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main.py
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main.py
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import streamlit as st
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
def calculate_rsi(data, window=14):
delta = data.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))
def backtest_strategy(df, strategy_function, initial_capital=10000):
position = 0
capital = initial_capital
trades = []
daily_returns = []
equity_curve = [initial_capital]
for i in range(1, len(df)):
current_data = df.iloc[i]
prev_data = df.iloc[i-1]
action, amount = strategy_function(current_data, prev_data, position, capital)
if action == "买入" and amount > 0:
cost = amount * current_data['close']
if cost <= capital:
capital -= cost
position += amount
trades.append({
"时间": current_data['timestamp'],
"操作": "买入",
"数量": amount,
"价格": current_data['close'],
"资金": capital
})
elif action == "卖出" and position > 0:
sell_amount = min(amount, position)
capital += sell_amount * current_data['close']
position -= sell_amount
trades.append({
"时间": current_data['timestamp'],
"操作": "卖出",
"数量": sell_amount,
"价格": current_data['close'],
"资金": capital
})
current_equity = capital + position * current_data['close']
equity_curve.append(current_equity)
daily_return = (current_equity - equity_curve[-2]) / equity_curve[-2]
daily_returns.append(daily_return)
final_equity = capital + position * df.iloc[-1]['close']
return trades, final_equity, daily_returns, equity_curve
def rsi_strategy(current_data, prev_data, position, capital):
current_rsi = current_data['RSI']
current_close = current_data['close']
prev_close = prev_data['close']
if current_rsi > 80 and position > 0:
return "卖出", position
elif current_rsi < 20 and position == 0:
shares_to_buy = capital // current_close
return "买入", shares_to_buy
elif position > 0 and (current_close - prev_close) > 10:
return "卖出", position
elif position > 0 and (prev_close - current_close) > 5:
return "卖出", position
return "持有", 0
def calculate_metrics(initial_capital, final_equity, daily_returns, equity_curve):
total_return = (final_equity - initial_capital) / initial_capital * 100
cagr = (final_equity / initial_capital) ** (252 / len(daily_returns)) - 1
daily_returns_series = pd.Series(daily_returns)
sharpe_ratio = np.sqrt(252) * daily_returns_series.mean() / daily_returns_series.std()
sortino_ratio = np.sqrt(252) * daily_returns_series.mean() / daily_returns_series[daily_returns_series < 0].std()
max_drawdown = np.min(equity_curve / np.maximum.accumulate(equity_curve) - 1)
calmar_ratio = cagr / abs(max_drawdown)
best_day = daily_returns_series.max()
worst_day = daily_returns_series.min()
metrics = {
"总回报率": f"{total_return:.2f}%",
"年化收益率 (CAGR)": f"{cagr*100:.2f}%",
"夏普比率": f"{sharpe_ratio:.2f}",
"索提诺比率": f"{sortino_ratio:.2f}",
"最大回撤": f"{max_drawdown*100:.2f}%",
"卡玛比率": f"{calmar_ratio:.2f}",
"最佳单日收益": f"{best_day*100:.2f}%",
"最差单日收益": f"{worst_day*100:.2f}%",
"交易次数": len(trades),
"最终权益": final_equity
}
return metrics
uploaded_file = st.file_uploader("选择CSV文件", type="csv")
if uploaded_file is not None:
df = pd.read_csv(uploaded_file, sep=',')
print(df.head())
df['timestamp'] = pd.to_datetime(df['timestamp'])
df = df.dropna()
df_resampled = df.set_index('timestamp').resample('15T').agg({'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last', 'volume': 'sum'})
df_resampled = df_resampled.dropna()
df_resampled['RSI'] = calculate_rsi(df_resampled['close'], window=14)
df_resampled = df_resampled.dropna()
df_resampled = df_resampled.reset_index()
st.write("数据预览:")
st.write(df_resampled.head())
columns = df_resampled.columns.tolist()
default_x = 'timestamp' if 'timestamp' in columns else columns[0]
default_y = 'close' if 'close' in columns else columns[0]
x_axis = st.selectbox("选择X轴", options=columns, index=columns.index(default_x))
y_axis = st.selectbox("选择Y轴", options=columns, index=columns.index(default_y))
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1, row_heights=[0.7, 0.3])
fig.add_trace(go.Scatter(x=df_resampled[x_axis], y=df_resampled[y_axis], name=y_axis), row=1, col=1)
fig.add_trace(go.Scatter(x=df_resampled[x_axis], y=df_resampled['RSI'], name='RSI'), row=2, col=1)
fig.update_layout(height=600, title_text=f'{y_axis} and RSI vs {x_axis}')
fig.update_yaxes(title_text=y_axis, row=1, col=1)
fig.update_yaxes(title_text='RSI', row=2, col=1)
st.plotly_chart(fig)
# 回测逻辑
if 'close' in df_resampled.columns and 'RSI' in df_resampled.columns:
initial_capital = 100000
trades, final_equity, daily_returns, equity_curve = backtest_strategy(df_resampled, rsi_strategy, initial_capital)
st.subheader("回测结果")
st.write("交易记录:")
if trades:
df_trades = pd.DataFrame(trades)
df_trades['时间'] = pd.to_datetime(df_trades['时间'])
df_trades['时间'] = df_trades['时间'].dt.strftime('%Y-%m-%d %H:%M')
df_trades['价格'] = df_trades['价格'].round(2)
df_trades['资金'] = df_trades['资金'].round(2)
st.table(df_trades)
else:
st.write("没有执行任何交易。")
metrics = calculate_metrics(initial_capital, final_equity, daily_returns, equity_curve)
st.subheader("策略表现指标")
for key, value in metrics.items():
st.write(f"{key}: {value}")
# 绘制权益曲线
fig_equity = go.Figure()
fig_equity.add_trace(go.Scatter(y=equity_curve, mode='lines', name='权益曲线'))
fig_equity.update_layout(title='策略权益曲线', xaxis_title='交易日', yaxis_title='权益')
st.plotly_chart(fig_equity)
else:
st.error("数据中缺少 'close' 或 'RSI' 列，无法进行回测。")
else:
st.write("请上传CSV文件以查看回测结果。")