:
- Type of price source: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 (using hl2, hlc3, ohlc4, hlcc4 allows smoothing the price in more volatile series).
- Risk-free return symbol: the risk-free rate for the market where the underlying asset is traded. For the cryptocurrency market, the return on the funding rate arbitrage strategy is accepted (a special function is written for its calculation based on the Premium Price).
- Volatility calculation model: realized (standard deviation over a moving period), implied (e.g., DVOL or VIX), or custom (you can specify a specific number in the field below). For the cryptocurrency market, the calculation of implied volatility is implemented based on the product of the realized volatility ratio of the considered asset and Bitcoin to the Bitcoin implied volatility index.
- User implied volatility: fixed implied volatility (used if "Custom" is selected in the "Volatility Calculation Method").
3. Display settings:
- Choose metric: what to display on the indicator scale – the price of the underlying asset, the option price, volatility, or Greeks (all are available).
- Measure: bps (basis points), percent. This parameter allows choosing the unit of measurement for the displayed metric (for all except the Greeks).
4. Trading settings:
- Hedge model: None (do not trade, default), Simple (just open a position for the full volume when the strike price is crossed), Synthetic option (creating a synthetic option based on the Black-Scholes model).
- Position side: Long, Short.
- Position size: the number of units of the underlying asset needed to create the option.
- Strategy start time: the moment in time after which the strategy will start working to create a synthetic option.
- Delta hedge interval: the interval in minutes for rebalancing the portfolio. For example, a value of 5 corresponds to rebalancing the portfolio every 5 minutes.
Post scriptum
My strategy based on the SegaRKO model. Many thanks to the author! Unfortunately, I don't have enough reputation points to include a link to the author in the description. You can find the original model via the link in the code, as well as through the search indicators on the charts by entering the name: "Black-Scholes Option Pricing Model". I have significantly improved the model: the calculation of volatility, risk-free rate and time value of the option have been reworked. The code performance has also been significantly optimized. And the most significant change is the execution, with which you can now trade using this script.
Rsi Long-Term Strategy [15min]Hello, I would like to present to you The "RSI Long-Term Strategy" for 15min tf
The "RSI Long-Term Strategy " is designed for traders who prefer a combination of momentum and trend-following techniques. The strategy focuses on entering long positions during significant market corrections within an overall uptrend, confirmed by both RSI and volume. The use of long-term SMAs ensures that trades are made in line with the broader market trend. The stop-loss feature provides risk management by limiting losses on trades that do not perform as expected. This strategy is particularly well-suited for longer-term traders who monitor 15-minute charts but look for substantial trend reversals or continuations.
Indicators and Parameters:
Relative Strength Index (RSI):
- The RSI is calculated using a 10-period length. It measures the magnitude of recent price changes to evaluate overbought or oversold conditions. The script defines oversold conditions when the RSI is at or below 30 and overbought conditions when the RSI is at or above 70.
Volume Condition:
-The strategy incorporates a volume condition where the current volume must be greater than 2.5 times the 20-period moving average of volume. This is used to confirm the strength of the price movement.
Simple Moving Averages (SMA):
- The strategy uses two SMAs: SMA1 with a length of 250 periods and SMA2 with a length of 500 periods. These SMAs help identify long-term trends and generate signals based on their crossover.
Strategy Logic:
Entry Logic:
A long position is initiated when all the following conditions are met:
The RSI indicates an oversold condition (RSI ≤ 30).
SMA1 is above SMA2, indicating an uptrend.
The volume condition is satisfied, confirming the strength of the signal.
Exit Logic:
The strategy closes the long position when SMA1 crosses under SMA2, signaling a potential end of the uptrend (a "Death Cross").
Stop-Loss:
A stop-loss is set at 5% below the entry price to manage risk and limit potential losses.
Buy and sell signals are highlighted with circles below or above bars:
Green Circle : Buy signal when RSI is oversold, SMA1 > SMA2, and the volume condition is met.
Red Circle : Sell signal when RSI is overbought, SMA1 < SMA2, and the volume condition is met.
Black Cross: "Death Cross" when SMA1 crosses under SMA2, indicating a potential bearish signal.
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
I hope the strategy will be helpful, as always, best regards and safe trades
;)
Scalper Bot [SMRT Algo]The SMRT Algo Bot is a trading strategy designed for use on TradingView, enabling traders to backtest and refine their strategies with precision. This bot is built to provide key performance metrics through TradingView’s strategy tester feature, offering insights such as net profit, maximum drawdown, profit factor, win rate, and more.
The SMRT Algo Bot is versatile, allowing traders to execute either pro-trend or contrarian strategies, each with customizable parameters to suit individual trading styles.
Traders can automate the bot to their brokerage platform via webhooks and use third-party software to facilitate this.
Core Features:
Backtesting Capabilities: The SMRT Algo Bot leverages TradingView’s powerful strategy tester, allowing traders to backtest their strategies over historical data. This feature is crucial for assessing the viability of a strategy before deploying it in live markets. By providing metrics such as net profit, maximum drawdown, profit factor, and win rate, traders can gain a comprehensive understanding of their strategy's performance, helping them to make informed decisions about potential adjustments or optimizations.
Advanced Take Profit and Stop Loss Methods: The SMRT Algo Bot offers multiple methods for setting Take Profit (TP) and Stop Loss (SL) levels, providing flexibility to match different market conditions and trading strategies.
Take Profit Methods:
- Normal (Percent-based): Traders can set their TP levels as a percentage. This method adjusts the TP dynamically based on market volatility, allowing for more responsive profit-taking in volatile markets.
- Donchian Channel: Alternatively, the bot can use the Donchian Channel to set TP levels, which is particularly useful in trend-following strategies. The Donchian Channel identifies the highest high and lowest low over a specified period, providing a clear target for profit-taking when prices reach extreme levels.
Stop Loss Methods:
- Percentage-Based Stop Loss: This method allows traders to set a fixed percentage of the entry price as the stop loss. It provides a straightforward, static risk management approach that is easy to implement.
- Normal (Percent-based): Traders can set their SL levels as a percentage. This method adjusts the SL dynamically based on market volatility, allowing for more responsive profit-taking in volatile markets.
- ATR Multiplier: Similar to the TP method, the SL can also be set using a multiple of the ATR.
Pro-Trend and Contrarian Strategies: The SMRT Algo Bot is designed to execute either pro-trend or contrarian trading strategies, though only one can be active at any given time.
Pro-Trend Strategy: This strategy aligns with the prevailing market trend, aiming to capitalize on the continuation of current price movements. It is particularly effective in trending markets, where momentum is expected to carry the price further in the direction of the trend.
Contrarian Strategy: In contrast, the contrarian strategy seeks to exploit potential reversals or corrections, trading against the prevailing trend. This approach is more suitable in overextended markets where a pullback is anticipated. Traders can switch between these strategies based on their market outlook and trading style.
