tradeNFLX.py

from zipline.api import schedule_function, date_rules, time_rules, order_target, record, symbol, set_benchmark
from zipline.pipeline import Pipeline
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.factors import AverageDollarVolume, RSI, BollingerBands
from zipline.finance.trading import TradingEnvironment
import math
import pandas
import matplotlib.pyplot as plt
import talib

def initialize(context):

    # Schedule our rebalance function to run at the start of
    # each week, when the market opens.
    schedule_function(
        func=my_rebalance,
        date_rule=date_rules.every_day(),
        time_rule=time_rules.market_open()
    )

def handle_data(context,data):
    
    # Data window
    window = 14
    
    # Get NFLX prices
    price = data.history(symbol('NFLX'), ['close'], window, '1d')
    latest_close = price['close'][-1]
    
    # Calculate RSI
    rsi = talib.RSI(price['close'][symbol('NFLX')], timeperiod=window)[-1]
    
    # Calculate Bollinger Bands
    bbHigh, bbMid, bbLow = talib.BBANDS(price['close'][symbol('NFLX')], timeperiod=window, nbdevup=2, nbdevdn=2, matype=0)
    
    rsiOverbought = rsi > 70
    rsiOversold = rsi < 30

    bbOverbought = latest_close > bbHigh
    bbOversold = latest_close < bbLow

    context.overbought = rsiOverbought & bbOverbought
    context.oversold = rsiOversold & bbOversold
    
def my_rebalance(context, data):
    """
    Rebalance daily.
    """

    if data.can_trade(symbol('NFLX')):
        if context.oversold:
            order_target_percent(symbol('NFLX'),1)
        elif context.overbought:
            order_target_percent(symbol('NFLX'),0)
	
def analyze(context, perf):
    #for item in perf.items():
        #print(item,"\n")

    fig = plt.figure()
    ax1 = fig.add_subplot(211)
    perf.excess_return.plot(ax=ax1)
    perf.benchmark_period_return.plot(ax=ax1)
    ax1.set_ylabel('returns')
    plt.legend(loc=0, labels=['Algorithm', 'Benchmark'])
    
    ax2 = fig.add_subplot(212)
    perf.portfolio_value.plot(ax=ax2)
    plt.ylabel('portfolio value in $')
    plt.show()