GFS上涨通道量化策略1

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我下载了 GFS 从 2025-11-14 到 2025-11-21 的 1 分钟级别数据，用于计算 120 周期与 250 周期的移动平均线。策略的买入信号设定为：两条均线均呈现上升趋势，且短期均线（120）位于长期均线（250）之上；卖出信号则为价格回落至 120 均线时触发。 该策略最初在 moomoo 平台上运行，但由于缺少 2025-11-21 的完整数据，后续改用 Python 结合 yfinance 进行回测。目前回测范围仅覆盖 11 月 21 日当天的交易情况，初步结果显示买卖点的捕捉效果较为理想。 尽管已有一定成效，但策略仍具备优化潜力。例如，可加入逻辑避免在卖出后立即重新买入的情况，提升整体执行效率和信号质量。 GFS 原本是 AMD 分拆出来的芯片代工企业，其股价波动特性适合短线量化策略测试。以下是实现该策略的部分代码逻辑，欢迎交流指正。

import yfinance as yf 
import pandas as pd
import os 


def get_data():
    filepath = 'GFS_20251121_1m.xlsx'
    if os.path.exists(filepath):
        df = pd.read_excel(filepath)
        df['datetime'] = pd.to_datetime(df['datetime'])
    else:
        df = yf.download(tickers='GFS', period='1d', interval='1m', progress=False, start='2025-11-21')
        if isinstance(df.columns, pd.MultiIndex):
            df.columns = df.columns.droplevel(1)
        df = df[['Open', 'High', 'Low', 'Close', 'Volume']]
        df.columns = ['open', 'high', 'low', 'close', 'volume']
        for col in ['open', 'high', 'low', 'close']:
            df[col] = df[col].round(3)
        df.index.name = 'datetime'
        df.reset_index(inplace=True)
        df['datetime'] = pd.to_datetime(df['datetime'], format="%Y-%m-%d").dt.tz_localize(None)
        df.to_excel(filepath, index=False)
    return df



df = get_data()
df.head()

df['ma120'] = df['close'].rolling(window=120).mean()
df['ma250'] = df['close'].rolling(window=250).mean()
df['ma120_prev'] = df['ma120'].shift(1)
df['ma250_prev'] = df['ma250'].shift(1)

df['trend_long'] = (
    (df['ma120_prev'] < df['ma120']) &
    (df['ma250_prev'] < df['ma250']) &
    (df['close'] > df['ma120']) &
    (df['ma120'] > df['ma250'])
)






position = 0
buy_price = 0
qty = 100
commission = 2
equity = 4000  


trade_log = []

for i in range(len(df)):
    row = df.iloc[i]
    price = row['close']
    ma120 = row['ma120']
    ma120_prev = row['ma120_prev']
    ma250 = row['ma250']
    ma250_prev = row['ma250_prev']

    # Skip until MAs exist
    if pd.isna(ma120) or pd.isna(ma250):
        continue

    if row['datetime'] < pd.Timestamp('2025-11-20'):
        continue

    # BUY CONDITION (same as your strategy)
    if position == 0:
        if (ma120_prev < ma120) and (ma250_prev < ma250) and (price > ma120) and (ma120 > ma250):
            position = qty
            buy_price = price
            equity -= price * qty + commission
            trade_log.append({
                'datetime': row['datetime'],
                'type': 'BUY',
                'price': price
            })

    # SELL CONDITION
    if position > 0:
        profit = (price - buy_price) * qty - commission

        if price < ma120 and profit > 0:
            equity += price * qty - commission
            position = 0
            trade_log.append({
                'datetime': row['datetime'],
                'type': 'SELL',
                'price': price,
                'profit': round(profit, 2)
            })

# Close any open position at the end
if position > 0:
    final_price = df['close'].iloc[-1]
    equity += final_price * qty - commission
    profit = (final_price - buy_price) * qty
    trade_log.append({
        'datetime': df['datetime'].iloc[-1],
        'type': 'FORCED EXIT',
        'price': final_price,
        'profit': round(profit, 2)
    })
    position = 0

# -------------------------------------------------------
# 4. Results
# -------------------------------------------------------
trades = pd.DataFrame(trade_log)
print("Final Equity:", equity)
print("Net Profit:", equity - 4000)
print("\nTrade Log:")
print(trades)

trades.to_csv("GFS_20251121_trades.csv", index=False)

            
        
import mplfinance as mpf
import numpy as np


df_plot = df.copy()
df_plot.set_index("datetime", inplace=True)


df_plot['buy'] = np.nan
df_plot['sell'] = np.nan

for t in trade_log:
    dt = t['datetime']
    if t['type'] == 'BUY':
        df_plot.loc[dt, 'buy'] = df_plot.loc[dt, 'close']
    elif t['type'] == 'SELL':
        df_plot.loc[dt, 'sell'] = df_plot.loc[dt, 'close']


apds = [
    mpf.make_addplot(df_plot['ma120'], color='blue'),
    mpf.make_addplot(df_plot['ma250'], color='red'),
    mpf.make_addplot(df_plot['buy'], type='scatter', markersize=80, marker='^', color='green'),
    mpf.make_addplot(df_plot['sell'], type='scatter', markersize=80, marker='v', color='red'),
]

mpf.plot(
    df_plot,
    type='candle',
    addplot=apds,
    volume=False,
    title='GFS Backtest with MA120/MA250 and Trade Points',
    style='yahoo',
    figsize=(18, 9)
)

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关键词：量化策略 GFS Commission condition Dataframe