Optimizing_MACD_Trading_Strategies_A_Dance_of_Finance_Wavelets_and_Genetics

research/arxiv_papers/Optimizing_MACD_Trading_Strategies_A_Dance_of_Finance_Wavelets_and_Genetics/divergence.py

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+import os
+import numpy as np
+import pandas as pd
+from scipy.signal import argrelextrema
+
+def find_local_extrema(data, window, greater=True):
+    special_indices = []
+    for i in range(window + 1, len(data)):
+        window_data = data[i-window-1:i]
+        if greater:
+            if data[i-1] == window_data.max() and data[i] < data[i-1]:
+                special_indices.append(i-1)
+        else:
+            if data[i-1] == window_data.min() and data[i] > data[i-1]:
+                special_indices.append(i-1)
+
+    return np.array(special_indices)
+
+folder_path = 'data'
+ans = []
+for file in os.listdir(folder_path):
+    if file.endswith('.xlsx'):
+        file_path = os.path.join(folder_path, file)
+        data = pd.read_excel(file_path)
+        data = data.iloc[:-2]
+        base_name, _ = os.path.splitext(file)
+        data = data.iloc[:-2]
+
+        ema_short = data['收盘价(元)'].ewm(span=12, adjust=False).mean()
+        ema_long = data['收盘价(元)'].ewm(span=26, adjust=False).mean()
+        dif = ema_short - ema_long
+        dea = dif.ewm(span=9, adjust=False).mean()
+        macd_histogram = dif - dea
+
+        data['DIF'] = dif
+        data['DEA'] = dea
+        data['MACD'] = macd_histogram
+        red_bar = np.where(macd_histogram > 0, macd_histogram, 0)
+        blue_bar = np.where(macd_histogram < 0, macd_histogram, 0)
+
+        order = 15
+
+        # 找到股价和MACD的局部极大值和极小值索引
+        price_max_idx = find_local_extrema(data['收盘价(元)'], window=order, greater=True)
+        price_min_idx = find_local_extrema(data['收盘价(元)'], window=order, greater=False)
+        macd_max_idx = find_local_extrema(data['MACD'], window=order, greater=True)
+        macd_min_idx = find_local_extrema(data['MACD'], window=order, greater=False)
+
+        data['price_max'] = pd.Series(data['收盘价(元)'].iloc[price_max_idx].values, index=price_max_idx)
+        data['price_min'] = pd.Series(data['收盘价(元)'].iloc[price_min_idx].values, index=price_min_idx)
+        data['macd_max'] = pd.Series(data['MACD'].iloc[macd_max_idx].values, index=macd_max_idx)
+        data['macd_min'] = pd.Series(data['MACD'].iloc[macd_min_idx].values, index=macd_min_idx)
+
+        # 初始化背离标识
+        data['bull_divergence'] = False
+        data['bear_divergence'] = False
+
+        for i in range(1, len(price_max_idx)):
+            current_idx = price_max_idx[i]
+            previous_idx = price_max_idx[i-1]
+
+            if data['收盘价(元)'].iloc[current_idx] > data['收盘价(元)'].iloc[previous_idx] and data['MACD'].iloc[current_idx] < data['MACD'].iloc[previous_idx]:
+                data.loc[current_idx, 'bull_divergence'] = True
+
+        for i in range(1, len(macd_max_idx)):
+            current_idx = macd_max_idx[i]
+            previous_idx = macd_max_idx[i-1]
+
+            if data['收盘价(元)'].iloc[current_idx] > data['收盘价(元)'].iloc[previous_idx] and data['MACD'].iloc[current_idx] < data['MACD'].iloc[previous_idx]:
+                data.loc[current_idx, 'bull_divergence'] = True
+
+        for i in range(1, len(price_min_idx)):
+            current_idx = price_min_idx[i]
+            previous_idx = price_min_idx[i-1]
+
+            if data['收盘价(元)'].iloc[current_idx] < data['收盘价(元)'].iloc[previous_idx] and data['MACD'].iloc[current_idx] > data['MACD'].iloc[previous_idx]:
+                data.loc[current_idx, 'bear_divergence'] = True
+
+        for i in range(1, len(macd_min_idx)):
