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portfolio.py
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portfolio.py
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import datetime
import numpy as np
import pandas as pd
import Queue
import matplotlib.pyplot as plt
from abc import ABCMeta,abstractmethod
from math import floor
from event import FillEvent,OrderEvent
from performance import create_sharpe_ratio,create_drawdowns
class Portfolio(object):
__metaclass__=ABCMeta
@abstractmethod
def update_signal(self,event):
raise NotImplementedError("Should implement update_signal()")
@abstractmethod
def update_fill(self,event):
raise NotImplementedError("Should implement update_fill()")
class NaivePortfolio(Portfolio):
def __init__(self,bars,events,start_date,initial_capital=100000.0):
self.bars=bars
self.events=events
self.symbol_list=self.bars.symbol_list
self.start_date=start_date
self.initial_capital=initial_capital
self.all_positions=self.construct_all_positions()
self.current_positions=dict( (k,v) for k,v in [(s,0) for s in self.symbol_list] )
self.all_holdings=self.construct_all_holdings()
self.current_holdings=self.construct_current_holdings()
def construct_all_positions(self):
d=dict( (k,v) for k,v in [(s,0) for s in self.symbol_list] )
d['datetime']=self.start_date
return [d]
def construct_all_holdings(self):
d=dict( (k,v) for k,v in [(s,0) for s in self.symbol_list])
d['datetime']=self.start_date
d['cash']=self.initial_capital
d['commission']=0.0
d['total']=self.initial_capital
return [d]
def construct_current_holdings(self):
d=dict( (k,v) for k,v in [(s,0) for s in self.symbol_list])
d['cash']=self.initial_capital
d['commission']=0.0
return d
def update_timeindex(self,event):
bars={}
for sym in self.symbol_list:
bars[sym]=self.bars.get_latest_bars(sym,N=1)
dp=dict( (k,v) for k,v in [(s,0) for s in self.symbol_list])
dp['datetime']=bars[self.symbol_list[0]][0][1]
for s in self.symbol_list:
dp[s]=self.current_positions[s]
self.all_positions.append(dp)
dh=dict( (k,v) for k,v in [(s,0) for s in self.symbol_list])
dh['datetime']=bars[self.symbol_list[0]][0][1]
dh['cash']=self.current_holdings['cash']
dh['commission']=self.current_holdings['commission']
dh['total']=self.current_holdings['cash']
for s in self.symbol_list:
market_value=self.current_positions[s]*bars[s][0][7]
dh[s]=market_value
dh['total']+=market_value
self.all_holdings.append(dh)
def update_positions_from_fill(self,fill):
fill_dir=0
if fill.direction=='BUY':
fill_dir=1
if fill.direction=='SELL':
fill_dir=-1
self.current_positions[fill.symbol]+=fill_dir*fill.quantity
def update_holdings_from_fill(self,fill):
fill_dir=0
if fill.direction=='BUY':
fill_dir=1
if fill.direction=='SELL':
fill_dir=-1
fill_cost=self.bars.get_latest_bars(fill.symbol)[0][7]
cost=fill_dir*fill_cost*fill.quantity
self.current_holdings[fill.symbol]+=cost
self.current_holdings['commission']+=fill.commission
self.current_holdings['cash']-=(cost+fill.commission)
def update_fill(self,event):
if event.type=='FILL':
self.update_positions_from_fill(event)
self.update_holdings_from_fill(event)
def generate_naive_order(self,signal):
order=None
symbol=signal.symbol
direction=signal.signal_type
mkt_quantity=100
cur_quantity=self.current_positions[symbol]
order_type='MKT'
if direction=='LONG' and cur_quantity==0:
order=OrderEvent(symbol,order_type,mkt_quantity,'BUY')
if direction=='SHORT' and cur_quantity==0:
order=OrderEvent(symbol,order_type,mkt_quantity,'SELL')
if direction=='EXIT' and cur_quantity>0:
order=OrderEvent(symbol,order_type,abs(cur_quantity),'SELL')
if direction=='EXIT' and cur_quantity<0:
order=OrderEvent(symbol,order_type,abs(cur_quantity),'BUY')
return order
def update_signal(self,event):
if event.type=='SIGNAL':
order_event=self.generate_naive_order(event)
self.events.put(order_event)
def create_equity_curve_dataframe(self):
curve=pd.DataFrame(self.all_holdings)
curve.set_index('datetime',inplace=True)
curve['returns']=curve['total'].pct_change()
curve['equity_curve']=(1.0+curve['returns']).cumprod()
self.equity_curve=curve
def output_summary_stats(self):
total_return=self.equity_curve['equity_curve'][-1]
returns=self.equity_curve['returns']
pnl=self.equity_curve['equity_curve']
sharpe_ratio=create_sharpe_ratio(returns)
max_dd,dd_duration=create_drawdowns(pnl)
stats=[("Total Return","%0.2f%%" % ((total_return-1.0)*1000.0)),("Sharpe Ratio", "%0.2f" % sharpe_ratio),("Max Drawdown", "%0.2f%%" % (max_dd*100.0)),("Drawdown Duration", "%d" % dd_duration)]
plt.clf()
plt.plot(self.equity_curve.index,pnl)
plt.savefig('cumulative_return')
return stats