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dailyRun.py
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dailyRun.py
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import precommons_updates
import commons
import mydata_get_delta
import csv
import datetime as dt
import time
import uuid
from market import stock_market
from database import db
from forecasts import forecast
from portfolio import investments
def readPortfolio():
globals portfolio,portfolioValue,cash
with open(daily_path+'currentPortfolio.csv','r') as csvfile:
csvreader=csv.reader(csvfile, delimiter=',')
lI=0
for k,v in commons.getIndexCodes().items():
index_t=v[-8:]
portfolio[index_t]=dict()
for row in csvreader:
if lI>0:
if dt.datetime.strptime(row[0],'%m/%d/%Y')==commons.idx_today:
if row[2]=='PORTFOLIOCASH':
cash[row[1]]=int(row[3])
elif row[2]=='PORTFOLIOVALUE':
portfolioValue[row[1]]=int(row[3])
else:
portfolio[row[1]][row[2]]=int(row[3])
else:
raise 'currentPortfolioWrongDate'
iI+=1
csvfile.close()
def setRefDates():
globals refDix,refDate,tradeDate,tradeDix
refDix=commons.date_index_internal[commons.getClosestDate(commons.idx_today)]
refDate=commons.date_index_external[refDix]
tradeDate=commons.getNextTradeDay(refDate)
tradeDix=commons.date_index_internal[tradeDate]
def trainIfNeeded():
globals f,refDix,train_uuid,m,dba
if refDix%20==0:
start=time.time()
train_uuid=uuid.uuid1().hex
#train_uuid='b988552ec64f11e69128c82a142bddcf'
print 'Retraining the models. Date:',refDate,'training guid:',train_uuid
newTraining=cl_trainSection(refDix-1,train_uuid,scenario,True)
newTraining.train()
end=time.time()
print 'Training took',end-start,'seconds.'
else:
maxDix=0
for row in dba.t_train_h:
if row['enddix']>maxDix:
maxDix=row['enddix']
train_uuid=row['train_uuid']
f=forecast(m,train_uuid)
def initializePortfolioAndMarket():
globals m,p,dba,sim_uuid
#initialize portfolio & market
sim_uuid=uuid.uuid1().hex
dba=db(sim_uuid,'r+')
m=stock_market(dba,0,refDix,False,portfolioValue)
p=investments(0,m,refDix,dba,sim_uuid,portfolio,cash)
#log recommendation
def logRecommendation(tradeDate,symbol,tradeTx,tradeVol,tradePrice,tradeDateCopy,_12dd):
with open(commons.daily_path+'planedTrades.csv','r+') as csvfile:
csvwriter = csv.writer(csvfile, delimiter=',')
csvwriter.writerow(list([tradeDate,symbol,tradeTx,tradeVol,tradePrice,tradeDateCopy,_12dd]))
csvfile.close()
#daily run
def playForADay(idx_external,sim_uuid,train_uuid,scenario):
global f,refDix,refDate,tradeDix,tradeDate
temperature=1.5
state=dict()
proposed_action=dict()
order_entry=dict()
order_untrained=dict()
reward=dict()
action=dict()
dba.db_main.flush()
index_t=commons.getIndexCodes()[idx_external][-8:]
sp500_ticker=commons.getHistSp500Ticker(commons.date_index_external[refDix])
for ticker in commons.getHistSp500Composition(commons.date_index_external[refDix])[index_t]:
reward[ticker]=9999
if f.trained(train_uuid,ticker):
state[ticker]=f.get_forecast_state(ticker,refDix)
proposed_action[ticker]=dba.get_softmax_action(ticker,state[ticker],temperature,scenario)
#sell
if proposed_action[ticker]==commons.action_code['sell']:
vol=p.portfolio[index_t][ticker]
forecastPrice=f.get_order_price(ticker,state[ticker],refDix,proposed_action[ticker],\
m.get_closing_price(ticker,refDix))
x=p.execute_order(ticker,vol,dix,forecast_price,proposed_action[ticker],\
m.get_closing_price(ticker,dix),state[ticker]['12dd_Close'],False)
logRecommendation(tradeDate,ticker,'sell',vol,forecastPrice,tradeDate,state[ticker]['12dd_Close'])
#buy, but only after everythin has been sold
if proposed_action[ticker]==commons.action_code['buy']:
order_entry[ticker]=0
else: #for the tickers that are not trained yet align with the index
action[ticker]=f.getActionUntrained(p,index_t,ticker,refDix)
if action[ticker][0]==commons.action_code['buy']:
order_untrained[ticker]=0
elif action[ticker][0]==commons.action_code['sell']:
x=p.execute_order(ticker,action[ticker][1],tradeDix,m.get_closing_price(ticker,refDix),\
commons.action_code['sell'],m.get_closing_price(ticker,refDix),0,False)
logRecommendation(tradeDate,ticker,'sellOpen',action[ticker][1],0,tradeDate,0)
#allocate for alignment
for ticker,opening_price in order_untrained.items():
x=p.execute_order(ticker,action[ticker][1],tradeDix,m.get_closing_price(ticker,refDix),\
commons.action_code['buy'],m.get_closing_price(ticker,refDix),0,False)
logRecommendation(tradeDate,ticker,'buyOpen',action[ticker][1],0,tradeDate,0)
budget=dict()
for k,v in commons.getIndexCodes().items():
index_t=v[-8:]
budget[index_t]=p.cash[index_t]
for ticker,price in order_entry.items():
index_t=sp500_ticker[ticker]
budget[index_t]+=p.portfolio[index_t][ticker]*m.get_closing_price(ticker,refDix)
#order book; realign the portfolio to the index according to buying recommendations
orderBook=p.get_portfolio_alignment(budget,order_entry,refDix)
for ticker,volume in orderBook.items():
if volume<0: #selling what we have too much of
forecast_price=f.get_order_price(ticker,state[ticker],refDix,commons.action_code['sell'],\
m.get_closing_price(ticker,refDix))
x=p.execute_order(ticker,0-volume,tradeDix,forecast_price,commons.action_code['sell'],\
m.get_closing_price(ticker,refDix),state[ticker]['12dd_Close'],False)
logRecommendation(tradeDate,ticker,'sell',volume,forecast_price,tradeDate,state['ticker']['12dd_Close'])
for ticker,volume in orderBook.items():
if volume>0:
if commons.data_sp500_1st_date[ticker]<=refDate:
forecast_price=f.get_order_price(ticker,state[ticker],refDix,commons.action_code['buy'],\
m.get_closing_price(ticker,refDix))
x=p.execute_order(ticker,volume,tradeDix,forecast_price,commons.action_code['buy'],\
m.get_closing_price(ticker,refDix),state[ticker]['12dd_Close'],False)
logRecommendation(tradeDate,ticker,'buy',volume,forecast_price,tradeDate,state['ticker']['12dd_Close'])
#EXECUTE
portfolio=dict()
portfolioValue=dict()
cash=dict()
scenario='best'
readPortfolio()
setRefDates()
initializePortfolioAndMarket()
trainIfNeeded()
for k,v in commons.getIndexCodes().items():
playForADay(k,sim_uuid,train_uuid,scenario)