立即開始

Educational

Random State Machine Strategy📌 Random State Machine Strategy (Educational) This strategy showcases a randomized entry model driven by a finite state machine, integrated with user-defined exit controls and a full-featured moving average filter. 🧠 Trade Entry Logic Entries occur only when: A random trigger occurs (~5% probability per bar) The state machine accepts a new transition (sm.step()) Price is: Above the selected MA for long entries Below the selected MA for short entries This ensures that entries are both stochastically driven and trend-aligned, avoiding frequent or arbitrary trades. ⚙️ How It Works Randomized Triggers A pseudo-random generator (seeded with time and volume) attempts to trigger state transitions. Finite State Machine Transitions are managed using the StateMachine from robbatt/lib_statemachine — credit to @robbatt for the modular FSM design. Controlled Reset The state machine resets every N bars (default: 100) if at least two transitions have occurred. This prevents stale or locked states. Backtest Range Define a specific test window using Start and End Date inputs. Risk & Exits Specify risk in points and a target risk/reward ratio. TP is auto-computed. Timed and MA-based exits can be toggled. 🧪 How to Use Enable Long or Short trades Choose your Moving Average type and length Set Risk per trade and R/R ratio Toggle TP/SL, timed exit, or MA cross exit Adjust the State Reset Interval to suit your signal frequency 📘 Notes Educational use only — not financial advice Random logic is used to model structure, not predict movement Thanks to @robbatt for the lib_statemachine integration
Pine Script®策略
由smoyer提供
Multi-Indicator Trend-Following Strategy v6Multi-Indicator Trend-Following Strategy v6 This strategy uses a combination of technical indicators to identify potential trend-following trade entries and exits. It is intended for educational and research purposes. How it works: Moving Averages (EMA): Entry signals are generated on crossovers between a fast and slow exponential moving average. RSI Filter: Confirms momentum with a threshold above/below 50 for long/short entries. Volume Confirmation: Requires volume to exceed a moving average multiplied by a user-defined factor. ATR-Based Risk Management: Stop loss and take profit levels are calculated using the Average True Range (ATR), allowing for dynamic risk control based on market volatility. Customizable Inputs: Fast/Slow MA lengths RSI length and levels MACD settings (used in calculation, not directly in signal) Volume MA and multiplier ATR period and multipliers for stop loss and take profit Notes: This strategy does not guarantee future results. It is provided for analysis and backtesting only. Alerts are available for buy/sell conditions. Feel free to adjust parameters to explore different market conditions and asset classes.
Pine Script®策略
由rockingtoor提供
magic wand STSM"Magic Wand STSM" Strategy: Trend-Following with Dynamic Risk Management Overview: The "Magic Wand STSM" (Supertrend & SMA Momentum) is an automated trading strategy designed to identify and capitalize on sustained trends in the market. It combines a multi-timeframe Supertrend for trend direction and potential reversal signals, along with a 200-period Simple Moving Average (SMA) for overall market bias. A key feature of this strategy is its dynamic position sizing based on a user-defined risk percentage per trade, and a built-in daily and monthly profit/loss tracking system to manage overall exposure and prevent overtrading. How it Works (Underlying Concepts): Multi-Timeframe Trend Confirmation (Supertrend): The strategy uses two Supertrend indicators: one on the current chart timeframe and another on a higher timeframe (e.g., if your chart is 5-minute, the higher timeframe Supertrend might be 15-minute). Trend Identification: The Supertrend's direction output is crucial. A negative direction indicates a bearish trend (price below Supertrend), while a positive direction indicates a bullish trend (price above Supertrend). Confirmation: A core principle is that trades are only considered when the Supertrend on both the current and the higher timeframe align in the same direction. This helps to filter out noise and focus on stronger, more confirmed trends. For example, for a long trade, both Supertrends must be indicating a bearish trend (price below Supertrend line, implying an uptrend context where price is expected to stay above/rebound from Supertrend). Similarly, for short trades, both must be indicating a bullish trend (price above Supertrend line, implying a downtrend context where price is expected to stay below/retest Supertrend). Trend "Readiness": The strategy specifically looks for situations where the Supertrend has been stable for a few bars (checking barssince the last direction change). Long-Term Market Bias (200 SMA): A 200-period Simple Moving Average is plotted on the chart. Filter: For long trades, the price must be above the 200 SMA, confirming an overall bullish bias. For short trades, the price must be below the 200 SMA, confirming an overall bearish bias. This acts as a macro filter, ensuring trades are taken in alignment with the broader market direction. "Lowest/Highest Value" Pullback Entries: The strategy employs custom functions (LowestValueAndBar, HighestValueAndBar) to identify specific price action within the recent trend: For Long Entries: It looks for a "buy ready" condition where the price has found a recent lowest point within a specific number of bars since the Supertrend turned bearish (indicating an uptrend). This suggests a potential pullback or consolidation before continuation. The entry trigger is a close above the open of this identified lowest bar, and also above the current bar's open. For Short Entries: It looks for a "sell ready" condition where the price has found a recent highest point within a specific number of bars since the Supertrend turned bullish (indicating a downtrend). This suggests a potential rally or consolidation before continuation downwards. The entry trigger is a close below the open of this identified highest bar, and also below the current bar's open. Candle Confirmation: The strategy also incorporates a check on the candle type at the "lowest/highest value" bar (e.g., closevalue_b < openvalue_b for buy signals, meaning a bearish candle at the low, suggesting a potential reversal before a buy). Risk Management and Position Sizing: Dynamic Lot Sizing: The lotsvalue function calculates the appropriate position size based on your Your Equity input, the Risk to Reward ratio, and your risk percentage for your balance % input. This ensures that the capital risked per trade remains consistent as a percentage of your equity, regardless of the instrument's volatility or price. The stop loss distance is directly used in this calculation. Fixed Risk Reward: All trades are entered with a predefined Risk to Reward ratio (default 2.0). This means for every unit of risk (stop loss distance), the target profit is rr times that distance. Daily and Monthly Performance Monitoring: The strategy tracks todaysWins, todaysLosses, and res (daily net result) in real-time. A "daily profit target" is implemented (day_profit): If the daily net result is very favorable (e.g., res >= 4 with todaysLosses >= 2 or todaysWins + todaysLosses >= 8), the strategy may temporarily halt trading for the remainder of the session to "lock in" profits and prevent overtrading during volatile periods. A "monthly stop-out" (monthly_trade) is implemented: If the lres (overall net result from all closed trades) falls below a certain threshold (e.g., -12), the strategy will stop trading for a set period (one week in this case) to protect capital during prolonged drawdowns. Trade Execution: Entry Triggers: Trades are entered when all buy/sell conditions (Supertrend alignment, SMA filter, "buy/sell situation" candle confirmation, and risk management checks) are met, and there are no open positions. Stop Loss and Take Profit: Stop Loss: The stop loss is dynamically placed at the upTrendValue for long trades and downTrendValue for short trades. These values are derived from the Supertrend indicator, which naturally adjusts to market volatility. Take Profit: The take profit is calculated based on the entry price, the stop loss, and the Risk to Reward ratio (rr). Position Locks: lock_long and lock_short variables prevent immediate re-entry into the same direction once a trade is initiated, or after a trend reversal based on Supertrend changes. Visual Elements: The 200 SMA is plotted in yellow. Entry, Stop Loss, and Take Profit lines are plotted in white, red, and green respectively when a trade is active, with shaded areas between them to visually represent risk and reward. Diamond shapes are plotted at the bottom of the chart (green for potential buy signals, red for potential sell signals) to visually indicate when the buy_sit or sell_sit conditions are met, along with other key filters. A comprehensive trade statistics table is displayed on the chart, showing daily wins/losses, daily profit, total deals, and overall profit/loss. A background color indicates the active trading session. Ideal Usage: This strategy is best applied to instruments with clear trends and sufficient liquidity. Users should carefully adjust the Your Equity, Risk to Reward, and risk percentage inputs to align with their individual risk tolerance and capital. Experimentation with different ATR Length and Factor values for the Supertrend might be beneficial depending on the asset and timeframe.
