Buysell — Penunjuk dan Strategi

Reversal Trading Bot Strategy[BullByte]Overview : The indicator Reversal Trading Bot Strategy is crafted to capture potential market reversal points by combining momentum, volatility, and trend alignment filters. It uses a blend of technical indicators to identify both bullish and bearish reversal setups, ensuring that multiple market conditions are met before entering a trade. Core Components : Technical Indicators Used : RSI (Relative Strength Index) : Purpose : Detects divergence conditions by comparing recent lows/highs in price with the RSI. Parameter : Length of 8. Bollinger Bands (BB) : Purpose : Measures volatility and identifies price levels that are statistically extreme. Parameter : Length of 20 and a 2-standard deviation multiplier. ADX (Average Directional Index) & DMI (Directional Movement Index) : Purpose : Quantifies the strength of the trend. The ADX threshold is set at 20, and additional filters check for the alignment of the directional indicators (DI+ and DI–). ATR (Average True Range) : Purpose : Provides a volatility measure used to set stop levels and determine risk through trailing stops. Volume SMA (Simple Moving Average of Volume ): Purpose : Helps confirm strength by comparing the current volume against a 20-period average, with an optional filter to ensure volume is at least twice the SMA. User-Defined Toggle Filters : Volume Filter : Confirms that the volume is above average (or twice the SMA) before taking trades. ADX Trend Alignment Filter : Checks that the ADX’s directional indicators support the trade direction. BB Close Confirmation : Optionally refines the entry by requiring price to be beyond the upper or lower Bollinger Band rather than just above or below. RSI Divergence Exit : Allows the script to close positions if RSI divergence is detected. BB Mean Reversion Exit : Closes positions if the price reverts to the Bollinger Bands’ middle line. Risk/Reward Filter : Ensures that the potential reward is at least twice the risk by comparing the distance to the Bollinger Band with the ATR. Candle Movement Filter : Optional filter to require a minimum percentage move in the candle to confirm momentum. ADX Trend Exit : Closes positions if the ADX falls below the threshold and the directional indicators reverse. Entry Conditions : Bullish Entry : RSI Divergence : Checks if the current close is lower than a previous low while the RSI is above the previous low, suggesting bullish divergence. Bollinger Confirmation : Requires that the price is above the lower (or upper if confirmation is toggled) Bollinger Band. Volume & Trend Filters : Combines volume condition, ADX strength, and an optional candle momentum condition. Risk/Reward Check : Validates that the trade meets a favorable risk-to-reward ratio. Bearish Entry : Uses a mirror logic of the bullish entry by checking for bearish divergence, ensuring the price is below the appropriate Bollinger level, and confirming volume, trend strength, candle pattern, and risk/reward criteria. Trade Execution and Exit Strateg y: Trade Execution : Upon meeting the entry conditions, the strategy initiates a long or short position. Stop Loss & Trailing Stops : A stop-loss is dynamically set using the ATR value, and trailing stops are implemented as a percentage of the close price. Exit Conditions : Additional exit filters can trigger early closures based on RSI divergence, mean reversion (via the middle Bollinger Band), or a weakening trend as signaled by ADX falling below its threshold. This multi-layered exit strategy is designed to lock in gains or minimize losses if the market begins to reverse unexpectedly. How the Strategy Works in Different Market Conditions : Trending Markets : The ADX filter ensures that trades are only taken when the trend is strong. When the market is trending, the directional movement indicators help confirm the momentum, making the reversal signal more reliable. Ranging Markets : In choppy markets, the Bollinger Bands expand and contract, while the RSI divergence can highlight potential turning points. The optional filters can be adjusted to avoid false signals in low-volume or low-volatility conditions. Volatility Management : With ATR-based stop-losses and a risk/reward filter, the strategy adapts to current market volatility, ensuring that risk is managed consistently. Recommendation on using this Strategy with a Trading Bot : This strategy is well-suited for high-frequency trading (HFT) due to its ability to quickly identify reversal setups and execute trades dynamically with automated stop-loss and trailing exits. By integrating this script with a TradingView webhook-based bot or an API-driven execution system, traders can automate trade entries and exits in real-time, reducing manual execution delays and capitalizing on fast market movements. Disclaimer : This script is provided for educational and informational purposes only. It is not intended as investment advice. Trading involves significant risk, and you should always conduct your own research and analysis before making any trading decisions. The author is not responsible for any losses incurred while using this script.

