策略類型
Mean Reversion Trading Strategies
Contrarian Trading on Price Normalization
Mean reversion strategies exploit the tendency of prices to return to their historical average after extreme deviations. These contrarian strategies buy oversold conditions and sell overbought conditions, profiting from price normalization.
2 個演算法2 個函式庫
演算法網路
均值回歸演算法如何跨函式庫連接
Mean Reversion
OctoBot1 algos
DCAbeginner
交易決策流程
均值回歸演算法如何在交易系統中協同運作
1
Deviation Detection
Overbought/oversold identification
RSI < 30 or > 70
Bollinger Band touch
2
Reversal Confirmation
Momentum shift signals
Divergence detection
Volume spike on reversal
3
Contrarian Entry
Fade the extreme
Buy at oversold extreme
Sell at overbought extreme
4
Mean Target
Return to average
Take profit at moving average
Partial position scaling
5
Trend Filter
Avoid strong trends
ADX < 25 (no strong trend)
Time-based stop if no reversion
演算法比較
在關鍵維度上對比均值回歸演算法
演算法對比矩陣點擊欄以展開詳情
| 指標 | DCAOctoBot | Michael Burry AgentAI Hedge Fund |
|---|---|---|
| 複雜度 | ⭐⭐beginner | ⭐⭐⭐intermediate |
| 預測類型 | 混合 | 混合 |
| 訓練速度 | ⚡⚡ | ⚡⚡ |
| 準確度 | 📊📊 | 📊📊 |
| 最適合 | 通用 | 通用 |
Mean Reversion Trading Strategies，演算法參考
- DCA (OctoBot)
- Dollar-Cost Averaging strategy buying fixed amounts at regular intervals. 關鍵參數： interval (Time between purchases), amount (Amount per purchase).原始碼： https://github.com/Drakkar-Software/OctoBot.
- Michael Burry Agent (AI Hedge Fund)
- Contrarian agent identifying mispriced securities through deep fundamental analysis. 原始碼： https://github.com/virattt/ai-hedge-fund.