Dashboard Display: A dashboard located in the bottom right corner of the TradingView interface provides real-time updates on the bot’s performance metrics. This includes key statistics such as net profit, drawdown, profit factor, and win rate, specific to the current instrument being tested. This immediate access to performance data allows traders to quickly assess the effectiveness of the strategy and make necessary adjustments on the fly.
Input Settings:
Reverse Signals: If turned on, buy trades will be shown as sell trades, etc.
Show Signal (Bar Color): Shows the signal bar as a green candle for buy or red candle for sell.
RSI: Used as a filter for one of the conditions for trade. Can be turned on/off by clicking on the checkbox.
Timeframe: Affects the timeframe of RSI filter.
Length: Length of RSI used in measurement.
First Cross: Whether or not to factor in the first RSI cross in the calculation.
Buy/Sell (Above/Below): Look for trades if RSI is above or below these values.
EMA: Used as a trend filter for one of the conditions for trade. Can be turned on/off by clicking on the checkbox.
Timeframe: Affects the timeframe of EMA filter.
Fast Length: Value for the fast EMA.
Middle Length: Value for the middle EMA
Slow Length: Value for the slow EMA.
ADX: Used as a volatility filter for one of the conditions for trade. Can be turned on/off by clicking on the checkbox.
Threshold: Threshold value for ADX.
ADX Smoothing: Smoothing value for the ADX
DI Length: DI length value for the ADX.
Donchian Channel Length: This value affects the length value of the DC. Used in TP calculation.
Close Trade On Opposite Signal: If true, the current trade will close if an opposite trade appears.
RSI: If turned on, it will also use the RSI to exit the trade (overextended zones).
Take Profit Option: Choose between normal (percentage-based) and Donchian Channel options.
Stop Loss Option: Choose between normal (percentage-based) and Donchian Channel options.
The SMRT Algo Bot’s components are designed to work together seamlessly, creating a comprehensive trading solution. Whether using the ATR multiplier for dynamic adjustments or the Donchian Channel for trend-based targets, these methods ensure that trades are managed effectively from entry to exit. The ability to switch between pro-trend and contrarian strategies offers adaptability, enabling traders to optimize their approach based on market behavior. The real-time dashboard ties everything together, providing continuous feedback that informs strategic adjustments.
Unlike basic or open-source bots, which often lack the flexibility to adapt to different market conditions, the SMRT Algo Bot provides a robust and dynamic trading solution. The inclusion of multiple TP and SL methods, particularly the ATR and Donchian Channel, adds significant value by offering traders tools that can be finely tuned to both volatile and trending markets.
The SMRT Algo Suite, which the SMRT Algo Bot is a part of, offers a comprehensive set of tools and features that extend beyond the capabilities of standard or open-source indicators, providing significant additional value to users.
What you also get with the SMRT Algo Suite:
Advanced Customization: Users can customize various aspects of the indicator, such as toggling the confirmation signals on or off and adjusting the parameters of the MA Filter. This customization enhances the adaptability of the tool to different trading styles and market conditions.
Enhanced Market Understanding: The combination of pullback logic, dynamic S/R zones, and MA filtering offers traders a nuanced understanding of market dynamics, helping them make more informed trading decisions.
Unique Features: The specific combination of pullback logic, dynamic S/R, and multi-level TP/SL management is unique to SMRT Algo, offering features that are not readily available in standard or open-source indicators.
Educational and Support Resources: As with other tools in the SMRT Algo suite, this indicator comes with comprehensive educational resources and access to a supportive trading community, as well as 24/7 Discord support.
The educational resources and community support included with SMRT Algo ensure that users can maximize the indicators’ potential, offering guidance on best practices and advanced usage.
SMRT Algo believe that there is no magic indicator that is able to print money. Indicator toolkits provide value via their convenience, adaptability and uniqueness. Combining these items can help a trader make more educated; less messy, more planned trades and in turn hopefully help them succeed.
RISK DISCLAIMER
Trading involves significant risk, and most day traders lose money. All content, tools, scripts, articles, and educational materials provided by SMRT Algo are intended solely for informational and educational purposes. Past performance is not indicative of future results. Always conduct your own research and consult with a licensed financial advisor before making any trading decisions.
RSI Strategy with Adjustable RSI and Stop-LossThis trading strategy uses the Relative Strength Index (RSI) and a Stop-Loss mechanism to make trading decisions. Here’s a breakdown of how it works:
RSI Calculation:
The RSI is calculated based on the user-defined length (rsi_length). This is a momentum oscillator that measures the speed and change of price movements.
Buy Condition:
The strategy generates a buy signal when the RSI value is below a user-defined threshold (rsi_threshold). This condition indicates that the asset might be oversold and potentially due for a rebound.
Stop-Loss Mechanism:
Upon triggering a buy signal, the strategy calculates the Stop-Loss level. The Stop-Loss level is set to a percentage below the entry price, as specified by the user (stop_loss_percent). This level is used to limit potential losses if the price moves against the trade.
Sell Condition:
A sell signal is generated when the current closing price is higher than the highest high of the previous day. This condition suggests that the price has reached a new high, and the strategy decides to exit the trade.
Plotting:
The RSI values are plotted on the chart for visual reference. A horizontal line is drawn at the RSI threshold level to help visualize the oversold condition.
Summary
Buying Strategy: When RSI is below the specified threshold, indicating potential oversold conditions.
Stop-Loss: Set based on a percentage of the entry price to limit potential losses.
Selling Strategy: When the price surpasses the highest high of the previous day, signaling a potential exit point.
This strategy aims to capture potential rebounds from oversold conditions and manage risk using a Stop-Loss mechanism. As with any trading strategy, it’s essential to test and optimize it under various market conditions to ensure its effectiveness.
Dual Chain StrategyDual Chain Strategy - Technical Overview
How It Works:
The Dual Chain Strategy is a unique approach to trading that utilizes Exponential Moving Averages (EMAs) across different timeframes, creating two distinct "chains" of trading signals. These chains can work independently or together, capturing both long-term trends and short-term price movements.
Chain 1 (Longer-Term Focus):
Entry Signal: The entry signal for Chain 1 is generated when the closing price crosses above the EMA calculated on a weekly timeframe. This suggests the start of a bullish trend and prompts a long position.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Exit Signal: The exit signal is triggered when the closing price crosses below the EMA on a daily timeframe, indicating a potential bearish reversal.
exitLongChain1 = enableChain1 and ta.crossunder(src1, exitEMA1)
Parameters: Chain 1's EMA length is set to 10 periods by default, with the flexibility for user adjustment to match various trading scenarios.