Adaptive Fibonacci Pullback System -FibonacciFlux Adaptive Fibonacci Pullback System (AFPS) - FibonacciFlux This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Original concepts by FibonacciFlux. Abstract The Adaptive Fibonacci Pullback System (AFPS) presents a sophisticated, institutional-grade algorithmic strategy engineered for high-probability trend pullback entries. Developed by FibonacciFlux, AFPS uniquely integrates a proprietary Multi-Fibonacci Supertrend engine (0.618, 1.618, 2.618 ratios) for harmonic volatility assessment, an Adaptive Moving Average (AMA) Channel providing dynamic market context, and a synergistic Multi-Timeframe (MTF) filter suite (RSI, MACD, Volume). This strategy transcends simple indicator combinations through its strict, multi-stage confluence validation logic. Historical simulations suggest that specific MTF filter configurations can yield exceptional performance metrics, potentially achieving Profit Factors exceeding 2.6 , indicative of institutional-level potential, while maintaining controlled risk under realistic trading parameters (managed equity risk, commission, slippage). 4 hourly MTF filtering 1. Introduction: Elevating Pullback Trading with Adaptive Confluence Traditional pullback strategies often struggle with noise, false signals, and adapting to changing market dynamics. AFPS addresses these challenges by introducing a novel framework grounded in Fibonacci principles and adaptive logic. Instead of relying on static levels or single confirmations, AFPS seeks high-probability pullback entries within established trends by validating signals through a rigorous confluence of: Harmonic Volatility Context: Understanding the trend's stability and potential turning points using the unique Multi-Fibonacci Supertrend. Adaptive Market Structure: Assessing the prevailing trend regime via the AMA Channel. Multi-Dimensional Confirmation: Filtering signals with lower-timeframe Momentum (RSI), Trend Alignment (MACD), and Market Conviction (Volume) using the MTF suite. The objective is to achieve superior signal quality and adaptability, moving beyond conventional pullback methodologies. 2. Core Methodology: Synergistic Integration AFPS's effectiveness stems from the engineered synergy between its core components: 2.1. Multi-Fibonacci Supertrend Engine: Utilizes specific Fibonacci ratios (0.618, 1.618, 2.618) applied to ATR, creating a multi-layered volatility envelope potentially resonant with market harmonics. The averaged and EMA-smoothed result (`smoothed_supertrend`) provides a robust, dynamic trend baseline and context filter. // Key Components: Multi-Fibonacci Supertrend & Smoothing average_supertrend = (supertrend1 + supertrend2 + supertrend3) / 3 smoothed_supertrend = ta.ema(average_supertrend, st_smooth_length) 2.2. Adaptive Moving Average (AMA) Channel: Provides dynamic market context. The `ama_midline` serves as a key filter in the entry logic, confirming the broader trend bias relative to adaptive price action. Extended Fibonacci levels derived from the channel width offer potential dynamic S/R zones. // Key Component: AMA Midline ama_midline = (ama_high_band + ama_low_band) / 2 2.3. Multi-Timeframe (MTF) Filter Suite: An optional but powerful validation layer (RSI, MACD, Volume) assessed on a lower timeframe. Acts as a **validation cascade** – signals must pass all enabled filters simultaneously. 2.4. High-Confluence Entry Logic: The core innovation. A pullback entry requires a specific sequence and validation: Price interaction with `average_supertrend` and recovery above/below `smoothed_supertrend`. Price confirmation relative to the `ama_midline`. Simultaneous validation by all enabled MTF filters. // Simplified Long Entry Logic Example (incorporates key elements) long_entry_condition = enable_long_positions and (low < average_supertrend and close > smoothed_supertrend) and // Pullback & Recovery (close > ama_midline and close > ama_midline) and // AMA Confirmation (rsi_filter_long_ok and macd_filter_long_ok and volume_filter_ok) // MTF Validation This strict, multi-stage confluence significantly elevates signal quality compared to simpler pullback approaches. 