Pine Script®策略
由hassansadeghirashed提供
2
Hybrid: RSI + Breakout + DashboardHybrid RSI + Breakout Strategy Adaptive trading system that switches modes based on market regime: Ranging: Buys when RSI < 30 and sells when RSI > 70. Trending: Enters momentum breakouts only in the direction of the 200-EMA bias, with ADX confirming trend strength. Risk Management: Trailing stop locks profits and caps drawdown. Optimized for BTC, ETH, and SOL on 1 h–1 D charts; back-tested from 2017 onward. Educational use only—run your own tests before deploying live funds.
Pine Script®策略
由RugSurvivor提供
31
Anomaly Counter-Trend StrategyA mean-reversion style strategy that automatically spots unusually large price moves over a configurable lookback period and takes the opposite side, with full risk-management, commission and slippage modeling—built in Pine Script® v6. 🔎 Overview ACTS monitors the percent-change over the past N minutes and, when that move exceeds your chosen threshold, enters a counter-trend position (short on a strong rise; long on a sharp fall). It’s ideal for markets that often “overshoot” and snap back, and can be applied on any symbol or timeframe. ⚙️ Key Features Anomaly Detection: Detect abnormal price swings based on a user-defined % change over a lookback period. Counter-Trend Entries: Auto-enter short on rise anomalies, long on fall anomalies (with seamless flat↔reverse transitions). Risk Management: Configurable stop-loss and take-profit in ticks per trade. Realistic Modeling: Simulates commissions (0.05 % default), slippage (2 ticks), and percent-of-equity sizing. Immediate Bar-Close Execution: Orders processed on bar close for faster fills. Visual Aids: Optional on-chart BUY/SELL triangles and background highlights during anomaly periods. ⚙️ Inputs Input Default Description Percentage Threshold (%) 2.00 Min % move over lookback to trigger an anomaly. Lookback Period (Minutes) 15 Number of minutes over which to measure change. Stop Loss (Ticks) 100 Distance from entry for stop-loss exit. Take Profit (Ticks) 200 Distance from entry for take-profit exit. Plot Trade Signal Shapes (on/off) true Show BUY/SELL triangles on chart. Highlight Anomaly Background true Shade background during anomaly bars. 📊 How to Use Add to Chart: Apply the script to any ticker & timeframe. Tune: Adjust your percentage threshold and lookback to match each instrument’s volatility. Review Backtest: Check built-in strategy performance (drawdown, Sharpe, etc.) under the Strategy Tester tab. Go Live: Once optimized, link to alerts or your trade execution system. ⚠️ Disclaimer This script is provided “as-is” for educational purposes and backtesting only. Past performance does not guarantee future results. Always backtest thoroughly, manage your own risk, and consider market conditions before live trading. Enjoy experimenting—and may your counter-trend entries catch the next big snapback!
Pine Script®策略
由gamblerino提供
1
Timeframe StrategyThis is a multi-timeframe trading strategy inspired by Ross Cameron's style, optimized for scalping and trend-following across various timeframes (1m, 5m, 15m, 1h, and 1D). The strategy integrates a comprehensive set of technical indicators, dynamic risk management, and visual tools. Core Features Dynamic Take Profit, Stop Loss & Trailing Stop > Separate settings per timeframe for: -TP% (Take Profit) -SL% (Stop Loss) -Trailing Stop % -Cooldown bars > Configurable via UI inputs. >Smart Entry Conditions Bullish entry: EMA9 crossover EMA20 and EMA50 > EMA200 Bearish entry: EMA9 crossunder EMA20 and EMA50 < EMA200 >Additional confirmation filters: -Volume Filter (enabled/disabled via UI) -Time Filter (e.g., only between 15:00–20:00 UTC) -Spike Filter: rejects high-volatility candles -RSI Filter: above/below 50 for trend confirmation -ADX Filter (only applied on 1m, e.g., ADX > 15) -Micro-Volatility Filter: minimum range percentage (1m only) -Trend Filter (1m only): price must be above/below EMA200 >Trailing Stop Logic -Configurable for each timeframe. - Optional via toggle (use_trailing). >Trade Cooldown Logic -Prevents consecutive trades within X bars, configurable per timeframe. >Technical Indicators Used -EMA 9 / 20 / 50 / 200 -VWAP -RSI (14) -ATR (14) for volatility-based spike filtering -Custom-calculated ADX (14) (manually implemented) >Visual Elements 🔼/🔽 Entry signals (long/short) plotted on the chart. 📉 Table in bottom-left: Displays current values of EMA/VWAP/volume/ATR/ADX. > Optional "Tab info" panel in top-right (toggleable): -Timeframe & strategy settings -Live status of filters (volume, time, cooldown, spike, RSI, ADX, range, trend) -Uses emoji (✅ / ❌) for quick diagnostics. >User Customization -Inputs per timeframe for all key parameters. -Toggle switches for: -Trailing stop -Volume filter -Info table visibility This strategy is designed for active traders seeking a balance between momentum entry, risk control, and adaptability across timeframes. It's ideal for backtesting quick reversals or breakout setups in fast markets, especially at lower timeframes like 1m or 5m.
Pine Script®策略
由alessandrocacchi提供
External Signals Strategy Tester v5External Signals Strategy Tester v5 – User Guide (English) 1. Purpose This Pine Script strategy is a universal back‑tester that lets you plug in any external buy/sell series (for example, another indicator, webhook feed, or higher‑time‑frame condition) and evaluate a rich set of money‑management rules around it – with a single click on/off workflow for every module. 2. Core Workflow Feed signals Buy Signal / Sell Signal inputs accept any series (price, boolean, output of request.security(), etc.). A crossover above 0 is treated as “signal fired”. Date filter Start Date / End Date restricts the test window so you can exclude unwanted history. Trade engine Optional Long / Short enable toggles. Choose whether opposite signals simply close the trade or reverse it (flip direction in one transaction). Risk modules – all opt‑in via check‑boxes Classic % block – fixed % Take‑Profit / Stop‑Loss / Break‑Even. Fibonacci Bollinger Bands (FBB) module Draws dynamic VWMA/HMA/SMA/EMA/DEMA/TEMA mid‑line with ATR‑scaled Fibonacci envelopes. Every line can be used for stops, trailing, or multi‑target exits. Separate LONG and SHORT sub‑modules Each has its own SL plus three Take‑Profits (TP1‑TP3). Per TP you set line, position‑percentage to close, and an optional trailing flag. Executed TP/SLs deactivate themselves so they cannot refire. Trailing behaviour If Trail is checked, the selected line is re‑evaluated once per bar; the order is amended via strategy.exit(). 3. Inputs Overview Group Parameter Notes Trade Settings Enable Long / Enable Short Master switches Close on Opposite / Reverse Position How to react to a counter‑signal Risk % Use TP / SL / BE + their % Traditional fixed‑distance management Fibo Bands FIBO LEVELS ENABLE + visual style/length Turn indicator overlay on/off FBB LONG SL / TP1‑TP3 Enable, Line, %, Trail Rules applied only while a long is open FBB SHORT SL / TP1‑TP3 Enable, Line, %, Trail Rules applied only while a short is open Line choices: Basis, 0.236, 0.382, 0.5, 0.618, 0.764, 1.0 – long rules use lower bands, short rules use upper bands automatically. 4. Algorithm Details Position open On the very first bar after entry, the script checks the direction and activates the corresponding LONG or SHORT module, deactivating the other. Order management loop (every bar) FBB Stop‑Loss: placed/updated at chosen band; if trailing, follows the new value. TP1‑TP3: each active target updates its limit price to the selected band (or holds static if trailing is off). The classic % block runs in parallel; its exits have priority because they call strategy.close_all(). Exit handling When any strategy.exit() fires, the script reads exit_id and flips the *_Active flag so that order will not be recreated. A Stop‑Loss (SL) also disables all remaining TPs for that leg. 5. Typical Use Cases Scenario Suggested Setup Scalping longs into VWAP‐reversion Enable LONG TP1 @ 0.382 (30 %), TP2 @ 0.618 (40 %), SL @ 0.236 + trailing Fade shorts during news spikes Enable SHORT SL @ 1.0 (no trail) and SHORT TP1,2,3 on consecutive lowers with small size‑outs Classic trend‑follow Use only classic % TP/SL block and disable FBB modules 6. Hints & Tips Signal quality matters – this script manages exits, it does not generate entries. Keep TV time zone in mind when picking start/end dates. For portfolio‑style testing allocate smaller default_qty_value than 100 % or use strategy.percent_of_equity sizing. You can combine FBB exits with fixed‑% ones for layered management. 7. Limitations / Safety No pyramiding; the script holds max one position at a time. All calculations are bar‑close; intra‑bar touches may differ from real‑time execution. The indicator overlay is optional, so you can run visual‑clean tests by unchecking FIBO LEVELS ENABLE.
Pine Script®策略
由RezzoRedPriest提供
Sharpe Ratio Forced Selling StrategyThis study introduces the “Sharpe Ratio Forced Selling Strategy”, a quantitative trading model that dynamically manages positions based on the rolling Sharpe Ratio of an asset’s excess returns relative to the risk-free rate. The Sharpe Ratio, first introduced by Sharpe (1966), remains a cornerstone in risk-adjusted performance measurement, capturing the trade-off between return and volatility. In this strategy, entries are triggered when the Sharpe Ratio falls below a specified low threshold (indicating excessive pessimism), and exits occur either when the Sharpe Ratio surpasses a high threshold (indicating optimism or mean reversion) or when a maximum holding period is reached. The underlying economic intuition stems from institutional behavior. Institutional investors, such as pension funds and mutual funds, are often subject to risk management mandates and performance benchmarking, requiring them to reduce exposure to assets that exhibit deteriorating risk-adjusted returns over rolling periods (Greenwood and Scharfstein, 2013). When risk-adjusted performance improves, institutions may rebalance or liquidate positions to meet regulatory requirements or internal mandates, a behavior that can be proxied effectively through a rising Sharpe Ratio. By systematically monitoring the Sharpe Ratio, the strategy anticipates when “forced selling” pressure is likely to abate, allowing for opportunistic entries into assets priced below fundamental value. Exits are equally mechanized, either triggered by Sharpe Ratio improvements or by a strict time-based constraint, acknowledging that institutional rebalancing and window-dressing activities are often time-bound (Coval and Stafford, 2007). The Sharpe Ratio is particularly suitable for this framework due to its ability to standardize excess returns per unit of risk, ensuring comparability across timeframes and asset classes (Sharpe, 1994). Furthermore, adjusting returns by a dynamically updating short-term risk-free rate (e.g., US 3-Month T-Bills from FRED) ensures that macroeconomic conditions, such as shifting interest rates, are accurately incorporated into the risk assessment. While the Sharpe Ratio is an efficient and widely recognized measure, the strategy could be enhanced by incorporating alternative or complementary risk metrics: • Sortino Ratio: Unlike the Sharpe Ratio, the Sortino Ratio penalizes only downside volatility (Sortino and van der Meer, 1991). This would refine entries and exits to distinguish between “good” and “bad” volatility. • Maximum Drawdown Constraints: Integrating a moving window maximum drawdown filter could prevent entries during persistent downtrends not captured by volatility alone. • Conditional Value at Risk (CVaR): A measure of expected shortfall beyond the Value at Risk, CVaR could further constrain entry conditions by accounting for tail risk in extreme environments (Rockafellar and Uryasev, 2000). • Dynamic Thresholds: Instead of static Sharpe thresholds, one could implement dynamic bands based on the historical distribution of the Sharpe Ratio, adjusting for volatility clustering effects (Cont, 2001). Each of these risk parameters could be incorporated into the current script as additional input controls, further tailoring the model to different market regimes or investor risk appetites. References • Cont, R. (2001) ‘Empirical properties of asset returns: stylized facts and statistical issues’, Quantitative Finance, 1(2), pp. 223-236. • Coval, J.D. and Stafford, E. (2007) ‘Asset Fire Sales (and Purchases) in Equity Markets’, Journal of Financial Economics, 86(2), pp. 479-512. • Greenwood, R. and Scharfstein, D. (2013) ‘The Growth of Finance’, Journal of Economic Perspectives, 27(2), pp. 3-28. • Rockafellar, R.T. and Uryasev, S. (2000) ‘Optimization of Conditional Value-at-Risk’, Journal of Risk, 2(3), pp. 21-41. • Sharpe, W.F. (1966) ‘Mutual Fund Performance’, Journal of Business, 39(1), pp. 119-138. • Sharpe, W.F. (1994) ‘The Sharpe Ratio’, Journal of Portfolio Management, 21(1), pp. 49-58. • Sortino, F.A. and van der Meer, R. (1991) ‘Downside Risk’, Journal of Portfolio Management, 17(4), pp. 27-31.