Chain 2 (Shorter-Term Focus):
Entry Signal: Chain 2 generates an entry signal when the closing price crosses above the EMA on a 12-hour timeframe. This setup is designed to capture quicker, shorter-term movements.
bullishChain2 = enableChain2 and ta.crossover(src2, entryEMA2)
Exit Signal: The exit signal occurs when the closing price falls below the EMA on a 9-hour timeframe, indicating the end of the shorter-term trend.
exitLongChain2 = enableChain2 and ta.crossunder(src2, exitEMA2)
Parameters: Chain 2's EMA length is set to 9 periods by default, and can be customized to better align with specific market conditions or trading strategies.
Key Features:
Dual EMA Chains: The strategy's originality shines through its dual-chain configuration, allowing traders to monitor and react to both long-term and short-term market trends. This approach is particularly powerful as it combines the strengths of trend-following with the agility of momentum trading.
Timeframe Flexibility: Users can modify the timeframes for both chains, ensuring the strategy can be tailored to different market conditions and individual trading styles. This flexibility makes it versatile for various assets and trading environments.
Independent Trade Logic: Each chain operates independently, with its own set of entry and exit rules. This allows for simultaneous or separate execution of trades based on the signals from either or both chains, providing a robust trading system that can handle different market phases.
Backtesting Period: The strategy includes a configurable backtesting period, enabling thorough performance assessment over a historical range. This feature is crucial for understanding how the strategy would have performed under different market conditions.
time_cond = time >= startDate and time <= finishDate
What It Does:
The Dual Chain Strategy offers traders a distinctive trading tool that merges two separate EMA-based systems into one cohesive framework. By integrating both long-term and short-term perspectives, the strategy enhances the ability to adapt to changing market conditions. The originality of this script lies in its innovative dual-chain design, providing traders with a unique edge by allowing them to capitalize on both significant trends and smaller, faster price movements.
Whether you aim to capture extended market trends or take advantage of more immediate price action, the Dual Chain Strategy provides a comprehensive solution with a high degree of customization and strategic depth. Its flexibility and originality make it a valuable tool for traders seeking to refine their approach to market analysis and execution.
How to Use the Dual Chain Strategy
Step 1: Access the Strategy
Add the Script: Start by adding the Dual Chain Strategy to your TradingView chart. You can do this by searching for the script by name or using the link provided.
Select the Asset: Apply the strategy to your preferred trading pair or asset, such as #BTCUSD, to see how it performs.
Step 2: Configure the Settings
Enable/Disable Chains:
The strategy is designed with two independent chains. You can choose to enable or disable each chain depending on your trading style and the market conditions.
enableChain1 = input.bool(true, title='Enable Chain 1')
enableChain2 = input.bool(true, title='Enable Chain 2')
By default, both chains are enabled. If you prefer to focus only on longer-term trends, you might disable Chain 2, or vice versa if you prefer shorter-term trades.
Set EMA Lengths:
Adjust the EMA lengths for each chain to match your trading preferences.
Chain 1: The default EMA length is 10 periods. This chain uses a weekly timeframe for entry signals and a daily timeframe for exits.
len1 = input.int(10, minval=1, title='Length Chain 1 EMA', group="Chain 1")
Chain 2: The default EMA length is 9 periods. This chain uses a 12-hour timeframe for entries and a 9-hour timeframe for exits.
len2 = input.int(9, minval=1, title='Length Chain 2 EMA', group="Chain 2")
Customize Timeframes:
You can customize the timeframes used for entry and exit signals for both chains.
Chain 1:
Entry Timeframe: Weekly
Exit Timeframe: Daily
tf1_entry = input.timeframe("W", title='Chain 1 Entry Timeframe', group="Chain 1")
tf1_exit = input.timeframe("D", title='Chain 1 Exit Timeframe', group="Chain 1")
Chain 2:
Entry Timeframe: 12 Hours
Exit Timeframe: 9 Hours
tf2_entry = input.timeframe("720", title='Chain 2 Entry Timeframe (12H)', group="Chain 2")
tf2_exit = input.timeframe("540", title='Chain 2 Exit Timeframe (9H)', group="Chain 2")
Set the Backtesting Period:
Define the period over which you want to backtest the strategy. This allows you to see how the strategy would have performed historically.
startDate = input.time(timestamp('2015-07-27'), title="StartDate")
finishDate = input.time(timestamp('2026-01-01'), title="FinishDate")
Step 3: Analyze the Signals
Understand the Entry and Exit Signals:
Buy Signals: When the price crosses above the entry EMA, the strategy generates a buy signal.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Sell Signals: When the price crosses below the exit EMA, the strategy generates a sell signal.
bearishChain2 = enableChain2 and ta.crossunder(src2, entryEMA2)
Review the Visual Indicators:
The strategy plots buy and sell signals on the chart with labels for easy identification:
BUY C1/C2 for buy signals from Chain 1 and Chain 2.
SELL C1/C2 for sell signals from Chain 1 and Chain 2.
This visual aid helps you quickly understand when and why trades are being executed.
Step 4: Optimize the Strategy
Backtest Results:
Review the strategy’s performance over the backtesting period. Look at key metrics like net profit, drawdown, and trade statistics to evaluate its effectiveness.
Adjust the EMA lengths, timeframes, and other settings to see how changes affect the strategy’s performance.
Customize for Live Trading:
Once satisfied with the backtest results, you can apply the strategy settings to live trading. Remember to continuously monitor and adjust as needed based on market conditions.
Step 5: Implement Risk Management
Use Realistic Position Sizing:
Keep your risk exposure per trade within a comfortable range, typically between 1-2% of your trading capital.
Set Alerts:
Set up alerts for buy and sell signals, so you don’t miss trading opportunities.
Paper Trade First:
Consider running the strategy in a paper trading account to understand its behavior in real market conditions before committing real capital.
This dual-layered approach offers a distinct advantage: it enables the strategy to adapt to varying market conditions by capturing both broad trends and immediate price action without one chain's activity impacting the other's decision-making process. The independence of these chains in executing transactions adds a level of sophistication and flexibility that is rarely seen in more conventional trading systems, making the Dual Chain Strategy not just unique, but a powerful tool for traders seeking to navigate complex market environments.
Double CCI Confirmed Hull Moving Average Reversal StrategyOverview
The Double CCI Confirmed Hull Moving Average Strategy utilizes hull moving average (HMA) in conjunction with two commodity channel index (CCI) indicators: the slow and fast to increase the probability of entering when the short and mid-term uptrend confirmed. The main idea is to wait until the price breaks the HMA while both CCI are showing that the uptrend has likely been already started. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Double trade setup confirmation: Strategy utilizes two different period CCI indicators to confirm the breakouts of HMA.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Short-term period CCI indicator shall be above 0.
Long-term period CCI indicator shall be above 0.
