bokeh_server.py

from __future__ import (
    absolute_import, division, print_function,unicode_literals
)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])
import backtrader as bt
import pandas as pd

from backtrader_plotting import Bokeh,OptBrowser
from backtrader_plotting.schemes import Tradimo

from backtrader.analyzers import (SQN, AnnualReturn, TimeReturn, SharpeRatio,
                                  TradeAnalyzer)

# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('map_period', 10),
        ('printlog', False),
    )


    def log(self, txt, dt=None, doprint=False):
        ''' Logging function for this strategy'''
        if self.params.printlog or doprint:
            dt = dt or self.datas[0].datetime.date(0)
            print('%s, %s' % (dt.isoformat(), txt))



    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.data_close = self.datas[0].close

        # To keep track of pending orders
        self.order = None
        self.buy_price = None
        self.buy_comm = None
        self.bar_executed = 0

        # Add a MovingAverageSimple indicator
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.map_period)

        # Indicators for the plotting show
        bt.indicators.ExponentialMovingAverage(self.datas[0], period=25)
        bt.indicators.WeightedMovingAverage(self.datas[0], period=25, subplot=True)
        bt.indicators.StochasticSlow(self.datas[0])
        bt.indicators.MACDHisto(self.datas[0])

        rsi = bt.indicators.RSI(self.datas[0])
        bt.indicators.SmoothedMovingAverage(rsi, period=10)
        bt.indicators.ATR(self.datas[0], plot=False)

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log('BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))
                self.buy_price = order.executed.price
                self.buy_comm = order.executed.comm
            else: # Sell
                self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                         (order.executed.price,
                          order.executed.value,
                          order.executed.comm))

            self.bar_executed = len(self)

        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        # Write down: no pending order
        self.order = None


    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
                 (trade.pnl, trade.pnlcomm))


    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.data_close[0])

        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return

        # Check if we are in the market
        if not self.position:
            # Not yet ... we MIGHT BUY if ...
            if self.data_close[0] > self.sma[0]:
                # BUY, BUY, BUY!!! (with all possible default parameters)
                self.log('BUY CREATE, %.2f' % self.data_close[0])
                # Keep track of the created order to avoid a 2nd order
                self.order = self.buy()
        else:
            if self.data_close[0] < self.sma[0]:
                # SELL, SELL, SELL!!! (with all possible default parameters)
                self.log('SELL CREATE, %.2f' % self.data_close[0])
                # Keep track of the created order to avoid a 2nd order
                self.order = self.sell()


    def stop(self):
        self.log('(MA Period %2d) Ending Value %.2f' %
                 (self.params.map_period, self.broker.getvalue()), doprint=True)


if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    # strats = cerebro.addstrategy(TestStrategy)
    # strats = cerebro.addstrategy(BollingerBifrostStrategy)
    # optimize the strategy
    strats = cerebro.optstrategy(TestStrategy, map_period=range(10, 31))

    cerebro.addanalyzer(AnnualReturn, _name='annual_return')
    cerebro.addanalyzer(SQN, _name='sqn')
    # cerebro.addanalyzer(TimeReturn, _name='time_return')
    cerebro.addanalyzer(SharpeRatio, _name='sharpe', timeframe=bt.TimeFrame.Days)
    cerebro.addanalyzer(TradeAnalyzer, _name='trade_analyzer')

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere
    modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
    datapath = os.path.join(modpath, 'datas/orcl-1995-2014.txt')

    # Create a Data Feed
    data = bt.feeds.YahooFinanceCSVData(
        dataname=datapath,
        # Do not pass values before this date
        fromdate=datetime.datetime(2000, 1, 1),
        # Do not pass values after this date
        todate=datetime.datetime(2000, 12, 31),
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(1000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize, stake=10)

    # Set the Commission - 0.1% ... divide by 100 to remove the %
    cerebro.broker.setcommission(commission=0.001)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    results = cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # bo = Bokeh(style='bar', plot_mode='single', scheme=Tradimo(), output_mode='show') # tabs = 'multi',
    # cerebro.plot(bo)

    b = Bokeh(style='bar', scheme=Tradimo())
    browser = OptBrowser(b, results)
    browser.start()


    # # Extract data into a list
    # data_list = []
    # for i in range(len(data.datetime.array)):
    #     data_list.append([
    #         data.datetime.date(-i),
    #         data.open[-i],
    #         data.high[-i],
    #         data.low[-i],
    #         data.close[-i],
    #         data.volume[-i],
    #         data.adjclose[-i]
    #     ])
    #
    # # Convert the list to a DataFrame
    # df = pd.DataFrame(data_list, columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'])
    #
    # # Display the first few rows of the DataFrame
    # print(df.head())