Pine Script®策略
由EdgeTools提供
Gold ORB Strategy (15-min Range, 5-min Entry)The Gold ORB (Opening Range Breakout) Strategy is designed for day traders looking to capitalize on the price action in the early part of the trading day, specifically using a 15-minute range for identifying the opening range and a 5-minute timeframe for breakout entries. The strategy trades the Gold market (XAU/USD) during the New York session. Opening Range: The strategy defines the Opening Range (ORB) between 9:30 AM EST and 9:45 AM EST using the highest and lowest points during this 15-minute window. Breakout Entries: The strategy enters trades when the price breaks above the ORB high for a long position or below the ORB low for a short position. It waits for a 5-minute candle close outside the range before entering a trade. Stop Loss and Take Profit: The stop loss is placed at 50% of the ORB range, and the take profit is set at twice the ORB range (1:2 risk-reward ratio). Time Window: The strategy only executes trades before 12:00 PM EST, avoiding late-day market fluctuations and consolidations.
Pine Script®策略
由krypson提供
Prop Firm Business SimulatorThe prop firm business simulator is exactly what it sounds like. It's a plug and play tool to test out any tradingview strategy and simulate hypothetical performance on CFD Prop Firms. Now what is a modern day CFD Prop Firm? These companies sell simulated trading challenges for a challenge fee. If you complete the challenge you get access to simulated capital and you get a portion of the profits you make on those accounts payed out. I've included some popular firms in the code as presets so it's easy to simulate them. Take into account that this info will likely be out of date soon as these prices and challenge conditions change. Also, this tool will never be able to 100% simulate prop firm conditions and all their rules. All I aim to do with this tool is provide estimations. Now why is this tool helpful? Most traders on here want to turn their passion into their full-time career, prop firms have lately been the buzz in the trading community and market themselves as a faster way to reach that goal. While this all sounds great on paper, it is sometimes hard to estimate how much money you will have to burn on challenge fees and set realistic monthly payout expectations for yourself and your trading. This is where this tool comes in. I've specifically developed this for traders that want to treat prop firms as a business. And as a business you want to know your monthly costs and income depending on the trading strategy and prop firm challenge you are using. How to use this tool It's quite simple you remove the top part of the script and replace it with your own strategy. Make sure it's written in same version of pinescript before you do that. //--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$ //--$$$$$--Strategy-- --$$$$$$--// ****************************************************************************************************************************** //--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$ length = input.int(20, minval=1, group="Keltner Channel Breakout") mult = input(2.0, "Multiplier", group="Keltner Channel Breakout") src = input(close, title="Source", group="Keltner Channel Breakout") exp = input(true, "Use Exponential MA", display = display.data_window, group="Keltner Channel Breakout") BandsStyle = input.string("Average True Range", options = , title="Bands Style", display = display.data_window, group="Keltner Channel Breakout") atrlength = input(10, "ATR Length", display = display.data_window, group="Keltner Channel Breakout") esma(source, length)=> s = ta.sma(source, length) e = ta.ema(source, length) exp ? e : s ma = esma(src, length) rangema = BandsStyle == "True Range" ? ta.tr(true) : BandsStyle == "Average True Range" ? ta.atr(atrlength) : ta.rma(high - low, length) upper = ma + rangema * mult lower = ma - rangema * mult //--Graphical Display--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$ u = plot(upper, color=#2962FF, title="Upper", force_overlay=true) plot(ma, color=#2962FF, title="Basis", force_overlay=true) l = plot(lower, color=#2962FF, title="Lower", force_overlay=true) fill(u, l, color=color.rgb(33, 150, 243, 95), title="Background") //--Risk Management--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$ riskPerTradePerc = input.float(1, title="Risk per trade (%)", group="Keltner Channel Breakout") le = high>upper ? false : true se = lowlower strategy.entry('PivRevLE', strategy.long, comment = 'PivRevLE', stop = upper, qty=riskToLots) if se and upper>lower strategy.entry('PivRevSE', strategy.short, comment = 'PivRevSE', stop = lower, qty=riskToLots) The tool will then use the strategy equity of your own strategy and use this to simulat prop firms. Since these CFD prop firms work with different phases and payouts the indicator will simulate the gains until target or max drawdown / daily drawdown limit gets reached. If it reaches target it will go to the next phase and keep on doing that until it fails a challenge. If in one of the phases there is a reward for completing, like a payout, refund, extra it will add this to the gains. If you fail the challenge by reaching max drawdown or daily drawdown limit it will substract the challenge fee from the gains. These gains are then visualised in the calendar so you can get an idea of yearly / monthly gains of the backtest. Remember, it is just a backtest so no guarantees of future income. The bottom pane (non-overlay) is visualising the performance of the backtest during the phases. This way u can check if it is realistic. For instance if it only takes 1 bar on chart to reach target you are probably risking more than the firm wants you to risk. Also, it becomes much less clear if daily drawdown got hit in those high risk strategies, the results will be less accurate. The daily drawdown limit get's reset every time there is a new dayofweek on chart. If you set your prop firm preset setting to "'custom" the settings below that are applied as your prop firm settings. Otherwise it will use one of the template by default it's FTMO 100K. The strategy I'm using as an example in this script is a simple Keltner Channel breakout strategy. I'm using a 0.05% commission per trade as that is what I found most common on crypto exchanges and it's close to the commissions+spread you get on a cfd prop firm. I'm targeting a 1% risk per trade in the backtest to try and stay within prop firm boundaries of max 1% risk per trade. Lastly, the original yearly and monthly performance table was developed by Quantnomad and I've build ontop of that code. Here's a link to the original publication: That's everything for now, hope this indicator helps people visualise the potential of prop firms better or to understand that they are not a good fit for their current financial situation.
Pine Script®策略
由Honestcowboy提供
DCA StrategyThis strategy makes it easy for you to backtest and automate the DCA strategy based on 2 triggers: Day of the week Every X candles This way you can set up your DCA strategy the way you like and automate on any exchange or even a DEX, which offers an API. The strategy is auto selling on the last candle, otherwise you won't see any performance numbers because all positions will still be open (non conclusive). Settings Start Date & End Date Use those dates to help you with your backtest period. It also helps when automating, to start at a specific time to mimic what you have already done on your own portfolio and thus be in sync in TV as well. Capital to invest per trade Set how capital to use per DCA buy signal. Hover over the tooltip to understand, which currency is used. Close All on last candle When backtesting, you must close open positions, otherwise the Strategy Tester won't show you any numbers. This is why the strategy automatically closes all positions on the last candle for your convenience (ON per default). BUT, when automating, you cannot have this checked because it would sell all of your asset on every candle open. So turn this OFF when automating. Use Day of Week Mode This checkbox switches between the "Day of Week" mode or the "Every X Candles" mode. Day of Week Opens a long position at the start of the weekday you have set it to. Hover over the tooltip to understand, which number to use for the day of the week you need. Every X Candles Opens a long position after every x candles. Always at the start of every such candle. On the daily chart, this number represents "1 day", on the 1h chart, it's "1 hour" and so on. Properties Initial Capital DCA has a special quirk and that is that it invests more and more and more funds the longer it runs. But TradingView takes the Initial Capital number to calculate Net Profit, thus the Initial Capital number has to grow with every additional dollar (money) that is being invested over time, otherwise the Net Profit number will be wrong. Sadly PineScript does not allow to set the Initial Capital number dynamically. So you have to set it manually. To that end, this strategy shows a Label on the last candle, which shows the Invested Capital. You must take that number and put it into the Initial Capital input and click Ok . If you don't do this, your Net Profit Number will be totally wrong! The label must show green . If it shows red it means you need to change the Initial Capital number before looking at the performance numbers. After every timeframe or settings change, you must adapt the Initial Capital, otherwise you will get wrong numbers.