+            current_idx = macd_min_idx[i]
+            previous_idx = macd_min_idx[i-1]
+
+            if data['收盘价(元)'].iloc[current_idx] < data['收盘价(元)'].iloc[previous_idx] and data['MACD'].iloc[current_idx] > data['MACD'].iloc[previous_idx]:
+                data.loc[current_idx, 'bear_divergence'] = True
+
+        initial_capital = 500000.0
+        capital = initial_capital
+        stocks_held = 0
+        portfolio_values = []
+
+        trades_tot = 0
+        buy_price = 0
+        trades = 0
+        gains = []
+        buy_queue = []
+        for i in range(len(data) - 1):
+            if data['bear_divergence'][i] and capital >= data['收盘价(元)'][i]:
+                num_shares_to_buy = capital // data['收盘价(元)'].iloc[i]
+                buy_price += num_shares_to_buy * data['收盘价(元)'].iloc[i]
+                capital -= num_shares_to_buy * data['收盘价(元)'].iloc[i]
+                stocks_held += num_shares_to_buy
+                trades_tot += 1
+            elif data['bull_divergence'][i] and stocks_held > 0:
+                num_shares_to_sell = stocks_held
+                sell_price = num_shares_to_sell * data['收盘价(元)'].iloc[i]
+                capital += sell_price
+                stocks_held = 0
+                trades += 1
+                trades_tot += 1
+                gains.append(sell_price - buy_price)
+                buy_price = 0
+            portfolio_value = capital + stocks_held * data['收盘价(元)'][i]
+            portfolio_values.append(portfolio_value)
+
+        if stocks_held > 0:
+            num_shares_to_sell = stocks_held
+            capital += num_shares_to_sell * data['收盘价(元)'].iloc[len(data)-1]
+            stocks_held = 0
+            trades += 1
+            trades_tot += 1
+            gains.append(sell_price - buy_price)
+
+        portfolio_value = capital + stocks_held * data['收盘价(元)'][len(data)-1]
+        portfolio_values.append(portfolio_value)
+
+        gains = np.array(gains)
+        profits = gains[gains > 0]
+        losses = gains[gains < 0]
+
+        win_rate = len(profits) / trades if trades > 0 else 0
+        avg_gain = np.mean(profits) if len(profits) > 0 else 0
+        avg_loss = np.mean(losses) if len(losses) > 0 else 0
+        odds_ratio = abs(avg_gain / avg_loss) if avg_loss != 0 else 0
+        trade_frequency = trades_tot / len(data) if len(data) > 0 else 0
+
+        total_return = capital - initial_capital
+        annual_return = ((total_return / initial_capital) + 1) ** (252 / len(data)) - 1
+        portfolio_values = np.array(portfolio_values)
+
+        annual_risk_free_rate = 2.653 / 100
+        daily_risk_free_rate = (1 + annual_risk_free_rate) ** (1/252) - 1
+        returns = np.diff(portfolio_values) / portfolio_values[:-1]
+        adjusted_returns = returns - daily_risk_free_rate
+        sharpe_ratio = (np.mean(adjusted_returns) / np.std(adjusted_returns) * np.sqrt(len(data))
+                        if np.std(adjusted_returns) != 0 else 0)
+        max_drawdown = np.max(np.maximum.accumulate(portfolio_values) - portfolio_values) / np.max(portfolio_values) if np.max(portfolio_values) > 0 else 0
+
+        ans.append({
+            "name": base_name,
+            "type": "dif",
+            "win_rate": win_rate,
+            "odds_ratio": odds_ratio,
+            "trade_frequency": trade_frequency,
+            "total_return": total_return,
+            "annual_return": annual_return,
+            "sharpe_ratio": sharpe_ratio,
+            "max_drawdown": max_drawdown,
+        })
+ansdf = pd.DataFrame(ans)
+
+file_path = "x3.xlsx"
+ansdf.to_excel(file_path, index=False)