Price shall cross the HMA and candle close above it with the same candle
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 25, used for calculation short term period CCI
CCI Slow Length (by default = 50, used for calculation long term period CCI)
Hull MA Length (by default = 34, period of HMA, which shall be broken to open trade)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI and HMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator used in trading to measure a security's price relative to its average price over a given period. Developed by Donald Lambert in 1980, the CCI is primarily used to identify cyclical trends in a security, helping traders to spot potential buying or selling opportunities.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Hull Moving Average (HMA) is a type of moving average that was developed by Alan Hull to improve upon the traditional moving averages by reducing lag while maintaining smoothness. The goal of the HMA is to create an indicator that is both quick to respond to price changes and less prone to whipsaws (false signals).
How the Hull Moving Average is Calculated?
The Hull Moving Average is calculated using the following steps:
Weighted Moving Average (WMA): The HMA starts by calculating the Weighted Moving Average (WMA) of the price data over a period square root of n (sqrt(n))
Speed Adjustment: A WMA is then calculated for half of the period n/2, and this is multiplied by 2 to give more weight to recent prices.
Lag Reduction: The WMA of the full period n is subtracted from the doubled n/2 WMA.
Final Smoothing: To smooth the result and reduce noise, a WMA is calculated for the square root of the period n.
The formula can be represented as:
HMA(n) = WMA(WMA(n/2) × 2 − WMA(n), sqrt(n))
The Weighted Moving Average (WMA) is a type of moving average that gives more weight to recent data points, making it more responsive to recent price changes than a Simple Moving Average (SMA). In a WMA, each data point within the selected period is multiplied by a weight, with the most recent data receiving the highest weight. The sum of these weighted values is then divided by the sum of the weights to produce the WMA.
This strategy leverages HMA of user given period as a critical level which shall be broken to say that probability of trend change to the upside increased. HMA reacts faster than EMA or SMA to the price change, that’s why it increases chances to enter new trade earlier. Long-term period CCI helps to have an approximation of mid-term trend. If it’s above 0 the probability of uptrend increases. Short-period CCI allows to have an approximation of short-term trend reversal from down to uptrend. This approach increases chances to have a long trade setup in the direction of mid-term trend when the short-term trend starts to reverse.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses HMA to enter the trade, but for trailing it leverages EMA. It’s used because EMA has no such fast reaction to price move which increases probability not to be stopped out from any significant uptrend move.
Backtest Results
Operating window: Date range of backtests is 2022.07.01 - 2024.08.01. 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: 100%
Maximum Single Position Loss: -4.67%
Maximum Single Profit: +19.66%
Net Profit: +14897.94 USDT (+148.98%)
Total Trades: 104 (36.54% win rate)
Profit Factor: 2.312
Maximum Accumulated Loss: 1302.66 USDT (-9.58%)
Average Profit per Trade: 143.25 USDT (+0.96%)
Average Trade Duration: 34 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 2h 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
Bollinger Bands Enhanced StrategyOverview
The common practice of using Bollinger bands is to use it for building mean reversion or squeeze momentum strategies. In the current script Bollinger Bands Enhanced Strategy we are trying to combine the strengths of both strategies types. It utilizes Bollinger Bands indicator to buy the local dip and activates trailing profit system after reaching the user given number of Average True Ranges (ATR). Also it uses 200 period EMA to filter trades only in the direction of a trend. Strategy can execute only long trades.
Unique Features
Trailing Profit System: Strategy uses user given number of ATR to activate trailing take profit. If price has already reached the trailing profit activation level, scrip will close long trade if price closes below Bollinger Bands middle line.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Major Trend Filter: Strategy utilizes 100 period EMA to take trades only in the direction of a trend.
Flexible Risk Management: Users can choose number of ATR as a stop loss (by default = 1.75) for trades. This is flexible approach because ATR is recalculated on every candle, therefore stop-loss readjusted to the current volatility.
Methodology
First of all, script checks if currently price is above the 200-period exponential moving average EMA. EMA is used to establish the current trend. Script will take long trades on if this filtering system showing us the uptrend. Then the strategy executes the long trade if candle’s low below the lower Bollinger band. To calculate the middle Bollinger line, we use the standard 20-period simple moving average (SMA), lower band is calculated by the substruction from middle line the standard deviation multiplied by user given value (by default = 2).
When long trade executed, script places stop-loss at the price level below the entry price by user defined number of ATR (by default = 1.75). This stop-loss level recalculates at every candle while trade is open according to the current candle ATR value. Also strategy set the trailing profit activation level at the price above the position average price by user given number of ATR (by default = 2.25). It is also recalculated every candle according to ATR value. When price hit this level script plotted the triangle with the label “Strong Uptrend” and start trail the price at the middle Bollinger line. It also started to be plotted as a green line.
When price close below this trailing level script closes the long trade and search for the next trade opportunity.
Risk Management
The strategy employs a combined and flexible approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined ATR stop loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 1.75*ATR drop from the entry point, but it can be adjusted according to the trader's preferences.
There is no fixed take profit, but strategy allows user to define user the ATR trailing profit activation parameter. By default, this stop-loss is set to a 2.25*ATR growth from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Bollinger bangs indicator to open long trades in the local dips. If price reached the lower band there is a high probability of bounce. Here is an issue: during the strong downtrend price can constantly goes down without any significant correction. That’s why we decided to use 200-period EMA as a trend filter to increase the probability of opening long trades during major uptrend only.
Usually, Bollinger Bands indicator is using for mean reversion or breakout strategies. Both of them have the disadvantages. The mean reversion buys the dip, but closes on the return to some mean value. Therefore, it usually misses the major trend moves. The breakout strategies usually have the issue with too high buy price because to have the breakout confirmation price shall break some price level. Therefore, in such strategies traders need to set the large stop-loss, which decreases potential reward to risk ratio.
In this strategy we are trying to combine the best features of both types of strategies. Script utilizes ate ATR to setup the stop-loss and trailing profit activation levels. ATR takes into account the current volatility. Therefore, when we setup stop-loss with the user-given number of ATR we increase the probability to decrease the number of false stop outs. The trailing profit concept is trying to add the beat feature from breakout strategies and increase probability to stay in trade while uptrend is developing. When price hit the trailing profit activation level, script started to trail the price with middle line if Bollinger bands indicator. Only when candle closes below the middle line script closes the long trade.
Backtest Results
Operating window: Date range of backtests is 2020.10.01 - 2024.07.01. 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: 30%
Maximum Single Position Loss: -9.78%
Maximum Single Profit: +25.62%
Net Profit: +6778.11 USDT (+67.78%)
Total Trades: 111 (48.65% win rate)
Profit Factor: 2.065
Maximum Accumulated Loss: 853.56 USDT (-6.60%)
Average Profit per Trade: 61.06 USDT (+1.62%)
Average Trade Duration: 76 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 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Self Optimizing RSI and Self Adaptive TP/SL [Starbots]Self Optimizing RSI and Self Adaptive TP/SL Strategy. (non-repainting)
This script continuously backtests 20 different combinations of RSI Buy conditions across 5 different Take Profit/Stop Loss combinations. In total, it tests 100 variants on every bar close and records the Net Profit gained for each combination. The strategy then selects and uses the best-performing combination of settings currently available for you to trade.
