第一階段、一個簡單策略入門量化投資

1-3移動均線交叉策略2

上一篇文章1-2 移動均線交叉策略1中我們最后提到：
如果我們從第一天買入股票，一直持有股票，最后一天賣出，獲得的收益是每股124.02美元，收益率為412%
如果按照我們的策略進行買賣，總共完成了21筆交易，收益為美股82.35美元，收益率為273%
仔細分析后，我發(fā)現有以下兩點需要改進：
1.回測需要使用調整后價格。
2.策略收益的計算有問題，需要充分考慮早先交易產生的收益或虧損對后續(xù)交易的收益產生的影響。
針對這兩點，修改代碼。
假設投資的初始資金為100萬，得到的資產變化圖如下：

修正后，我們的策略在截止日期的資金總額為298萬，也就是說平均收益率為198%
雖然這回收益的計算錯誤已經改正，但結果是令人沮喪的，我們使用滑動平均模型得到的收益確實比不操作要少許多。

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注：由于第一次寫的博客不小心被自己蓋掉了，你看到的是重寫了一遍的，內容簡略了許多，實在是不高興再寫一遍了，見諒，各位施主直接看代碼吧。

完整代碼

import numpy as np import pandas as pd import pandas_datareader.data as web import matplotlib.pyplot as plt import datetime import timeimport draw_candle import stockdata_preProcess as preProcess##### in stock_movingAverageCross_Strategy.py # we have give a simple strategy to trade stocks called Moving Average Model # it is still imperfect and we find it is necessary to consider the adjust price # so in stock_movingAverageCross_Strategy2.py we try to improve the strategy##### read the data from csv apple=pd.read_csv(filepath_or_buffer='data_AAPL.csv') # note that some format(data type) of data we read from .csv has changed # for example the attribute 'Date' should be the index of the dataframe, and the date type changed from datetime to string # this changes would made our methods in draw_candle.py got trouble # So we need to make the following changes date_list = [] for i in range(len(apple)):date_str = apple['Date'][i]t = time.strptime(date_str, "%Y-%m-%d")temp_date = datetime.datetime(t[0], t[1], t[2])date_list.append(temp_date) apple['DateTime'] = pd.Series(date_list,apple.index) del apple['Date'] apple = apple.set_index('DateTime')##### it seems we need to consider the adjust price # yahoo only provides the adjust price of 'Close' # but it is easy to adjust the price by the proportion of 'Adj Close' and 'Close'# now we will use the adjust data apple_adj in the following code apple_adj = preProcess.ohlc_adjust(apple)##### compute the trade information like before, use adjust price apple_adj["20d"] = np.round(apple_adj["Close"].rolling(window = 20, center = False).mean(), 2) apple_adj["50d"] = np.round(apple_adj["Close"].rolling(window = 50, center = False).mean(), 2) apple_adj["200d"] = np.round(apple_adj["Close"].rolling(window = 200, center = False).mean(), 2)apple_adj['20d-50d'] = apple_adj['20d'] - apple_adj['50d']apple_adj["Regime"] = np.where(apple_adj['20d-50d'] > 0, 1, 0) apple_adj["Regime"] = np.where(apple_adj['20d-50d'] < 0, -1, apple_adj["Regime"])regime_orig = apple_adj.ix[-1, "Regime"] apple_adj.ix[-1, "Regime"] = 0 apple_adj["Signal"] = np.sign(apple_adj["Regime"] - apple_adj["Regime"].shift(1)) apple_adj.ix[-1, "Regime"] = regime_origapple_adj_signals = pd.concat([pd.DataFrame({"Price": apple_adj.loc[apple_adj["Signal"] == 1, "Close"],"Regime": apple_adj.loc[apple_adj["Signal"] == 1, "Regime"],"Signal": "Buy"}),pd.DataFrame({"Price": apple_adj.loc[apple_adj["Signal"] == -1, "Close"],"Regime": apple_adj.loc[apple_adj["Signal"] == -1, "Regime"],"Signal": "Sell"}),]) apple_adj_signals.sort_index(inplace = True)apple_adj_long_profits = pd.DataFrame({"Price": apple_adj_signals.loc[(apple_adj_signals["Signal"] == "Buy") &apple_adj_signals["Regime"] == 1, "Price"],"Profit": pd.Series(apple_adj_signals["Price"] - apple_adj_signals["Price"].shift(1)).loc[apple_adj_signals.loc[(apple_adj_signals["Signal"].shift(1) == "Buy") & (apple_adj_signals["Regime"].shift(1) == 1)].index].tolist(),"End Date": apple_adj_signals["Price"].loc[apple_adj_signals.loc[(apple_adj_signals["Signal"].shift(1) == "Buy") & (apple_adj_signals["Regime"].shift(1) == 1)].index].index}) #draw_candle.pandas_candlestick_ohlc(apple_adj, stick = 45, otherseries = ["20d", "50d", "200d"])##### take a simple analysis again # compute a rough profit (don't consider fee of the deal) rough_profit = apple_adj_long_profits['Profit'].sum() print(rough_profit)# compute the profit if we don't take any operation # (take long position at the first day and sale it on the last day of the date) no_operation_profit = apple['Adj Close'][-1]-apple['Adj Close'][0] print(no_operation_profit)tradeperiods = pd.DataFrame({"Start": apple_adj_long_profits.index,"End": apple_adj_long_profits["End Date"]})apple_adj_long_profits["Low"] = tradeperiods.apply(lambda x: min(apple_adj.loc[x["Start"]:x["End"], "Low"]), axis = 1) #print(apple_adj_long_profits)cash = 1000000 apple_backtest = pd.DataFrame({"Start Port. Value": [],"End Port. Value": [],"End Date": [],"Shares": [],"Share Price": [],"Trade Value": [],"Profit per Share": [],"Total Profit": [],"Stop-Loss Triggered": []}) port_value = 1 batch = 100 stoploss = .2 for index, row in apple_adj_long_profits.iterrows():# Maximum number of batches of stocks invested in# The arithmetic operator "//" represents an integer division that returns a maximum integer that is not greater than the resultbatches = np.floor(cash * port_value) // np.ceil(batch * row["Price"])trade_val = batches * batch * row["Price"]if row["Low"] < (1 - stoploss) * row["Price"]: # Account for the stop-loss#share_profit = np.round((1 - stoploss) * row["Price"], 2)share_profit = np.round(- stoploss * row["Price"], 2) # ??? I think this line need to be modified as left showsstop_trig = Trueelse:share_profit = row["Profit"]stop_trig = Falseprofit = share_profit * batches * batchapple_backtest = apple_backtest.append(pd.DataFrame({"Start Port. Value": cash,"End Port. Value": cash + profit,"End Date": row["End Date"],"Shares": batch * batches,"Share Price": row["Price"],"Trade Value": trade_val,"Profit per Share": share_profit,"Total Profit": profit,"Stop-Loss Triggered": stop_trig}, index = [index]))cash = max(0, cash + profit) print(apple_backtest) apple_backtest["End Port. Value"].plot() plt.show() 創(chuàng)作挑戰(zhàn)賽新人創(chuàng)作獎勵來咯，堅持創(chuàng)作打卡瓜分現金大獎

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