1hourly filtering 3. Realistic Implementation and Performance Potential AFPS is designed for practical application, incorporating realistic defaults and highlighting performance potential with crucial context: 3.1. Realistic Default Strategy Settings: The script includes responsible default parameters: strategy('Adaptive Fibonacci Pullback System - FibonacciFlux', shorttitle = "AFPS", ..., initial_capital = 10000, // Accessible capital default_qty_type = strategy.percent_of_equity, // Equity-based risk default_qty_value = 4, // Default 4% equity risk per initial trade commission_type = strategy.commission.percent, commission_value = 0.03, // Realistic commission slippage = 2, // Realistic slippage pyramiding = 2 // Limited pyramiding allowed ) Note: The default 4% risk (`default_qty_value = 4`) requires careful user assessment and adjustment based on individual risk tolerance. 3.2. Historical Performance Insights & Institutional Potential: Backtesting provides insights into historical behavior under specific conditions (always specify Asset/Timeframe/Dates when sharing results): Default Performance Example: With defaults, historical tests might show characteristics like Overall PF ~1.38, Max DD ~1.16%, with potential Long/Short performance variance (e.g., Long PF 1.6+, Short PF < 1). Optimized MTF Filter Performance: Crucially, historical simulations demonstrate that meticulous configuration of the MTF filters (particularly RSI and potentially others depending on market) can significantly enhance performance. Under specific, optimized MTF filter settings combined with appropriate risk management (e.g., 7.5% risk), historical tests have indicated the potential to achieve **Profit Factors exceeding 2.6**, alongside controlled drawdowns (e.g., ~1.32%). This level of performance, if consistently achievable (which requires ongoing adaptation), aligns with metrics often sought in institutional trading environments. Disclaimer Reminder: These results are strictly historical simulations. Past performance does not guarantee future results. Achieving high performance requires careful parameter tuning, adaptation to changing markets, and robust risk management. 3.3. Emphasizing Risk Management: Effective use of AFPS mandates active risk management. Utilize the built-in Stop Loss, Take Profit, and Trailing Stop features. The `pyramiding = 2` setting requires particularly diligent oversight. Do not rely solely on default settings. 4. Conclusion: Advancing Trend Pullback Strategies The Adaptive Fibonacci Pullback System (AFPS) offers a sophisticated, theoretically grounded, and highly adaptable framework for identifying and executing high-probability trend pullback trades. Its unique blend of Fibonacci resonance, adaptive context, and multi-dimensional MTF filtering represents a significant advancement over conventional methods. While requiring thoughtful implementation and risk management, AFPS provides discerning traders with a powerful tool potentially capable of achieving institutional-level performance characteristics under optimized conditions. Acknowledgments Developed by FibonacciFlux. Inspired by principles of Fibonacci analysis, adaptive averaging, and multi-timeframe confirmation techniques explored within the trading community. Disclaimer Trading involves substantial risk. AFPS is an analytical tool, not a guarantee of profit. Past performance is not indicative of future results. Market conditions change. Users are solely responsible for their decisions and risk management. Thorough testing is essential. Deploy at your own considered risk.

Simple APF Strategy Backtesting [The Quant Science]Simple backtesting strategy for the quantitative indicator Autocorrelation Price Forecasting. This is a Buy & Sell strategy that operates exclusively with long orders. It opens long positions and generates profit based on the future price forecast provided by the indicator. It's particularly suitable for trend-following trading strategies or directional markets with an established trend. Main functions 1. Cycle Detection: Utilize autocorrelation to identify repetitive market behaviors and cycles. 2. Forecasting for Backtesting: Simulate trades and assess the profitability of various strategies based on future price predictions. Logic The strategy works as follow: Entry Condition: Go long if the hypothetical gain exceeds the threshold gain (configurable by user interface). Position Management: Sets a take-profit level based on the future price. Position Sizing: Automatically calculates the order size as a percentage of the equity. No Stop-Loss: this strategy doesn't includes any stop loss. Example Use Case A trader analyzes a dayli period using 7 historical bars for autocorrelation. Sets a threshold gain of 20 points using a 5% of the equity for each trade. Evaluates the effectiveness of a long-only strategy in this period to assess its profitability and risk-adjusted performance. User Interface Length: Set the length of the data used in the autocorrelation price forecasting model. Thresold Gain: Minimum value to be considered for opening trades based on future price forecast. Order Size: percentage size of the equity used for each single trade. Strategy Limit This strategy does not use a stop loss. If the price continues to drop and the future price forecast is incorrect, the trader may incur a loss or have their capital locked in the losing trade. Disclaimer! This is a simple template. Use the code as a starting point rather than a finished solution. The script does not include important parameters, so use it solely for educational purposes or as a boilerplate.

Ehlers Combo Strategy 🚀 Presenting the Enhanced Ehlers Combo Strategy 🚀 Hello Traders! 👋 I'm thrilled to share the latest version of the Ehlers Combo Strategy v2.0. This powerful algorithm combines Ehlers Elegant Oscillator, Decycler, Instantaneous Trendline, Spearman Rank, and introduces the Signal to Noise Ratio for even more precise trading signals. 📊 Strategy Highlights: Ehlers Elegant Oscillator: Captures market momentum and turning points. Ehlers Decycler: Filters out market noise for clearer trend signals. Instantaneous Trendline: Offers a dynamic view of the market trend. Spearman Rank: Analyzes market rank correlations for enhanced insights. Signal to Noise Ratio (SNR): Filters out noise for more accurate signals. 💡 Key Features & Customizations: Adaptive Length: Enable adaptive length based on the market's current conditions. SNR Threshold: Set your desired SNR threshold for filtering signals. Exit Length: Define the length for exit signals. 📈 Trading Signals: Long Entry: Elegant Oscillator and Decycler cross above 0, source crosses above Decycler, source is greater than an increasing Instantaneous Trendline, Spearman Rank is positive, and SNR exceeds the threshold. Long Exit: Source crosses below the Instantaneous Trendline after entering a long position. Short Entry: Elegant Oscillator and Decycler cross below 0, source crosses below Decycler, source is less than a decreasing Instantaneous Trendline, Spearman Rank is negative, and SNR exceeds the threshold. Short Exit: Source crosses above the Instantaneous Trendline after entering a short position. 📊 Insights & Enhancements: Dynamic Length: The strategy adapts its length dynamically based on market conditions. Improved SNR: Signal to Noise Ratio ensures better filtering of signals. Enhanced Visualization: The Elegant Oscillator now features improved color coding for a clearer interpretation. 🚨 Disclaimer: Trading involves risk, and this script should be used judiciously. It's not a guaranteed profit machine, but with careful use, it can be a valuable addition to your toolkit. Feel free to backtest, tweak, and make it your own! Let's conquer the markets together! 💪📈 🚀✨ Happy Trading! ✨🚀 --- 🙌 Credits: A big shoutout to the original contributors: @blackcat1402 @cheatcountry @DasanC