Pine Script®策略
由michaelionita提供
Z-Score Normalized VIX StrategyThis strategy leverages the concept of the Z-score applied to multiple VIX-based volatility indices, specifically designed to capture market reversals based on the normalization of volatility. The strategy takes advantage of VIX-related indicators to measure extreme levels of market fear or greed and adjusts its position accordingly. 1. Overview of the Z-Score Methodology The Z-score is a statistical measure that describes the position of a value relative to the mean of a distribution in terms of standard deviations. In this strategy, the Z-score is calculated for various volatility indices to assess how far their values are from their historical averages, thus normalizing volatility levels. The Z-score is calculated as follows: Z = \frac{X - \mu}{\sigma} Where: • X is the current value of the volatility index. • \mu is the mean of the index over a specified period. • \sigma is the standard deviation of the index over the same period. This measure tells us how many standard deviations the current value of the index is away from its average, indicating whether the market is experiencing unusually high or low volatility (fear or calm). 2. VIX Indices Used in the Strategy The strategy utilizes four commonly referenced volatility indices: • VIX (CBOE Volatility Index): Measures the market’s expectations of 30-day volatility based on S&P 500 options. • VIX3M (3-Month VIX): Reflects expectations of volatility over the next three months. • VIX9D (9-Day VIX): Reflects shorter-term volatility expectations. • VVIX (VIX of VIX): Measures the volatility of the VIX itself, indicating the level of uncertainty in the volatility index. These indices provide a comprehensive view of the current volatility landscape across different time horizons. 3. Strategy Logic The strategy follows a long entry condition and an exit condition based on the combined Z-score of the selected volatility indices: • Long Entry Condition: The strategy enters a long position when the combined Z-score of the selected VIX indices falls below a user-defined threshold, indicating an abnormally low level of volatility (suggesting a potential market bottom and a bullish reversal). The threshold is set as a negative value (e.g., -1), where a more negative Z-score implies greater deviation below the mean. • Exit Condition: The strategy exits the long position when the combined Z-score exceeds the threshold (i.e., when the market volatility increases above the threshold, indicating a shift in market sentiment and reduced likelihood of continued upward momentum). 4. User Inputs • Z-Score Lookback Period: The user can adjust the lookback period for calculating the Z-score (e.g., 6 periods). • Z-Score Threshold: A customizable threshold value to define when the market has reached an extreme volatility level, triggering entries and exits. The strategy also allows users to select which VIX indices to use, with checkboxes to enable or disable each index in the calculation of the combined Z-score. 5. Trade Execution Parameters • Initial Capital: The strategy assumes an initial capital of $20,000. • Pyramiding: The strategy does not allow pyramiding (multiple positions in the same direction). • Commission and Slippage: The commission is set at $0.05 per contract, and slippage is set at 1 tick. 6. Statistical Basis of the Z-Score Approach The Z-score methodology is a standard technique in statistics and finance, commonly used in risk management and for identifying outliers or unusual events. According to Dumas, Fleming, and Whaley (1998), volatility indices like the VIX serve as a useful proxy for market sentiment, particularly during periods of high uncertainty. By calculating the Z-score, we normalize volatility and quantify the degree to which the current volatility deviates from historical norms, allowing for systematic entry and exit based on these deviations. 7. Implications of the Strategy This strategy aims to exploit market conditions where volatility has deviated significantly from its historical mean. When the Z-score falls below the threshold, it suggests that the market has become excessively calm, potentially indicating an overreaction to past market events. Entering long positions under such conditions could capture market reversals as fear subsides and volatility normalizes. Conversely, when the Z-score rises above the threshold, it signals increased volatility, which could be indicative of a bearish shift in the market, prompting an exit from the position. By applying this Z-score normalized approach, the strategy seeks to achieve more consistent entry and exit points by reducing reliance on subjective interpretation of market conditions. 8. Scientific Sources • Dumas, B., Fleming, J., & Whaley, R. (1998). “Implied Volatility Functions: Empirical Tests”. The Journal of Finance, 53(6), 2059-2106. This paper discusses the use of volatility indices and their empirical behavior, providing context for volatility-based strategies. • Black, F., & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities”. Journal of Political Economy, 81(3), 637-654. The original Black-Scholes model, which forms the basis for many volatility-related strategies.
Pine Script®策略
由EdgeTools提供
Cycle Biologique Strategy // (\_/) // ( •.•) // (")_(") //@fr33domz Experimental Research: Cycle Biologique Strategy Overview The "Cycle Biologique Strategy" is an experimental trading algorithm designed to leverage periodic cycles in price movements by utilizing a sinusoidal function. This strategy aims to identify potential buy and sell signals based on the behavior of a custom-defined biological cycle. Key Parameters Cycle Length: This parameter defines the duration of the cycle, set by default to 30 periods. The user can adjust this value to optimize the strategy for different asset classes or market conditions. Amplitude: The amplitude of the cycle influences the scale of the sinusoidal wave, allowing for customization in the sensitivity of buy and sell signals. Offset: The offset parameter introduces phase shifts to the cycle, adjustable within a range of -360 to 360 degrees. This flexibility allows the strategy to align with various market rhythms. Methodology The core of the strategy lies in the calculation of a periodic cycle using a sinusoidal function. Trading Signals Buy Signal: A buy signal is generated when the cycle value crosses above zero, indicating a potential upward momentum. Sell Signal: Conversely, a sell signal is triggered when the cycle value crosses below zero, suggesting a potential downtrend. Execution The strategy executes trades based on these signals: Upon receiving a buy signal, the algorithm enters a long position. When a sell signal occurs, the strategy closes the long position. Visualization To enhance user experience, the periodic cycle is plotted visually on the chart in blue, allowing traders to observe the cyclical nature of the strategy and its alignment with market movements.
