Question

我是一名Python初学者并为简单的移动平均策略编写了一个函数。我在函数内部创建了一个组合DataFrame，现在我想在函数外部使用这个DataFrame来绘制一些图形。我的解决方案是：退货组合 - 但这不起作用。有人能帮助我吗？

这是我的代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Import a data source - FSE-Data with Index 'Date'
all_close_prices = pd.read_csv('FSE_daily_close.csv')
all_close_prices = all_close_prices.set_index('Date')
# Fill NaN Values with the last available stock price - except for Zalando
all_close_prices = all_close_prices.fillna(method='ffill')
# Import ticker symbols
ticker_list = list(all_close_prices)
# Zalando 'FSE/ZO1_X' (position row 99) - doesn't begin in 2004
# Drop Zalando
all_close_prices.drop('FSE/ZO1_X', axis=1)
# Also from the ticker list
ticker_list.remove('FSE/ZO1_X')
# Create an empty signal dataframe with datetime index equivalent to the stocks
signals = pd.DataFrame(index=all_close_prices.index)

def ma_strategy(ticker, long_window, short_window):
    # Calculate the moving avergaes
    moving_avg_long = all_close_prices.rolling(window=long_window, min_periods=1).mean()
    moving_avg_short = all_close_prices.rolling(window=short_window, min_periods=1).mean()
    moving_avg_short = moving_avg_short
    moving_avg_long = moving_avg_long
    # Add the two MAs for the stocks in the ticker_list to the signals dataframe
    for i in ticker_list:
        signals['moving_avg_short_' + i] = moving_avg_short[i]
        signals['moving_avg_long_' + i] = moving_avg_long[i]

    # Set up the signals
    for i in ticker_list:
        signals['signal_' + i] = np.where(signals['moving_avg_short_' + i] > signals['moving_avg_long_' + i], 1, 0)
        signals['positions_' + i] = signals['signal_' + i].diff(periods=1)
    #Backtest
    initial_capital = float(100000)
    # Create a DataFrame `positions` with index of signals
    positions = pd.DataFrame(index=all_close_prices)
    # Create a new column in the positions DataFrame
    # On the days that the signal is 1 (short moving average crosses the long moving average, you’ll buy a 100 shares.
    # The days on which the signal is 0, the final result will be 0 as a result of the operation 100*signals['signal']
    positions = 100 * signals[['signal_' + ticker]]
    # Store the portfolio value owned with the stock
    # DataFrame.multiply(other, axis='columns', fill_value=None) - Multiplication of dataframe and other, element-wise
    # Store the difference in shares owned - same like position column in signals
    pos_diff = positions.diff()
    # Add `holdings` to portfolio
    portfolio = pd.DataFrame(index=all_close_prices.index)
    portfolio['holdings'] = (positions.multiply(all_close_prices[ticker], axis=0)).sum(axis=1)
    # Add `cash` to portfolio
    portfolio['cash'] = initial_capital - (pos_diff.multiply(all_close_prices[ticker], axis=0)).sum(
        axis=1).cumsum()
    # Add `total` to portfolio
    portfolio['total'] = portfolio['cash'] + portfolio['holdings']
    # Add `returns` to portfolio
    portfolio['return'] = portfolio['total'].pct_change()
    portfolio['return_cum'] = portfolio['total'].pct_change().cumsum()
    return portfolio


ma_strategy('FSE/VOW3_X',20,5)

# Visualize the total value of the portfolio
portfolio_value = plt.figure(figsize=(12, 8))
ax1 = portfolio_value.add_subplot(1, 1, 1, ylabel='Portfolio value in $')
# Plot the equity curve in dollars
portfolio['total'].plot(ax=ax1, lw=2.)

Answer 1

您需要将函数返回值赋给变量。这句话说

ma_strategy('FSE/VOW3_X',20,5)

可能需要更改为

portfolio = ma_strategy('FSE/VOW3_X',20,5)

使用在函数

1 个答案: