chore: move indicator example files to docs folder

docs/dual_ma_with_params.py

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+# ============================================================
+# 双均线策略 (支持外部参数配置)
+# Dual Moving Average Strategy with External Parameters
+# ============================================================
+# 
+# 使用方法:
+# 1. 在交易助手中选择此指标
+# 2. 根据不同币种配置不同参数
+#    - BTC/USDT: sma_short=5, sma_long=10
+#    - ETH/USDT: sma_short=5, sma_long=20
+# 
+# ============================================================
+
+# === 参数声明 (会在前端表单中显示) ===
+# @param sma_short int 14 短期均线周期
+# @param sma_long int 28 长期均线周期
+
+# === 获取参数 (带默认值作为后备) ===
+sma_short_period = params.get('sma_short', 14)
+sma_long_period = params.get('sma_long', 28)
+
+# === 指标信息 ===
+my_indicator_name = "双均线策略"
+my_indicator_description = f"短期{sma_short_period}/长期{sma_long_period}均线交叉策略"
+
+# === 计算均线 ===
+df = df.copy()
+sma_short = df["close"].rolling(sma_short_period).mean()
+sma_long = df["close"].rolling(sma_long_period).mean()
+
+# === 生成买卖信号 ===
+# 金叉：短期均线上穿长期均线
+buy = (sma_short > sma_long) & (sma_short.shift(1) <= sma_long.shift(1))
+# 死叉：短期均线下穿长期均线
+sell = (sma_short < sma_long) & (sma_short.shift(1) >= sma_long.shift(1))
+
+df["buy"] = buy.fillna(False).astype(bool)
+df["sell"] = sell.fillna(False).astype(bool)
+
+# === 买卖标记点 (用于K线图显示) ===
+buy_marks = [df["low"].iloc[i] * 0.995 if df["buy"].iloc[i] else None for i in range(len(df))]
+sell_marks = [df["high"].iloc[i] * 1.005 if df["sell"].iloc[i] else None for i in range(len(df))]
+
+# === 图表输出配置 ===
+output = {
+    "name": my_indicator_name,
+    "plots": [
+        {"name": f"SMA{sma_short_period}", "data": sma_short.tolist(), "color": "#FF9800", "overlay": True},
+        {"name": f"SMA{sma_long_period}", "data": sma_long.tolist(), "color": "#3F51B5", "overlay": True}
+    ],
+    "signals": [
+        {"type": "buy", "text": "B", "data": buy_marks, "color": "#00E676"},
+        {"type": "sell", "text": "S", "data": sell_marks, "color": "#FF5252"}
+    ]
+}

docs/multi_indicator_composite.py

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+# ============================================================
+# 多指标组合策略 (均线+RSI+MACD)
+# Multi-Indicator Composite Strategy
+# ============================================================
+# 
+# 使用方法:
+# 1. 可配置均线周期、RSI阈值等参数
+# 2. 买入条件: RSI超卖 + MACD金叉 + 成交量放大
+# 3. 卖出条件: RSI超买 或 MACD死叉
+# 
+# ============================================================
+
+# === 参数声明 ===
+# @param sma_short int 10 短期均线周期
+# @param sma_long int 30 长期均线周期
+# @param rsi_period int 14 RSI周期
+# @param rsi_oversold int 30 RSI超卖阈值
+# @param rsi_overbought int 70 RSI超买阈值
+# @param use_macd bool True 是否使用MACD过滤
+# @param use_volume bool False 是否使用成交量过滤
+# @param volume_mult float 1.5 成交量放大倍数
+
+# === 获取参数 ===
+sma_short_period = params.get('sma_short', 10)
+sma_long_period = params.get('sma_long', 30)
+rsi_period = params.get('rsi_period', 14)
+rsi_oversold = params.get('rsi_oversold', 30)
+rsi_overbought = params.get('rsi_overbought', 70)
+use_macd = params.get('use_macd', True)
+use_volume = params.get('use_volume', False)
+volume_mult = params.get('volume_mult', 1.5)
+
+# === 指标信息 ===
+my_indicator_name = "多指标组合策略"
+my_indicator_description = f"SMA{sma_short_period}/{sma_long_period} + RSI{rsi_period}"
+
+df = df.copy()
+
+# === 计算均线 ===
+sma_short = df["close"].rolling(sma_short_period).mean()
+sma_long = df["close"].rolling(sma_long_period).mean()
+
+# === 计算RSI ===
+delta = df["close"].diff()
+gain = delta.where(delta > 0, 0).rolling(window=rsi_period).mean()
+loss = (-delta.where(delta < 0, 0)).rolling(window=rsi_period).mean()
+rs = gain / loss
+rsi = 100 - (100 / (1 + rs))
+
+# === 计算MACD ===
+exp1 = df["close"].ewm(span=12, adjust=False).mean()
+exp2 = df["close"].ewm(span=26, adjust=False).mean()
+macd = exp1 - exp2
+macd_signal = macd.ewm(span=9, adjust=False).mean()
+macd_hist = macd - macd_signal
+
+# === 计算成交量均线 ===
+volume_ma = df["volume"].rolling(20).mean()
+
+# === 生成信号条件 ===
+# 均线金叉
+ma_golden = (sma_short > sma_long) & (sma_short.shift(1) <= sma_long.shift(1))
+# 均线死叉
+ma_death = (sma_short < sma_long) & (sma_short.shift(1) >= sma_long.shift(1))
+# RSI超卖
+rsi_buy = rsi < rsi_oversold
+# RSI超买
+rsi_sell = rsi > rsi_overbought
+# MACD金叉
+macd_golden = (macd > macd_signal) & (macd.shift(1) <= macd_signal.shift(1))
+# MACD死叉
+macd_death = (macd < macd_signal) & (macd.shift(1) >= macd_signal.shift(1))
+# 成交量放大
+volume_up = df["volume"] > volume_ma * volume_mult
+
+# === 综合买卖信号 ===
+buy = ma_golden | rsi_buy  # 均线金叉 或 RSI超卖
+
+if use_macd:
+    buy = buy & (macd > macd_signal)  # 需要MACD向上
+    
+if use_volume:
+    buy = buy & volume_up  # 需要成交量放大
+
+sell = ma_death | rsi_sell  # 均线死叉 或 RSI超买
+
+if use_macd:
+    sell = sell | macd_death  # MACD死叉也卖出
+
+df["buy"] = buy.fillna(False).astype(bool)
+df["sell"] = sell.fillna(False).astype(bool)
+
+# === 买卖标记点 ===
+buy_marks = [df["low"].iloc[i] * 0.995 if df["buy"].iloc[i] else None for i in range(len(df))]
+sell_marks = [df["high"].iloc[i] * 1.005 if df["sell"].iloc[i] else None for i in range(len(df))]
+
+# === 图表输出配置 ===
+output = {
+    "name": my_indicator_name,
+    "plots": [
+        {"name": f"SMA{sma_short_period}", "data": sma_short.tolist(), "color": "#FF9800", "overlay": True},
+        {"name": f"SMA{sma_long_period}", "data": sma_long.tolist(), "color": "#3F51B5", "overlay": True}
+    ],
+    "signals": [
+        {"type": "buy", "text": "B", "data": buy_marks, "color": "#00E676"},
+        {"type": "sell", "text": "S", "data": sell_marks, "color": "#FF5252"}
+    ]
+}