Pine Script®策略
由FollowtheWhiteRabbitz提供
Strategy Stats [presentTrading]Hello! it's another weekend. This tool is a strategy performance analysis tool. Looking at the TradingView community, it seems few creators focus on this aspect. I've intentionally created a shared version. Welcome to share your idea or question on this. █ Introduction and How it is Different Strategy Stats is a comprehensive performance analytics framework designed specifically for trading strategies. Unlike standard strategy backtesting tools that simply show cumulative profits, this analytics suite provides real-time, multi-timeframe statistical analysis of your trading performance. Multi-timeframe analysis: Automatically tracks performance metrics across the most recent time periods (last 7 days, 30 days, 90 days, 1 year, and 4 years) Advanced statistical measures: Goes beyond basic metrics to include Information Coefficient (IC) and Sortino Ratio Real-time feedback: Updates performance statistics with each new trade Visual analytics: Color-coded performance table provides instant visual feedback on strategy health Integrated risk management: Implements sophisticated take profit mechanisms with 3-step ATR and percentage-based exits BTCUSD Performance The table in the upper right corner is a comprehensive performance dashboard showing trading strategy statistics. Note: While this presentation uses Vegas SuperTrend as the underlying strategy, this is merely an example. The Stats framework can be applied to any trading strategy. The Vegas SuperTrend implementation is included solely to demonstrate how the analytics module integrates with a trading strategy. ⚠️ Timeframe Limitations Important: TradingView's backtesting engine has a maximum storage limit of 10,000 bars. When using this strategy stats framework on smaller timeframes such as 1-hour or 2-hour charts, you may encounter errors if your backtesting period is too long. Recommended Timeframe Usage: Ideal for: 4H, 6H, 8H, Daily charts and above May cause errors on: 1H, 2H charts spanning multiple years Not recommended for: Timeframes below 1H with long history █ Strategy, How it Works: Detailed Explanation The Strategy Stats framework consists of three primary components: statistical data collection, performance analysis, and visualization. 🔶 Statistical Data Collection The system maintains several critical data arrays: equityHistory: Tracks equity curve over time tradeHistory: Records profit/loss of each trade predictionSignals: Stores trade direction signals (1 for long, -1 for short) actualReturns: Records corresponding actual returns from each trade For each closed trade, the system captures: float tradePnL = strategy.closedtrades.profit(tradeIndex) float tradeReturn = strategy.closedtrades.profit_percent(tradeIndex) int tradeType = entryPrice < exitPrice ? 1 : -1 // Direction 🔶 Performance Metrics Calculation The framework calculates several key performance metrics: Information Coefficient (IC): The correlation between prediction signals and actual returns, measuring forecast skill. IC = Correlation(predictionSignals, actualReturns) Where Correlation is the Pearson correlation coefficient: Correlation(X,Y) = (nΣXY - ΣXY) / √ Sortino Ratio: Measures risk-adjusted return focusing only on downside risk: Sortino = (Avg_Return - Risk_Free_Rate) / Downside_Deviation Where Downside Deviation is: Downside_Deviation = √ R_i represents individual returns, T is the target return (typically the risk-free rate), and n is the number of observations. Maximum Drawdown: Tracks the largest percentage drop from peak to trough: DD = (Peak_Equity - Trough_Equity) / Peak_Equity * 100 🔶 Time Period Calculation The system automatically determines the appropriate number of bars to analyze for each timeframe based on the current chart timeframe: bars_7d = math.max(1, math.round(7 * barsPerDay)) bars_30d = math.max(1, math.round(30 * barsPerDay)) bars_90d = math.max(1, math.round(90 * barsPerDay)) bars_365d = math.max(1, math.round(365 * barsPerDay)) bars_4y = math.max(1, math.round(365 * 4 * barsPerDay)) Where barsPerDay is calculated based on the chart timeframe: barsPerDay = timeframe.isintraday ? 24 * 60 / math.max(1, (timeframe.in_seconds() / 60)) : timeframe.isdaily ? 1 : timeframe.isweekly ? 1/7 : timeframe.ismonthly ? 1/30 : 0.01 🔶 Visual Representation The system presents performance data in a color-coded table with intuitive visual indicators: Green: Excellent performance Lime: Good performance Gray: Neutral performance Orange: Mediocre performance Red: Poor performance █ Trade Direction The Strategy Stats framework supports three trading directions: Long Only: Only takes long positions when entry conditions are met Short Only: Only takes short positions when entry conditions are met Both: Takes both long and short positions depending on market conditions █ Usage To effectively use the Strategy Stats framework: Apply to existing strategies: Add the performance tracking code to any strategy to gain advanced analytics Monitor multiple timeframes: Use the multi-timeframe analysis to identify performance trends Evaluate strategy health: Review IC and Sortino ratios to assess predictive power and risk-adjusted returns Optimize parameters: Use performance data to refine strategy parameters Compare strategies: Apply the framework to multiple strategies to identify the most effective approach For best results, allow the strategy to generate sufficient trade history for meaningful statistical analysis (at least 20-30 trades). █ Default Settings The default settings have been carefully calibrated for cryptocurrency markets: Performance Tracking: Time periods: 7D, 30D, 90D, 1Y, 4Y Statistical measures: Return, Win%, MaxDD, IC, Sortino Ratio IC color thresholds: >0.3 (green), >0.1 (lime), <-0.1 (orange), <-0.3 (red) Sortino color thresholds: >1.0 (green), >0.5 (lime), <0 (red) Multi-Step Take Profit: ATR multipliers: 2.618, 5.0, 10.0 Percentage levels: 3%, 8%, 17% Short multiplier: 1.5x (makes short take profits more aggressive) Stop loss: 20%
Pine Script®策略
由PresentTrading提供
2
External Signals Strategy TesterExternal Signals Strategy Tester This strategy is designed to help you backtest external buy/sell signals coming from another indicator on your chart. It is a flexible and powerful tool that allows you to simulate real trading based on signals generated by any indicator, using input.source connections. 🔧 How It Works Instead of generating signals internally, this strategy listens to two external input sources: One for buy signals One for sell signals These sources can be connected to the plots from another indicator (for example, custom indicators, signal lines, or logic-based plots). To use this: Add your indicator to the chart (it must be visible on the same pane as this strategy). Open the settings of the strategy. In the fields Buy Signal and Sell Signal, select the appropriate plot (line, value, etc.) from the indicator that represents the buy/sell logic. The strategy will open positions when the selected buy signal crosses above 0, and sell signal crosses above 0. This logic can be easily adapted by modifying the crossover rule inside the script if your signal style is different. ⚙️ Features Included ✅ Configurable trade direction: You can choose whether to allow long trades, short trades, or both. ✅ Optional close on opposite signal: When enabled, the strategy will exit the current position if an opposite signal appears. ✅ Optional full position reversal: When enabled, the strategy will close the current position and immediately open an opposite one on the reverse signal. ✅ Risk Management Tools: You can define: Take Profit (TP): Position will be closed once the specified profit (in %) is reached. Stop Loss (SL): Position will be closed if the price drops to the specified loss level (in %). BreakEven (BE): Once the specified profit threshold is reached, the strategy will move the stop-loss to the entry price. 📌 If any of these values (TP, SL, BE) are set to 0, the feature is disabled and will not be applied. 🧪 Best Use Cases Backtesting signals from custom indicators, without rewriting the logic into a strategy. Comparing the performance of different signal sources. Testing external indicators with optional position management logic. Validating strategies using external filters, oscillators, or trend signals. 📌 Final Notes You can visualize where the strategy detected buy/sell signals using green/red markers on the chart. All parameters are customizable through the strategy settings panel. This strategy does not repaint, and it processes signals in real-time only (no lookahead bias).
