python - Zipline - csv 文件

Question

我正在尝试弄清楚如何在 Zipline 中使用我自己的 csv 数据文件(最初来自雅虎财经)。我知道您需要将 csv 文件加载到 pandas 数据框中。但我似乎无法阻止 Zipline 从雅虎下载数据。

我的 csv 文件格式:

Date, Open, High, Low, Close, Volume, AdjClose

我的算法文件:

from zipline.api import order, record, symbol
import pandas as pd

data = pd.read_csv('AAPL.csv')

def initialize(context):
    pass

def handle_data(context, data):
    order(symbol('AAPL'), 10)
    record(AAPL=data[symbol('AAPL')].price)

我的命令行创建pickle文件:

run_algo.py -f E:\..\Main.py --start 2011-1-1 --end 2015-1-1 -o buyapple_out.pickle

命令行输出:

[2015-03-27 10:18:20.809959] WARNING: Loader: No benchmark data found for date range.
start_date=2015-03-27 00:00:00+00:00, end_date=2015-03-27 10:18:19.973911, url=http://ichart.finance.yahoo.com/table.csv?a=2&s=%5EGSPC&b=27&e=27&d=2&g
=d&f=2015&c=2015
[2015-03-27 10:20:05.811965] INFO: Performance: Simulated 504 trading days out of 504.
[2015-03-27 10:20:05.811965] INFO: Performance: first open: 2013-01-02 14:31:00+00:00
[2015-03-27 10:20:05.811965] INFO: Performance: last close: 2014-12-31 21:00:00+00:00

我的 pickle 文件已正确创建。但它似乎仍在使用雅虎而不是我的 csv，因为命令行输出谈论雅虎财经。 除了“将 csv 加载到数据框中”之外，Zipline 似乎没有关于如何执行此操作的文档。还有什么？

非常感谢。

Answer 1

我认为您的问题在于如何使用数据，而不是如何读取数据。这是使用布林带的示例策略。

import pandas as pd
import locale
import matplotlib.pyplot as plt
from zipline.algorithm import TradingAlgorithm
import zipline.finance.trading as trading
from zipline.utils.factory import load_from_yahoo
from datetime import datetime, date
from pytz import timezone
import pytz
from dateutil.tz import tzutc
from dateutil.parser import parse
from zipline.finance.slippage import FixedSlippage
from zipline.finance.commission import PerShare, PerTrade


central = timezone('US/Central')
HOLDTIME = 5
locale.setlocale( locale.LC_ALL, 'en_US.UTF-8')
COMMISSION=0.005

def date_utc(s):
    return parse(s, tzinfos=tzutc)


class BollingerBands(TradingAlgorithm):   

    def initialize(self):
        self.invested       = False

        self.trade_size=1000
        self.long = False
        self.short = False
        self.closed = False
        self.trading_day_counter = 0  
        self.pct_stop = 0.025
        self.long_stop_price =  0.0
        self.short_stop_price=0.0
        self.target=0.05
        commission_cost = self.trade_size * COMMISSION
        self.set_slippage(FixedSlippage(spread=0.10))  
        self.set_commission(PerTrade(cost=commission_cost))  

    def handle_data(self, data):


        if data['Close'].price >= data['upper'].price and not self.invested:
            self.order('Close', self.trade_size)
            self.long_stop_price =  data['Open'].price - data['Open'].price * float(self.pct_stop) 
            self.short_stop_price =  data['Open'].price + data['Open'].price * float(self.target)
            self.long = True
            self.closed=False
            self.invested = True
            self.trading_day_counter = 0  
        if data['Close'].price <= data['lower'].price and not self.invested:
            self.short_stop_price =  data['Open'].price + data['Open'].price * float(self.pct_stop)
            self.long_stop_price =  data['Open'].price - data['Open'].price * float(self.target) 
            self.order('Close', -self.trade_size)
            self.short = True
            self.closed=False
            self.invested = True
            self.trading_day_counter = 0  
        if self.invested and (data['Close'].price <=  self.long_stop_price or data['Close'].price >=  self.short_stop_price): #or self.trading_day_counter == HOLDTIME):

            if self.long: 
                self.order('Close', -self.trade_size)
             if self.short:
                self.order('Close', self.trade_size)

            self.closed=True
            self.long=False
            self.short=False
            self.invested=False


        self.trading_day_counter =  self.trading_day_counter +1
        self.record(Close=data['Close'].price,
                    upper=data['upper'].price,
                    lower=data['lower'].price,
                    long=self.long,
                    short=self.short,
                    holdtime=self.trading_day_counter,
                    closed_position=self.closed,
                    shares=self.trade_size)


if __name__ == '__main__':
    df=pd.read_csv('SPY.csv') # contains Date, Open, High, Low, Close, Volume


    df['avg'] = pd.rolling_mean(df['Close'], 21)
    df['std'] = pd.rolling_std(df['Close'], 21)
    df['upper'] = df['avg'] + 2 * df['std']
    df['lower'] = df['avg'] - 2 * df['std']
    df = df.dropna()

    # # # # init Strat Class
    Strategy = BollingerBands()
    # #print df

    # # # # # # Run Strategy
    results = Strategy.run(df)
    results['algorithm_returns'] = (1 + results.returns).cumprod()


    results.to_csv('output.csv')
    print results['algorithm_returns'].tail(1)[0]*100