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更新済 Linear Mean Reversion Strategy
📘 Strategy Introduction: Linear Mean Reversion with Fixed Stop
This strategy implements a simple yet powerful mean reversion model that assumes price tends to oscillate around a dynamic average over time. It identifies statistically significant deviations from the moving average using a z-score, and enters trades expecting a return to the mean.
🧠 Core Logic:
A z-score is calculated by comparing the current price to its moving average, normalized by standard deviation, over a user-defined half-life window.
Trades are entered when the z-score crosses a threshold (e.g., ±1), signaling overbought or oversold conditions.
The strategy exits positions either when price reverts back near the mean (z-score close to 0), or if a fixed stop loss of 100 points is hit, whichever comes first.
⚙️ Key Features:
Dynamic mean and volatility estimation using moving average and standard deviation
Configurable z-score thresholds for entry and exit
Position size scaling based on z-score magnitude
Fixed stop loss to control risk and avoid prolonged drawdowns
🧪 Use Case:
Ideal for range-bound markets or assets that exhibit stationary behavior around a mean, this strategy is especially useful on assets with mean-reverting characteristics like currency pairs, ETFs, or large-cap stocks. It is best suited for traders looking for short-term reversions rather than long-term trends.
This strategy implements a simple yet powerful mean reversion model that assumes price tends to oscillate around a dynamic average over time. It identifies statistically significant deviations from the moving average using a z-score, and enters trades expecting a return to the mean.
🧠 Core Logic:
A z-score is calculated by comparing the current price to its moving average, normalized by standard deviation, over a user-defined half-life window.
Trades are entered when the z-score crosses a threshold (e.g., ±1), signaling overbought or oversold conditions.
The strategy exits positions either when price reverts back near the mean (z-score close to 0), or if a fixed stop loss of 100 points is hit, whichever comes first.
⚙️ Key Features:
Dynamic mean and volatility estimation using moving average and standard deviation
Configurable z-score thresholds for entry and exit
Position size scaling based on z-score magnitude
Fixed stop loss to control risk and avoid prolonged drawdowns
🧪 Use Case:
Ideal for range-bound markets or assets that exhibit stationary behavior around a mean, this strategy is especially useful on assets with mean-reverting characteristics like currency pairs, ETFs, or large-cap stocks. It is best suited for traders looking for short-term reversions rather than long-term trends.
リリースノート
The idea is simple: if an instrument’s price is truly mean-reverting, we can expect it to return to its long-term average. Most assets, however, behave more like random walks and show little tendency to revert. The VIX—an index of S&P 500 volatility—is a notable exception, exhibiting clear mean-reversion.
For this strategy, I’ve assumed a trading cost of 0.1%, which seems reasonable given the typical bid-ask spread on the VIX is about 0.18 points.
A z-score is calculated by comparing the current price to its moving average, normalized by standard deviation, over a user-defined half-life window.
Trades are entered when the z-score crosses a threshold (e.g., ±1), signaling overbought or oversold conditions.
The strategy exits positions either when price reverts back near the mean (z-score close to 0), or if a fixed stop loss is hit, whichever comes first.
Designed for a 1 H window, where the VIX’s mean reversion and assumed costs tend to align well.
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オープンソーススクリプト
TradingViewの精神に則り、この作者はスクリプトのソースコードを公開しているので、その内容を理解し検証することができます。作者に感謝です！無料でお使いいただけますが、このコードを投稿に再利用する際にはハウスルールに従うものとします。
免責事項
これらの情報および投稿は、TradingViewが提供または保証する金融、投資、取引、またはその他の種類のアドバイスや推奨を意図したものではなく、またそのようなものでもありません。詳しくは利用規約をご覧ください。