Pine Script®策略
由RezzoRedPriest提供
2
EMA 10/55/200 - LONG ONLY MTF (4h with 1D & 1W confirmation)Title: EMA 10/55/200 - Long Only Multi-Timeframe Strategy (4h with 1D & 1W confirmation) Description: This strategy is designed for trend-following long entries using a combination of exponential moving averages (EMAs) on the 4-hour chart, confirmed by higher timeframe trends from the daily (1D) and weekly (1W) charts. 🔍 How It Works 🔹 Entry Conditions (4h chart): EMA 10 crosses above EMA 55 and price is above EMA 55 OR EMA 55 crosses above EMA 200 OR EMA 10 crosses above EMA 500 These entries indicate short-term momentum aligning with medium/long-term trend strength. 🔹 Confirmation (multi-timeframe alignment): Daily (1D): EMA 55 is above EMA 200 Weekly (1W): EMA 55 is above EMA 200 This ensures that we only enter long trades when the higher timeframes support an uptrend, reducing false signals during sideways or bearish markets. 🛑 Exit Conditions Bearish crossover of EMA 10 below EMA 200 or EMA 500 Stop Loss: 5% below entry price ⚙️ Backtest Settings Capital allocation per trade: 10% of equity Commission: 0.1% Slippage: 2 ticks These are realistic conditions for crypto, forex, and stocks. 📈 Best Used On Timeframe: 4h Instruments: Trending markets like BTC/ETH, FX majors, or growth stocks Works best in volatile or trending environments ⚠️ Disclaimer This is a backtest tool and educational resource. Always validate on demo accounts before applying to real capital. Do your own due diligence.
Pine Script®策略
由ivan31416neda提供
Buy on 5% dip strategy with time adjustment This script is a strategy called "Buy on 5% Dip Strategy with Time Adjustment 📉💡," which detects a 5% drop in price and triggers a buy signal 🔔. It also automatically closes the position once the set profit target is reached 💰, and it has additional logic to close the position if the loss exceeds 14% after holding for 230 days ⏳. Strategy Explanation Buy Condition: A buy signal is triggered when the price drops 5% from the highest price reached 🔻. Take Profit: The position is closed when the price hits a 1.22x target from the average entry price 📈. Forced Sell Condition: If the position is held for more than 230 days and the loss exceeds 14%, the position is automatically closed 🚫. Leverage & Capital Allocation: Leverage is adjustable ⚖️, and you can set the percentage of capital allocated to each trade 💸. Time Limits: The strategy allows you to set a start and end time ⏰ for trading, making the strategy active only within that specific period. Code Credits and References Credits: This script utilizes ideas and code from @QuantNomad and jangdokang for the profit table and algorithm concepts 🔧. Sources: Monthly Performance Table Script by QuantNomad: ZenAndTheArtOfTrading's Script: Strategy Performance This strategy provides risk management through take profit and forced sell conditions and includes a performance table 📊 to track monthly and yearly results. You can compare backtest results with real-time performance to evaluate the strategy's effectiveness. The performance numbers shown in the backtest reflect what would have happened if you had used this strategy since the launch date of the SOXL (the Direxion Daily Semiconductor Bull 3x Shares ETF) 📅. These results are not hypothetical but based on actual performance from the day of the ETF’s launch 📈. Caution ⚠️ No Guarantee of Future Results: The results are based on historical performance from the launch of the SOXL ETF, but past performance does not guarantee future results. It’s important to approach with caution when applying it to live trading 🔍. Risk Management: Leverage and capital allocation settings are crucial for managing risk ⚠️. Make sure to adjust these according to your risk tolerance ⚖️.
Pine Script®策略
由FutureUBCStudent提供
1
IU Bigger than range strategyDESCRIPTION IU Bigger Than Range Strategy is designed to capture breakout opportunities by identifying candles that are significantly larger than the previous range. It dynamically calculates the high and low of the last N candles and enters trades when the current candle's range exceeds the previous range. The strategy includes multiple stop-loss methods (Previous High/Low, ATR, Swing High/Low) and automatically manages take-profit and stop-loss levels based on user-defined risk-to-reward ratios. This versatile strategy is optimized for higher timeframes and assets like BTC but can be fine-tuned for different instruments and intervals. USER INPUTS: Look back Length: Number of candles to calculate the high-low range. Default is 22. Risk to Reward: Sets the target reward relative to the stop-loss distance. Default is 3. Stop Loss Method: Choose between:(Default is "Previous High/Low") - Previous High/Low - ATR (Average True Range) - Swing High/Low ATR Length: Defines the length for ATR calculation (only applicable when ATR is selected as the stop-loss method) (Default is 14). ATR Factor: Multiplier applied to the ATR to determine stop-loss distance(Default is 2). Swing High/Low Length: Specifies the length for identifying swing points (only applicable when Swing High/Low is selected as the stop-loss method).(Default is 2) LONG CONDITION: The current candle’s range (absolute difference between open and close) is greater than the previous range. The closing price is higher than the opening price (bullish candle). SHORT CONDITIONS: The current candle’s range exceeds the previous range. The closing price is lower than the opening price (bearish candle). LONG EXIT: Stop-loss: - Previous Low - ATR-based trailing stop - Recent Swing Low Take-profit: - Defined by the Risk-to-Reward ratio (default 3x the stop-loss distance). SHORT EXIT: Stop-loss: - Previous High - ATR-based trailing stop - Recent Swing High Take-profit: - Defined by the Risk-to-Reward ratio (default 3x the stop-loss distance). ALERTS: Long Entry Triggered Short Entry Triggered WHY IT IS UNIQUE: This strategy dynamically adapts to different market conditions by identifying candles that exceed the previous range, ensuring that it only enters trades during strong breakout scenarios. Multiple stop-loss methods provide flexibility for different trading styles and risk profiles. The visual representation of stop-loss and take-profit levels with color-coded plots improves trade monitoring and decision-making. HOW USERS CAN BENEFIT FROM IT: Ideal for breakout traders looking to capitalize on momentum-driven price moves. Provides flexibility to customize stop-loss methods and fine-tune risk management parameters. Helps minimize drawdowns with a strong risk-to-reward framework while maximizing profit potential.