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The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30. Signals can be generated by looking for divergences and failure swings. RSI can also be used to identify the general trend.
To improve our results we are calculating Multiple Length RSI - Average RSI based on the multiple periods. You can use just 1 Length or Multiple.
Set Inputs to Min=14, Max=14 if you want to use just 1 period.
= RSI(14)
3 RSI Lengths example (12,13 and 14):
Min=12, Max=14
(12+13+14) / 3 = avg. RSI
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Backtester - Optimizer Explained:
The backtester runs numerous backtests in the background to optimize trading strategies. Here’s how it works:
Default Inputs (Combinations of TP/SL)
TP 1%, SL4%
TP 2%, SL4%
TP 3%, SL4%
TP 2%, SL5%
TP 4.5%, SL10%
Default Inputs (RSI Crossover Buys) :
18 ,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,45,55, 69
_______________________________________________________
Backtest RSI Crossover 18:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
,...
,...
Backtest RSI Crossover 69:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
Self Optimizing Buy Condition and Self Optimizing Take Profit - Stop Los
This process involves testing various combinations of RSI crossover values with different Take Profit (TP) and Stop Loss (SL) percentages. The net profit for each combination is saved, allowing the optimizer to select the best-performing settings for trading.
It recalculates on every bar close. If one combination starts performing better than others—achieving a higher net profit gain (essentially like running 100 backtests with different settings in the background)—the strategy switches to that combination of TP/SL and Buy condition. It continues trading with the new settings until another parameter starts performing better and the strategy switches to that setting.
________________________________________________________________________
If you wish to use it as INDICATOR - turn on 'Recalculate - On every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
Other functions:
Set the %fee for optimizing engine. If you set this % higher, you also punish small average trades and make the strategy prefer larger avg. trades, giving you better chances to make your strategy profitable.
Trade with trend and optimize the strategy only when the market is uptrending with EMA/HMA
Use Moving Average of avg.RSI and smooth the values for indicator even more. (Yes strategy is self optimizing RSI or avg.RSI or RSI-MA, you can select all sorts of this indicator for optimizing)
All trading alerts are working and functional, if you want to automate the strategy
This script is simple to use for any trader as it saves a lot of time for searching good parameters on your own. It's self-optimizing and adjusting to the markets on the go.
ADX + CCI + MA - Uncle SamStrategy Name: ADX + CCI + MA - Uncle Sam
Overview
This strategy aims to capitalize on trending markets by combining the Average Directional Index (ADX), Commodity Channel Index (CCI), and a customizable Moving Average (MA). It's designed for traders seeking a balanced approach to both long (buy) and short (sell) opportunities. Special thanks to the creators of the ADX and CCI indicators for their invaluable contributions to technical analysis.
Strategy Concept
The core idea is to identify strong trends with the ADX, confirm potential entry points with the CCI, and use the MA to filter trades in the direction of the broader trend. This approach seeks to avoid entering positions during periods of consolidation or when the trend is weak.
Indicator Logic
ADX (Average Directional Index): The ADX measures the strength of a trend, regardless of its direction. A value above the customizable adx_threshold (default 20) signals a strong trend, making it a prime environment for this strategy.
CCI (Commodity Channel Index): The CCI is a momentum oscillator that helps identify overbought (above 100) and oversold (below -100) conditions. We use CCI crossovers to time entries in the direction of the prevailing trend.
MA (Moving Average): The MA acts as a trend filter, ensuring we only enter trades aligned with the overall market direction. You have flexibility in choosing the MA type (SMA, EMA, etc.) and its length to suit your trading style and timeframe.
Entry Conditions
Long (Buy):
ADX is above the adx_threshold.
CCI crosses above 100.
Price is above the chosen Moving Average (if MA trend filtering is enabled).
Short (Sell):
ADX is above the adx_threshold.
CCI crosses below -100.
Price is below the chosen Moving Average (if MA trend filtering is enabled).
Exit Conditions
Stop Loss (SL): Each position has a customizable stop-loss percentage to manage risk. The default setting is 1%.
Take Profit (TP): Each position has a customizable take-profit percentage to secure gains. The default setting is 5%.
MA-Based Risk Management (Optional): This feature allows for early exits if the price closes against the MA trend for a specified number of candles. The default setting is 2 candles.
Default Settings
CCI Period: 15
ADX Length: 10
ADX Threshold: 20
MA Type: HMA
MA Length: 200
MA Source: Close
Commission Fee: $0.0
A commission fee is not added, add your trading/platform commission for realistic trading costs.
Backtest Results
The strategy has been backtested on with the default settings and a starting capital of $1000, with 0.0% commission fee. It shows promising results.
Disclaimer: Backtesting is hypothetical and does not guarantee future performance.
Important Considerations:
Customization: The strategy offers extensive customization to tailor it to your preferences. Experiment with different parameters and settings to find what works best for your trading style.
Risk Management: Always use proper risk management techniques, including position sizing and stop losses, to protect your capital.
TSI w SuperTrend decision - Strategy [presentTrading]This strategy aims to improve upon the performance of Traidngview's newly published "Trend Strength Index" indicator by incorporating the SuperTrend for better trade execution and risk management. Enjoy :)
█ Introduction and How it is Different
The "TSI with SuperTrend Decision - Strategy" combines the Trend Strength Index (TSI) with SuperTrend indicators to determine entry and exit points. Unlike traditional strategies that rely solely on one indicator, this method leverages the strengths of both TSI and SuperTrend to provide a more nuanced and adaptive trading strategy.
This dual approach allows for capturing trends more effectively, especially in volatile markets.
BTCUSD 8h LS Performance
█ Strategy, How it Works: Detailed Explanation
🔶 Trend Strength Index (TSI)
The TSI is a momentum oscillator that shows both the direction and strength of a trend. It is calculated by comparing the price movement with the bar index over a specified period. The formula for TSI is as follows:
```
TSI = (PC / |PC|)
where:
PC = Change in price over the period
```
In this strategy, TSI is calculated using the closing prices and a default period of 64 bars. The TSI values help identify overbought and oversold conditions, providing signals for potential market reversals.
🔶 SuperTrend Indicator
The SuperTrend is a trend-following indicator based on the average true range (ATR). It helps in identifying the direction of the market trend. The SuperTrend calculation involves:
```
SuperTrend = HLC3 ± (Factor * ATR)
where:
HLC3 = (High + Low + Close) / 3
Factor = User-defined multiplier
ATR = Average True Range over a period
```
The SuperTrend settings in this strategy include a length of 10 bars and a factor of 3.0.