72s Strat: Backtesting Adaptive HMA+ pt.1 This is a follow up to my previous publication of Adaptive HMA+ few months ago, as a mean to provide some kind of initial backtesting tools. Which can be use to explore many possible strategies, optimise its settings to better conform user's pair/tf, and hopefully able to help tweaking your general strategy. If you haven't read the study or use the indicator, kindly go here first to get the overall idea. The first strategy introduce in this backtest is one most basic already described in the study; buy/sell is when movement is there and everything is on the right side; When RSI has turned to other side, we can use it as exit point (if in profit of course, else just let it hit our TP/SL, why would we exit before profit). Also, base on RSI when we make entry, we can further differentiate type of signals. --Please check all comments in code directly where the signals , entries , and exits section are. Second additional strategy to check; is when we also use second faster Adaptive HMA+ for exit. So this is like a double orders on a signal but with different exit-rule (/more on this on snapshots below). Alternatively, you can also work the code so to only use this type of exit. There's also an additional feature which you can enable its visuals, the Distance Zone , is to help measuring price distance to our xHMA+. It's just a simple atr based envelope really, I already put the sample code in study's comment section, but better gonna update it there directly for non-coder too, after this. In this sample I use Lot for order quantity size just because that's what I use on my broker. Also what few friends use while we forward-testing it since the study is published, so we also checked/compared each profit/loss report by real number. To use default or other unit of measurement, change the entry code accordingly. If you change your order size, you should also change the commission in Properties Tab. My broker commission is 5 USD per order/lot, so in there with example order size 0.1 lot I put commission 0.5$ per order (I'll put 2.5$ for 0.5 lot, 10$ for 2 lot, and so on). Crypto usually has higher charge. --It is important that you should fill it base on your broker. SETTINGS I'm trying to keep it short. Please explore it further again. (Beginner should also first get acquaintance with terms use here.) ORDERS: Base Minimum Profit Before Exit: The number is multiplier of ongoing ATR. Means that when basic exit condition is met, algo will check whether you're already in minimum profit or not, if not, let it still run to TP or SL, or until it meets subsequent exit condition, then it will check again. Default Target Profit: Multiplier of ATR at signal. If reached before any eligible exit condition is met, exit TP. Base StopLoss Point: You can change directly in code to use other like ATR Trailing SL, fix percent SL, or whatever. In the sample, 4 options provided. Maximum StopLoss: This is like a safety-net, that if at some point your chosen SL point from input above happens to be exceeding this maximum input that you can tolerate, then this max point is the one will be use as SL. Activate 2nd order...: The additional doubling of certain buy/sell with different exits as described above. If enable, you should also set pyramiding to at least: 2. If not, it does nothing. ADAPTIVE HMA+ PERIOD Many users already have their own settings for these. So in here I only sample the default as first presented in the study. Make it to your adaptive. MARKET MOVEMENT (1) Now you can check in realtime how much slope degree is best to define your specific pair/tf is out of congestion (yellow) area. And (2) also able to check directly what ATR lengths are more suitable defining your pair's volatility. DISTANCE ZONE Distance Multiplier. Each pair/tf has its own best distance zone (in xHMA+ perspective). The zone also determine whether a signal should appear or not. (Or what type of signal, if you wanna go more detail in constructing your strategy) USAGE (Provided you already have your own comfortable settings for minimum-maximum period of Adaptive HMA+. Best if you already have backtested it manually too and/or apply as an add-on to your working strategy) 1. In our experiences, first most important to define is both elements in the Market Movement Settings . These also tend to be persistent for whole season since it's kinda describing that pair/tf overall behaviour. Don't worry if you still get a low Profit Factor here, but by tweaking you should start to see positive changes in one of Max Drawdown and Net Profit, or Percent Profitable. 2. Afterwards, find your pair/tf Distance Zone . When optimising this, what we seek is just a "not to bad" equity curves to start forming. At least Max Drawdown should lessen more. Doesn't have to be great already, but should be better, no red in Net Profit. 3. Then go manage the "Trailing Minimum Profit", TP, SL, and max SL. 4. Repeat 1,2,3. 