策略类型
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 个算法2 个库
算法网络
均值回归算法如何跨库连接
Mean Reversion
OctoBot1 algos
DCAbeginner
交易决策流程
均值回归算法如何在交易系统中协同工作
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
算法对比
在关键维度上对比均值回归算法
算法对比矩阵点击列以展开详情
| 指标 | DCAOctoBot | Michael Burry AgentAI Hedge Fund |
|---|---|---|
| 复杂度 | ⭐⭐beginner | ⭐⭐⭐intermediate |
| 预测类型 | 混合 | 混合 |
| 训练速度 | ⚡⚡ | ⚡⚡ |
| 准确度 | 📊📊 | 📊📊 |
| 最适合 | 通用 | 通用 |
Mean Reversion Trading Strategies，算法参考
- DCA (OctoBot)
- Dollar-Cost Averaging strategy buying fixed amounts at regular intervals. 关键参数： interval (Time between purchases), amount (Amount per purchase).源代码： https://github.com/Drakkar-Software/OctoBot.
- Michael Burry Agent (AI Hedge Fund)
- Contrarian agent identifying mispriced securities through deep fundamental analysis. 源代码： https://github.com/virattt/ai-hedge-fund.