Last Bull Cycle of BTC
🔶 Entry and Exit Conditions
The strategy uses the TSI and SuperTrend together to determine entry and exit points:
- Long Entry: When the SuperTrend indicates a downward trend (st.d < 0) and the TSI is above the oversold level (-0.241).
- Long Exit: When the SuperTrend indicates an upward trend (st.d > 0) and the TSI is below the overbought level (0.241).
- Short Entry: When the SuperTrend indicates an upward trend (st.d > 0) and the TSI is below the overbought level (0.241).
- Short Exit: When the SuperTrend indicates a downward trend (st.d < 0) and the TSI is above the oversold level (-0.241).
█ Trade Direction
The strategy allows users to select the trade direction through the `tradeDirection` input. The options are:
- Both: Enables both long and short trades.
- Long: Enables only long trades.
- Short: Enables only short trades.
█ Default Settings
- TSI Length: 64
- SuperTrend Length: 10
- SuperTrend Factor: 3.0
- Trade Direction: Both
- Take Profit (%): 30.0
- Stop Loss (%): 20.0
Impact of Default Settings
- TSI Length: A longer TSI period smooths out noise but may lag in identifying trends. A shorter period is more responsive but can generate false signals.
- SuperTrend Length: A shorter length provides quicker signals but can be prone to whipsaws. A longer length is more reliable but may delay entries and exits.
- SuperTrend Factor: A higher factor increases the distance of the SuperTrend from the price, reducing sensitivity to minor price fluctuations.
- Trade Direction: Allows flexibility in trading strategies by enabling both long and short trades based on market conditions.
- Take Profit and Stop Loss: These settings manage risk by automatically closing trades at predefined profit or loss levels. Higher percentages provide larger potential gains but also higher risk.
IsAlgo - Manual TrendLine► Overview:
Manual TrendLine is a strategy that allows traders to manually insert a trendline and opens trades when the trendline is retested or when the price hits a new highest high or lowest low. It provides flexibility in trendline configuration and trading behavior, enabling responsive and adaptable trading strategies.
► Description:
The Manual TrendLine strategy revolves around using manually defined trendlines as the primary tool for making trading decisions. Traders start by specifying two key points on the chart to establish the trendline. Each point is defined by a specific time and price, enabling precise placement according to the trader’s analysis and insights. Additionally, the strategy allows for the adjustment of the trendline’s width, which acts as a buffer zone around the trendline, providing flexibility in how closely price movements must align with the trendline to trigger trades.
Once the trendline is established, the strategy continuously monitors price movements relative to this line. One of its core functions is to execute trades when the price retests the trendline. A retest occurs when the price approaches the trendline after initially diverging from it, indicating potential continuation of the prevailing trend. This behavior is often seen as a confirmation of the trend’s strength, and the strategy takes advantage of these moments to enter trades in the direction of the trend.
Beyond retests, the strategy also tracks the formation of new highest highs and lowest lows in relation to the trendline. When the price reaches a new highest high or lowest low, it signifies strong momentum in the trend’s direction. The strategy can be configured to open trades at these critical points.
Another key feature of the strategy is its response to trendline breaks. A break occurs when the price moves through the trendline, potentially signaling a reversal or a significant shift in market sentiment. The strategy can be set to open reverse trades upon such breaks, enabling traders to quickly adapt to changing market conditions. Additionally, traders have the option to stop opening new trades after a trendline break, helping to avoid trades during periods of uncertainty or increased volatility.
↑ Up Trend Example:
↓ Down Trend Example:
► Features and Settings:
⚙︎ TrendLine: Define the time and price of the two main points of the trendline, and set the trendline width.
⚙︎ Entry Candle: Specify the minimum and maximum body size and the body-to-candle size ratio for entry candles.
⚙︎ Trading Session: Define specific trading hours during which the strategy operates, restricting trades to preferred market periods.
⚙︎ Trading Days: Specify active trading days to avoid certain days of the week.
⚙︎ Backtesting: backtesting for a selected period to evaluate strategy performance. This feature can be deactivated if not needed.
⚙︎ Trades: Configure trade direction (long, short, or both), position sizing (fixed or percentage-based), maximum number of open trades, and daily trade limits.
⚙︎ Trades Exit: Set profit/loss limits, specify trade duration, or exit based on band reversal signals.
⚙︎ Stop Loss: Choose from various stop-loss methods, including fixed pips, ATR-based, or highest/lowest price points within a specified number of candles. Trades can also be closed after a certain number of adverse candle movements.
⚙︎ Break Even: Adjust stop loss to break even once predefined profit levels are reached, protecting gains.
⚙︎ Trailing Stop: Implement a trailing stop to adjust the stop loss as the trade becomes profitable, securing gains and potentially capturing further upside.
⚙︎ Take Profit: Set up to three take-profit levels using methods such as fixed pips, ATR, or risk-to-reward ratios. Alternatively, specify a set number of candles moving in the trade’s direction.
⚙︎ Alerts: Comprehensive alert system to notify users of significant actions, including trade openings and closings. Supports dynamic placeholders for take-profit levels and stop-loss prices.
⚙︎ Dashboard: Visual display on the chart providing detailed information about ongoing and past trades, aiding users in monitoring strategy performance and making informed decisions.
► Backtesting Details:
Timeframe: 30-minute EURUSD chart
Initial Balance: $10,000
Order Size: 500 units
Commission: 0.05%
Slippage: 5 ticks
This strategy opens trades around a manually drawn trendline, which results in a smaller number of closed trades.
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.
Universal Algo [Coff3eG]Universal Algo By G
Overview:
Universal Algo By G is a comprehensive LONG-ONLY trading strategy specifically designed for medium to long-term use in cryptocurrency markets, particularly Bitcoin. This algorithm can be manually adjusted to fit the volatility of specific coins, ensuring the best possible results. While it does not generate a large number of trades due to the nature of bull and bear market cycles, it has been rigorously backtested and forward-tested to ensure the strategy is not overfitted.
Core Features:
Integrated Systems: Universal Algo is built around five core systems, each contributing unique analytical perspectives to enhance trade signal reliability. These systems are designed to identify clear trend opportunities for significant gains while also employing logic to navigate through ranging markets effectively.
Optional Ranging Market Filter: Helps filter out noise, potentially enhancing signal clarity.
Market State Detection: Identifies four distinct market states:
Trending
Ranging
Danger (Possible top)
Possible Bottom
Global Liquidity Indicator (GLI) Integration: Leverages GLI values to identify positive liquidity trends.
Volatility Bands: Provides insights into market volatility.
Top and Bottom Detection: Shows possible bottoms with green backgrounds and red backgrounds for possible top detection.
The Market State Detection, GLI, Volatility Bands, and Top and Bottom Detection feature all serve as an expectation management feature.
Additional Features:
Optional Metrics Table: Displays strategy metrics and statistics, providing detailed insights into performance.
