pip install pandas
pip install numpy

import pandas_datareader.data as web
import datetime
 
# 確定起始日期和截止日期
start = datetime.datetime(2020, 1, 1)
end = datetime.datetime(2023, 1, 1)
 
# 獲取某一特定股票的數(shù)據(jù)
stock_data = web.DataReader('AAPL', 'yahoo', start, end)
print(stock_data.head())

import numpy as np
 
# 簡單移動平均線（SMA）
stock_data['SMA_20'] = stock_data['Adj Close'].rolling(window=20).mean()
stock_data['SMA_50'] = stock_data['Adj Close'].rolling(window=50).mean()
 
# 相對強(qiáng)弱指數(shù)（RSI）
def calculate_rsi(data, window):
    delta = data['Adj Close'].diff(1)
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi
 
stock_data['RSI_14'] = calculate_rsi(stock_data, 14)

# 生成買入/賣出信號
stock_data['Signal'] = 0
stock_data['Signal'][20:] = np.where(stock_data['SMA_20'][20:] > stock_data['SMA_50'][20:], 1, 0)
stock_data['Position'] = stock_data['Signal'].diff()

initial_capital = 100000.0
stock_data['Holdings'] = stock_data['Adj Close'] * stock_data['Position'].cumsum()
stock_data['Cash'] = initial_capital - (stock_data['Adj Close'] * stock_data['Position']).cumsum()
stock_data['Total'] = stock_data['Cash'] + stock_data['Holdings']
 
# 計算收益
stock_data['Returns'] = stock_data['Total'].pct_change()
 
# 打印最終投資組合價值
print("Final Portfolio Value: ${}".format(stock_data['Total'].iloc[-1]))

cumulative_returns = (stock_data['Total'].iloc[-1] - initial_capital) / initial_capital
average_daily_returns = stock_data['Returns'].mean()
volatility = stock_data['Returns'].std()
 
print("Cumulative Returns: {:.2f}%".format(cumulative_returns * 100))
print("Average Daily Returns: {:.4f}".format(average_daily_returns))
print("Volatility: {:.4f}".format(volatility))

# 假設(shè)已有包含'Volume'和'Close'列的DataFrame：vwap_data
vwap_data['VWAP'] = (vwap_data['Close'] * vwap_data['Volume']).cumsum() / vwap_data['Volume'].cumsum()

vwap_data['Signal'] = np.where(vwap_data['Close'] > vwap_data['VWAP'], -1, 1)
vwap_data['Position'] = vwap_data['Signal'].diff()

# 假設(shè)已有包含'Close'列的DataFrame和股票停止損失價格stock_stop_loss_price、股票獲利了結(jié)價格stock_take_profit_price
def handle_data(context, data):
    if data[context.stock].price < context.stock_stop_loss_price:
        order_target(context.stock, 0)  # 止損
    elif data[context.stock].price > context.stock_take_profit_price:
        order_target(context.stock, 0)  # 止盈