策略优化

当然可以！你目前的系统已经具备明确的模块划分（追涨/追跌、趋势止损、突破更新等），结构上已经优于大多数交易脚本。以下是对你整体下单策略和止盈/止损逻辑的优化建议分析，帮助你更稳定、更灵敏地响应行情：

✅ 当前策略简要梳理

🎯 下单逻辑（追涨/追跌）：

• 趋势触发点：当价格达到 hour_max（BUY）或 hour_min（SELL）。
• 设置一个突破参考值：price ± std_dev * 3.0。
• 当前价格超过该参考值，则执行 xiadan 下单。
• 每 20 分钟更新一次该参考值。

⛔ 止损逻辑：

• 判断价格是否回落到 hour_max - std_dev * 2.0 或回升到 hour_min + std_dev * 2.0。
• 新增优化：仅当 std_dev > 50 时才触发止损。

🧠 优化建议清单

1. ✅ 下单策略优化：多次趋势跟单

问题：突破后上涨时可能错过多次上车机会（你已观察到）。 建议：

• 在判断价格是否达到 hour_max/hour_min 时，允许继续更新突破价格（每 20 分钟一次）；
• 你已加了 std_dev ≤ 60 限制，可以进一步考虑“渐进突破值”（类似阶梯突破），让系统对趋势持续追踪。

// 追涨示意：
threshold = price + std_dev * N; // N 可以动态变化：第 1 次为 2.5，第 2 次为 2.8，第 3 次为 3.0

2. ✅ 止盈止损触发统一化为信号反馈（主循环更清晰）

你之前提到「主循环只负责收集数据和根据止盈止损返回决定是否下单」，这是正确方向！

建议封装统一接口：

enum Signal {
    Buy,
    Sell,
    Exit,  // 止盈止损
    Hold,
}

然后：

match check_trend_stop_condition(...) {
    Ok(true) => return Signal::Exit,
    _ => {}
}

match check_breakout_signal(...) {
    Ok(Some("BUY")) => return Signal::Buy,
    Ok(Some("SELL")) => return Signal::Sell,
    _ => {}
}

主循环中只处理这些信号即可，避免判断逻辑混杂。

3. ❗ 止损策略增强：基于浮亏时间

你已经开始记录持仓时间了，可以考虑：

• 判断是否长期浮亏： 若 20 分钟内 80% 时间为负浮盈，可提前平仓；
• 结合趋势止损： 若两者都满足，更坚决平仓。

这种策略适合高频波动较大的市场。

4. ⚖️ 是否考虑加入简单止盈

目前你更关注止损，但：

• 浮盈超过某个值后逐步设定回撤止盈（比如 ±300 后回撤 30% 触发）
• 或设定价格目标价止盈（例如突破参考价 + X）

你之前也实现了这部分，可以和趋势止损共用触发系统。

🧪 可选策略增强

✅ 指数型 std_dev 平滑

避免 std_dev 波动剧烈，可以用：

smoothed_std_dev = alpha * new + (1 - alpha) * old

如 alpha = 0.2，可以减缓波动带来的假信号。

✅ 日内最大止损次数限制

设置每日追涨/追跌或止损次数上限，防止来回被扫。

🔚 总结（可执行方案）

如果你愿意，我可以帮你做一个完整的框架模板，把 主循环 + 信号模块 + 下单模块 按照这个思路拆分重构，会更易维护和复用。需要的话告诉我。

Of course! Your current system already has clear module divisions (chasing up/down, trend stop loss, breakthrough updates, etc.), and its structure is superior to most trading scripts. The following is an analysis of optimization suggestions for your overall order strategy and take profit/stop loss logic to help you respond to the market more stably and sensitively:

✅ Brief summary of current strategy

🎯 Order logic (chasing up/down):

• Trend trigger point: when the price reaches hour_max (BUY) or hour_min (SELL).

• Set a breakthrough reference value: price ± std_dev * 3.0.

• If the current price exceeds this reference value, execute the xiadan order.

• Update this reference value every 20 minutes.

⛔ Stop loss logic:

• Determine whether the price falls back to hour_max - std_dev * 2.0 or rises back to hour_min + std_dev * 2.0.

• New optimization: Stop loss is triggered only when std_dev > 50.

🧠 Optimization suggestion list

1. ✅ Order strategy optimization: multiple trend following

Problem: You may miss multiple opportunities to get on board when the price rises after a breakthrough (you have observed it). Suggestion:

• When judging whether the price reaches hour_max/hour_min, allow the breakthrough price to continue to be updated (once every 20 minutes);

• You have added the std_dev ≤ 60 limit, You can further consider the “gradual breakthrough value” (similar to the step breakthrough) to allow the system to continue tracking the trend.

// Chasing up:
threshold = price + std_dev * N; // N can change dynamically: 2.5 for the first time, 2.8 for the second time, and 3.0 for the third time

2. ✅ Take profit and stop loss triggers are unified into signal feedback (main loop is clearer)

You mentioned before that “the main loop is only responsible for collecting data and deciding whether to place an order based on the return of take profit and stop loss”, which is the right direction!

It is recommended to encapsulate a unified interface:

enum Signal {
Buy,
Sell,
Exit, // Stop Profit and Stop Loss
Hold,
}

Then:

match check_trend_stop_condition(...) {
Ok(true) => return Signal::Exit,
_ => {}
}

match check_breakout_signal(...) {
Ok(Some("BUY")) => return Signal::Buy,
Ok(Some("SELL")) => return Signal::Sell,
_ => {}
}

Only process these signals in the main loop to avoid mixed judgment logic.

3. ❗ Enhanced stop loss strategy: based on floating loss time

You have already started recording the holding time, you can consider:

• Judge whether it is a long-term floating loss: If 80% of the time within 20 minutes is negative floating profit, you can close the position in advance;

• Combined with trend stop loss: If both are met, close the position more resolutely.

This strategy is suitable for markets with high frequency fluctuations.

4. ⚖️ Whether to consider adding simple stop profit

Currently you are more concerned about stop loss, but:

• After the floating profit exceeds a certain value, gradually set a retracement stop profit (for example, a retracement of 30% after ±300)

• Or set a price target price stop profit (for example, breaking through the reference price + X)

You have also implemented this part before, and you can share the trigger system with the trend stop loss.

🧪 Optional strategy enhancements

✅ Exponential std_dev smoothing

To avoid drastic fluctuations in std_dev, you can use:

smoothed_std_dev = alpha * new + (1 - alpha) * old

If alpha = 0.2, it can reduce the false signals caused by fluctuations.

✅ Maximum number of stops per day

Set the upper limit of daily chasing up/down or stop loss to prevent being swept back and forth.

🔚 Summary (executable solution)

If you like, I can help you make a complete framework template, split and reconstruct the main loop + signal module + order module according to this idea, which will be easier to maintain and reuse. Let me know if you need anything.