Customization Options: The script offers a range of user inputs, allowing for customization of the backtesting starting date, the decision to display the strategy equity curve, among other settings. These inputs cater to diverse trading needs and preferences, offering users control over their strategy implementation.
Operational Parameters:
Customizable Inputs: Users can adjust thresholds to match the coin's volatility, enhancing strategy performance.
Transparency and Logic Insight: While specific calculation details and proprietary indicators are integral to maintaining the uniqueness of Universal Algo, the strategy is grounded on well-established financial analysis techniques. These include momentum analysis, volatility assessments, and adaptive thresholding, among others, to formulate its trade signals. Notably, no single indicator is used in isolation; each indicator is combined with another to enhance signal accuracy and robustness. Some of the indicators include customized versions of the TEMA, Supertrend, Augmented Dickey-Fuller (ADF), and Weekly Positive Directional Movement Index (WPDM), all integrated together to create a cohesive and effective trading strategy.
System Operation:
Universal Algo works by taking the average score of the five core systems used for the signals. Three of these systems have been lengthened out to function as longer-term systems, while the remaining two operate at a slightly faster speed. This combination and averaging of systems help to balance the overall strategy, ensuring it maintains the right amount of speed to remain effective for medium to long-term use with minimal noise. The average score is then compared against customizable thresholds. The strategy will go long if the average score is above the threshold and short if it is below the threshold. This averaging mechanism helps to smooth out individual system anomalies and provides a more robust signal for trading decisions.
Originality and Usefulness:
Universal Algo is an original strategy that combines multiple proprietary and customized indicators to deliver robust trading signals. The strategy integrates various advanced indicators and methodologies, including:
System Indicator: Calculates a cumulative score based on recent price movements, aiding in trend detection.
Median For Loop: Utilizes percentile rank calculations of price data to gauge market direction.
Volatility Stop: A modified volatility-based stop-loss indicator that adjusts based on market conditions.
Supertrend: A customized supertrend indicator that uses percentile ranks and ATR for trend detection.
RSI and DEMA: Combines a modified RSI and DEMA for overbought/oversold conditions.
TEMA: Uses 3 different types of MA for trend detection and standard deviation bands for additional confirmation.
Detailed Explanation of Components and Their Interaction:
RSI (Relative Strength Index): Used to identify overbought and oversold conditions. In Universal Algo, RSI is combined with DEMA (Double Exponential Moving Average) to smooth the price data and provide clearer signals.
ATR (Average True Range): Used to measure market volatility. ATR is incorporated into the Volatility Stop and Supertrend indicators to adjust stop-loss levels and trend detection based on current market conditions.
DEMA (Double Exponential Moving Average): Provides a smoother price trend compared to traditional moving averages, reducing lag and making it easier to identify trend changes.
Modified TEMA (Triple Exponential Moving Average): Similar to DEMA but provides even greater smoothing, reducing lag further and enhancing trend detection accuracy.
Volatility Stop: Utilizes ATR to dynamically set stop-loss levels that adapt to changing market volatility. This helps in protecting profits and minimizing losses.
Customized Supertrend: Uses ATR and percentile ranks to determine trend direction and strength. This indicator helps in capturing major trends while filtering out market noise.
Median For Loop: Calculates percentile ranks of price data over a specified period to assess market direction. This helps in identifying potential reversals and trend continuations.
HMA (Hull Moving Average): A fast-acting moving average that reduces lag while maintaining smoothness. It helps in quickly identifying trend changes.
SMA (Simple Moving Average): A traditional moving average that provides baseline trend information. Combined with HMA and other indicators, it forms a comprehensive trend detection system.
Universal Algo offers a sophisticated blend of advanced indicators and proprietary logic that is not available in free or open-source scripts. Here are some reasons why it is worth paying for:
Customization and Flexibility: The strategy provides a high degree of customization, allowing users to adjust various parameters to suit their trading style and market conditions. This flexibility is often not available in free scripts.
Proprietary Indicators: The use of proprietary and customized indicators such as the TEMA, Supertrend, ADF, and WPDM ensures that the strategy is unique and not replicable by free or open-source scripts.
Integrated Systems: The strategy combines multiple systems and indicators to provide a more comprehensive and reliable trading signal. This integration helps to smooth out anomalies and reduces noise, providing clearer trading opportunities.
Rigorous Testing: Universal Algo has undergone extensive backtesting and forward-testing to ensure its robustness and reliability. The results demonstrate its ability to perform well under various market conditions, offering users confidence in its effectiveness.
Detailed Metrics and Analysis: The optional metrics table provides users with detailed insights into the strategy's performance, including metrics like equity, drawdown, Sharpe ratio, and more. This level of detail helps traders make informed decisions.
Value Addition: By providing a strategy that combines advanced indicators, customization options, and thorough testing, Universal Algo adds significant value to traders looking for a reliable and adaptable trading tool.
Realistic Trading Conditions:
Backtesting and Forward-Testing: Rigorous testing ensures performance and reliability, with a focus on prudent risk management. Default properties include an initial capital of $1000, 0 pyramiding, 20 slippage, 0.05% commission, and using 5% of equity for trades.
The strategy is designed and tested with a focus on achieving a balance between risk and reward, striving for robustness and reliability rather than unrealistic profitability promises. Realistic trading conditions are considered, including appropriate account size, commission, slippage, and sustainable risk levels per trade.
Concluding Thoughts:
Universal Algo By G is offered to the TradingView community as a robust tool for enhancing market analysis and trading strategies. It is designed with a commitment to quality, innovation, and adaptability, aiming to provide valuable insights and decision support across various market conditions. Potential users are encouraged to evaluate Universal Algo within the context of their overall trading approach and objectives.
Ultimate Trading StrategyDescription:
In this TradingView Pine Script publication, we introduce a powerful tool designed to enhance your trading strategies by combining the Exponential Moving Average (EMA) and the Relative Strength Index (RSI). This strategy is specifically tailored for the EGLD/USDT.P pair on Binance, using a 5-minute interval to capture timely trading opportunities in a volatile market.
Key Features:
Combining EMA and RSI for Robust Signals
This script combines the EMA, which helps identify the overall trend direction, with the RSI, which measures the speed and change of price movements to identify overbought and oversold conditions.
The combination ensures that you get high-probability signals by leveraging both trend-following and momentum-based indicators.
Multiple Timeframe Analysis
Analyze the EMA and RSI across different timeframes to gain a comprehensive view of market conditions and make more informed trading decisions.
Reversing and Extending Signals
Reverse signals generated by indicators to adapt to various market conditions.
Extend signals by specifying conditions such as "RSI cross AND EMA cross WITHIN 2 bars" to capture more nuanced trading opportunities.
Backtesting and Risk Management
Evaluate the performance of your strategies by feeding the results into a backtesting engine.
The strategy risks a maximum of 10% of the account on a single trade to maintain sustainable risk levels.
