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我试图弄清楚如何在 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)

我创建泡菜文件的命令行：

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

我的泡菜文件已正确创建。但它似乎仍在使用 yahoo 而不是我的 csv，因为命令行输出谈论的是 yahoo 财务。除了“将 csv 加载到数据框”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。

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1 回答 1

2

我相信您的问题是您如何使用数据，而不是您如何读取数据。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
于 2015-03-27T13:34:27.787 回答