👻 5. Manage order size, commission, and/or enable double-order (need pyramiding) if you like. Check if your equity can handle max drawdown before margin call. 6. After getting an acceptable backtest result, go to List of Trades tab and find the biggest loss or when many sequencing loss in a row happened. Click on it to go to exact point on chart, observe why the signal failed and get at least general idea how it can be prevented . The rest is yours, you should know your pair/tf more than other. You can also re-explore your minimum-maximum period for both Major and minor xHMA+. Keep in mind that all numbers in Setting are conceptually in a form of range . You don't want to get superb equity curves but actually a "fragile" , means one can easily turn it to disaster just by changing only a fraction in one/two of the setting. --- If you just wanna test the strength of the indicator alone, you can disable "Use StopLoss" temporarily while optimising settings. Using no SL might be tempting in overall result data in some cases, but NOTE: It is not recommended to not using SL, don't forget that we deliberately enter when it's in high volatility. If want to add flexibility or trading for long-term, just maximise your SL. ie.: chose SL Point>ATR only and set it maximum. (Check your max drawdown after this). I think this is quite important specially for beginners, so here's an example; Hypothetically in below scenario, because of some settings, the buy order after the loss sell signal didn't appear. Let's say if our initial capital only 1000$ using leverage and order size 0,5 lot (risky position sizing already), moreover if this happens at the beginning of your trading season, that's half of account gone already in one trade . Your max SL should've made you exit after that pumping bar. The Trailing Minimum Profit is actually look like this. Search in the code if you want to plot it. I just don't like too many lines on chart. To maximise profit we can try enabling double-order. The only added rule coded is: RSI should rising when buy and falling when sell. 2nd signal will appears above or below default buy/sell signal. (Of course it's also prone to double-loss, re-check your max drawdown after. Profit factor play its part in here for a long run). Snapshot in comparison: Two default sell signals on left closed at RSI exit, the additional sell signal closed later on when price crossover minor xHMA+. On buy side, price haven't met our minimum profit when first crossunder minor xHMA+. If later on we hit SL on this "+buy" signal, at least we already profited from default buy signal. You can also consider/treat this as multiple TP points. For longer-term trading, what you need to maximise is the Minimum Profit , so it won't exit whenever an exit condition happened, it can happen several times before reaching minimum profit. Hopefully this snapshot can explain: Notice in comparison default sell and buy signal now close in average after 3 days. What's best is when we also have confirmation from higher TF. It's like targeting higher TF by entering from smaller TF. As also mention in the study, we can still experiment via original HMA by putting same value for minimum-maximum period setting. This is experimental EU 1H with Major xHMA+: 144-144, Flat market 13, Distance multiplier 3.6, with 2nd order activated. Kiwi was a bit surprising for me. It's flat market is effectively below 6, with quite far distance zone of 3.5. Probably because I'm using big numbers in adaptive period. --- The result you see in strategy tester report below for EURUSD 15m is using just default settings you see in code, as follow: 0,1 lot for each order (which is the smallest allowed by my broker). No pyramiding. Commission: 0.5 usd per order. Slippage: 3 Opening position is only using basic strategy #1 (RSI exit). Additional exit not activated. Minimum Profit: 1. TP: 3. SL use: Half-distance zone. Max SL: 4.5. Major xHMA+: 172-233. minor xHMA+: 89-121 Distance Zone Multiplier: 2.7 RSI: Standard 14. (From our forward-testing, the difference we get from net profit is because of the spread, our entry isn't exactly at the close/open price. Not so much though, but not the same. If somebody can direct me to any example where we can code our entry via current bid/ask price, that would be awesome!) It's already a long post (sorry), think I'm gonna pause here. Check out the code :) --- DISCLAIMER: Past performance is no guarantee of future results , and so on.. you know the drill ;) Please read whole description first before using, don't take 1-2 paragraph and claim it's the whole logic, you are responsible of your own actions and understanding.