Available Indicators:
EMA (Exponential Moving Average)
Helps identify the overall trend direction.
Signals:
Long Entry: When the price closes above the EMA.
Short Entry: When the price closes below the EMA.
RSI (Relative Strength Index)
Measures the speed and change of price movements.
Signals:
Long Entry: When RSI is below the oversold level (30).
Short Entry: When RSI is above the overbought level (70).
How It Works:
Long Entry: A buy signal is generated when the price closes above the EMA and the RSI is below the oversold level (30). This indicates that the price is in an upward trend and temporarily oversold, presenting a potential buying opportunity.
Short Entry: A sell signal is generated when the price closes below the EMA and the RSI is above the overbought level (70). This indicates that the price is in a downward trend and temporarily overbought, presenting a potential selling opportunity.
Close Long Position: The script closes long positions when the conditions for a short entry are met.
Close Short Position: The script closes short positions when the conditions for a long entry are met.
Parameters:
EMA Length: 20 (default)
RSI Length: 14 (default)
RSI Overbought Level: 70 (default)
RSI Oversold Level: 30 (default)
Initial Capital: 10,000 USDT (default) – Realistic starting capital for an average trader.
Commission: 0.1% (default) – Reflects typical trading commissions.
Slippage: 0.5 ticks (default) – Accounts for market conditions and potential price slippage during order execution.
Backtesting:
Trading Range: – Ensure that the dataset used covers a significant period to generate a sufficient number of trades.
Dataset Limitation: Due to TradingView Premium's limitation of backtesting only 20,000 candles, it may not be possible to generate more than 100 trades. This limitation affects the statistical relevance of the backtesting results, but the strategy has been tested to provide meaningful insights within these constraints.
Use Case:
This strategy combines the EMA and RSI to identify potential trading opportunities by detecting trend direction and overbought/oversold conditions. It is particularly effective in volatile markets where quick trend reversals are common.
How to Use:
Set the parameters according to your preference or use the default values.
Run the script on the EGLD/USDT.P pair with a 5-minute interval.
Monitor the signals and adjust your trades accordingly.
trend_switch
█ Description
Asset price data was time series data, commonly consisting of trends, seasonality, and noise. Many applicable indicators help traders to determine between trend or momentum to make a better trading decision based on their preferences. In some cases, there is little to no clear market direction, and price range. It feels much more appropriate to use a shorter trend identifier, until clearly defined market trend. The indicator/strategy developed with the notion aims to automatically switch between shorter and longer trend following indicator. There were many methods that can be applied and switched between, however in this indicator/strategy will be limited to the use of predictive moving average and MESA adaptive moving average (Ehlers), by first determining if there is a strong trend identified by calculating the slope, if slope value is between upper and lower threshold assumed there is not much price direction.
█ Formula
// predictive moving average
predict = (2*wma1-wma2)
trigger = (4*predict+3*predict +2*predict *predict)
// MESA adaptive moving average
mama = alpha*src+(1-alpha)*mama
fama = .5*alpha*mama+(1-.5-alpha)*fama
█ Feature
The indicator will have a specified default parameter of:
source = ohlc4
lookback period = 10
threshold = 10
fast limit = 0.5
slow limit = 0.05
Strategy type can be switched between Long/Short only and Long-Short strategy
Strategy backtest period
█ How it works
If slope between the upper (red) and lower (green) threshold line, assume there is little to no clear market direction, thus signal predictive moving average indicator
If slope is above the upper (red) or below the lower (green) threshold line, assume there is a clear trend forming, the signal generated from the MESA adaptive moving average indicator
█ Example 1 - Slope fall between the Threshold - activate shorter trend
█ Example 2 - Slope fall above/below Threshold - activate longer trend
Triple EMA + QQE Trend Following Strategy [TradeDots]The "Triple EMA + QQE Trend Following Strategy" harnesses the power of two sophisticated technical indicators, the Triple Exponential Moving Average (TEMA) and the Qualitative Quantitative Estimation (QQE), to generate precise buy and sell signals. This strategy excels in capturing shifts in trends by identifying short-term price momentum and dynamic overbought or oversold conditions.
HOW IT WORKS
This strategy integrates two pivotal indicators:
Triple Exponential Moving Average (TEMA): TEMA enhances traditional moving averages by reducing lag and smoothing the data more effectively. It achieves this by applying the EMA formula three times onto the price, as follows:
tema(src, length) =>
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
ema3 = ta.ema(ema2, length)
tema = 3*ema1 - 3*ema2 + ema3
This computation helps to sharpen the sensitivity to price movements.
Qualitative Quantitative Estimation (QQE): The QQE indicator improves upon the standard RSI by incorporating a smoothing mechanism. It starts with the standard RSI, overlays a 5-period EMA on this RSI, and then enhances the result using a double application of a 27-period EMA. A slow trailing line is then derived by multiplying the result with a factor number. This approach establishes a more refined and less jittery trend-following signal, complementing the TEMA to enhance overall market timing during fluctuating conditions.
APPLICATION
Referenced from insights on "Trading Tact," the strategy implementation follows:
First of all, we utilize two TEMA lines: one set at a 20-period and the other at a 40-period. Then following the rules below:
40-period TEMA is rising
20-period TEMA is above 40-period TEMA
Price closes above 20-period TEMA
Today is not Monday
RSI MA crosses the Slow trailing line
This strategy does not employ an active take profit mechanism; instead, it utilizes a trailing stop loss to allow the price to reach the stop loss naturally, thereby maximizing potential profit margins.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Reference:
Trading Tact. What Is the QQE Indicator? Retrieved from: tradingtact.com
Price and Volume Breakout Buy Strategy [TradeDots]The "Price and Volume Breakout Buy Strategy" is a trading strategy designed to identify buying opportunities by detecting concurrent price and volume breakouts over a specified range of candlesticks.
This strategy is optimized for assets demonstrating high volatility and significant momentum spikes.
HOW IT WORKS
The strategy first takes the specific number of candlesticks as the examination window for both price and volume.
These values are used as benchmarks to identify breakout conditions.
A trade is initiated when both the closing price and the trading volume surpass the maximum values observed within the predetermined window.
Price must be above a designated moving average, serving as the trend indicator, ensuring that all trades align with the prevailing market trend.
APPLICATION
This strategy is particularly effective for highly volatile assets such as Bitcoin and Ethereum, capitalizing on the cues from sudden price and volume breakouts indicative of significant market movement, often driven by market smart money traders.
However, for broader markets like the S&P 500, this strategy may be less effective due to less pronounced volume and price shifts compared to the cryptocurrency markets.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 70%
Backtest result sometimes gives fewer than 100 trades under certain higher timeframes, as most trades tend to have a long holding period. Entry conditions are also more stringent, which, combined with the relatively brief history of cryptocurrencies, results in fewer trades on longer timeframes.
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