Pine Script®策略
由Shivam_Mandrai提供
Trendline Breaks with Multi Fibonacci Supertrend StrategyTMFS Strategy: Advanced Trendline Breakouts with Multi-Fibonacci Supertrend Elevate your algorithmic trading with institutional-grade signal confluence Strategy Genesis & Evolution This advanced trading system represents the culmination of a personal research journey, evolving from my custom " Multi Fibonacci Supertrend with Signals " indicator into a comprehensive trading strategy. Built upon the exceptional trendline detection methodology pioneered by LuxAlgo in their " Trendlines with Breaks " indicator, I've engineered a systematic framework that integrates multiple technical factors into a cohesive trading system. Core Fibonacci Principles At the heart of this strategy lies the Fibonacci sequence application to volatility measurement: // Fibonacci-based factors for multiple Supertrend calculations factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval = 0.01, step = 0.01) factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval = 0.01, step = 0.01) factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval = 0.01, step = 0.01) These precise Fibonacci ratios create a dynamic volatility envelope that adapts to changing market conditions while maintaining mathematical harmony with natural price movements. Dynamic Trendline Detection The strategy incorporates LuxAlgo's pioneering approach to trendline detection: // Pivotal swing detection (inspired by LuxAlgo) pivot_high = ta.pivothigh(swing_length, swing_length) pivot_low = ta.pivotlow(swing_length, swing_length) // Dynamic slope calculation using ATR slope = atr_value / swing_length * atr_multiplier // Update trendlines based on pivot detection if bool(pivot_high) upper_slope := slope upper_trendline := pivot_high else upper_trendline := nz(upper_trendline) - nz(upper_slope) This adaptive trendline approach automatically identifies key structural market boundaries, adjusting in real-time to evolving chart patterns. Breakout State Management The strategy implements sophisticated state tracking for breakout detection: // Track breakouts with state variables var int upper_breakout_state = 0 var int lower_breakout_state = 0 // Update breakout state when price crosses trendlines upper_breakout_state := bool(pivot_high) ? 0 : close > upper_trendline ? 1 : upper_breakout_state lower_breakout_state := bool(pivot_low) ? 0 : close < lower_trendline ? 1 : lower_breakout_state // Detect new breakouts (state transitions) bool new_upper_breakout = upper_breakout_state > upper_breakout_state bool new_lower_breakout = lower_breakout_state > lower_breakout_state This state-based approach enables precise identification of the exact moment when price breaks through a significant trendline. Multi-Factor Signal Confluence Entry signals require confirmation from multiple technical factors: // Define entry conditions with multi-factor confluence long_entry_condition = enable_long_positions and upper_breakout_state > upper_breakout_state and // New trendline breakout di_plus > di_minus and // Bullish DMI confirmation close > smoothed_trend // Price above Supertrend envelope // Execute trades only with full confirmation if long_entry_condition strategy.entry('L', strategy.long, comment = "LONG") This strict requirement for confluence significantly reduces false signals and improves the quality of trade entries. Advanced Risk Management The strategy includes sophisticated risk controls with multiple methodologies: // Calculate stop loss based on selected method get_long_stop_loss_price(base_price) => switch stop_loss_method 'PERC' => base_price * (1 - long_stop_loss_percent) 'ATR' => base_price - long_stop_loss_atr_multiplier * entry_atr 'RR' => base_price - (get_long_take_profit_price() - base_price) / long_risk_reward_ratio => na // Implement trailing functionality strategy.exit( id = 'Long Take Profit / Stop Loss', from_entry = 'L', qty_percent = take_profit_quantity_percent, limit = trailing_take_profit_enabled ? na : long_take_profit_price, stop = long_stop_loss_price, trail_price = trailing_take_profit_enabled ? long_take_profit_price : na, trail_offset = trailing_take_profit_enabled ? long_trailing_tp_step_ticks : na, comment = "TP/SL Triggered" ) This flexible approach adapts to varying market conditions while providing comprehensive downside protection. Performance Characteristics Rigorous backtesting demonstrates exceptional capital appreciation potential with impressive risk-adjusted metrics: Remarkable total return profile (1,517%+) Strong Sortino ratio (3.691) indicating superior downside risk control Profit factor of 1.924 across all trades (2.153 for long positions) Win rate exceeding 35% with balanced distribution across varied market conditions Institutional Considerations The strategy architecture addresses execution complexities faced by institutional participants with temporal filtering and date-range capabilities: // Time Filter settings with flexible timezone support import jason5480/time_filters/5 as time_filter src_timezone = input.string(defval = 'Exchange', title = 'Source Timezone') dst_timezone = input.string(defval = 'Exchange', title = 'Destination Timezone') // Date range filtering for precise execution windows use_from_date = input.bool(defval = true, title = 'Enable Start Date') from_date = input.time(defval = timestamp('01 Jan 2022 00:00'), title = 'Start Date') // Validate trading permission based on temporal constraints date_filter_approved = time_filter.is_in_date_range( use_from_date, from_date, use_to_date, to_date, src_timezone, dst_timezone ) These capabilities enable precise execution timing and market session optimization critical for larger market participants. Acknowledgments Special thanks to LuxAlgo for the pioneering work on trendline detection and breakout identification that inspired elements of this strategy. Their innovative approach to technical analysis provided a valuable foundation upon which I could build my Fibonacci-based methodology. This strategy is shared under the same Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license as LuxAlgo's original work. Past performance is not indicative of future results. Conduct thorough analysis before implementing any algorithmic strategy.
Pine Script®策略
由FibonacciFlux提供
24
BTCUSD with adjustable sl,tpThis strategy is designed for swing traders who want to enter long positions on pullbacks after a short-term trend shift, while also allowing immediate short entries when conditions favor downside movement. It combines SMA crossovers, a fixed-percentage retracement entry, and adjustable risk management parameters for optimal trade execution. Key Features: ✅ Trend Confirmation with SMA Crossover The 10-period SMA crossing above the 25-period SMA signals a bullish trend shift. The 10-period SMA crossing below the 25-period SMA signals a bearish trend shift. Short trades are only taken if the price is below the 150 EMA, ensuring alignment with the broader trend. 📉 Long Pullback Entry Using Fixed Percentage Retracement Instead of entering immediately on the SMA crossover, the strategy waits for a retracement before going long. The pullback entry is defined as a percentage retracement from the recent high, allowing for an optimized entry price. The retracement percentage is fully adjustable in the settings (default: 1%). A dynamic support level is plotted on the chart to visualize the pullback entry zone. 📊 Short Entry Rules If the SMA(10) crosses below the SMA(25) and price is below the 150 EMA, a short trade is immediately entered. Risk Management & Exit Strategy: 🚀 Take Profit (TP) – Fully customizable profit target in points. (Default: 1000 points) 🛑 Stop Loss (SL) – Adjustable stop loss level in points. (Default: 250 points) 🔄 Break-Even (BE) – When price moves in favor by a set number of points, the stop loss is moved to break-even. 📌 Extra Exit Condition for Longs: If the SMA(10) crosses below SMA(25) while the price is still below the EMA150, the strategy force-exits the long position to avoid reversals. How to Use This Strategy: Enable the strategy on your TradingView chart (recommended for stocks, forex, or indices). Customize the settings – Adjust TP, SL, BE, and pullback percentage for your risk tolerance. Observe the plotted retracement levels – When the price touches and bounces off the level, a long trade is triggered. Let the strategy manage the trade – Break-even protection and take-profit logic will automatically execute. Ideal Market Conditions: ✅ Trending Markets – The strategy works best when price follows strong trends. ✅ Stocks, Indices, or Forex – Can be applied across multiple asset classes. ✅ Medium-Term Holding Period – Suitable for swing trades lasting days to weeks.
Pine Script®策略
由muana2提供