# get stock prices on all the days up until this trade
to_calculate = historic[(historic.index <= trade_date) & (historic.index > fund_ts.index[-1])]
# multiply prices by our current shares
values_by_stock = to_calculate * prev_row
# for date, sym in values_by_stock.iteritems():
# print date,sym
# print values_by_stock
prev_row = row
#update leverage
ts_leverage = _calculate_leverage(values_by_stock, ts_leverage)
# calculate total value and append to our fund history
fund_ts = fund_ts.append([values_by_stock.sum(axis=1)])
return [fund_ts, ts_leverage]
if __name__ == "__main__":
filename = "Strat.csv"
plot_name = "Strategy"
print "load csv"
[share_table, commissions, i_start_cash] = csv2fund(filename)
print share_table
[fund_ts, ts_leverage] = share_table2fund(share_table)
print "print report"
print fund_ts
report.print_stats(fund_ts, ["SPY"], plot_name, directory="./" + plot_name, commissions=commissions, i_start_cash=i_start_cash)
print "analyze transactions"
#Generate new plot based off transactions alone
analyze_transactions(filename, plot_name, share_table, True)
print "done"
5,
0.02,
log="transactions2.csv")
df_goog = pand.DataFrame(index=df_funds.index,
data=df_funds.values,
columns=["FUND"])
ls_hedge_params={"Weight of Hedge" : 0.5, \
"Target Beta" : 1,\
"Type of Hedge" : "Long and Short",\
"Rebalancing" : "Weekly",\
"Number of equities" : 30, \
"Lookback period" : "365 days"}
report.print_stats(
df_aapl, ["$SPX", "MSFT"],
"AAPL",
s_fund_name="Test Portfolio",
original=df_goog,
s_original_name="UnHedged",
d_trading_params={
"Number of trades": 200,
"Trading Period": "Weekly"
},
d_hedge_params=ls_hedge_params,
s_comments="This is a sample report generated by tutorial 5.",
commissions=f_commision,
slippage=f_slippage,
directory="./AAPL/")
#print "Analyzing Transactions..."
#csv2fund.analyze_transactions("transactions.csv","AAPL")
print "Done"
# for date, sym in values_by_stock.iteritems():
# print date,sym
# print values_by_stock
prev_row = row
#update leverage
ts_leverage = _calculate_leverage(values_by_stock, ts_leverage)
# calculate total value and append to our fund history
fund_ts = fund_ts.append([values_by_stock.sum(axis=1)])
return [fund_ts, ts_leverage]
if __name__ == "__main__":
filename = "Strat.csv"
plot_name = "Strategy"
print("load csv")
[share_table, commissions, i_start_cash] = csv2fund(filename)
print(share_table)
[fund_ts, ts_leverage] = share_table2fund(share_table)
print("print report")
print(fund_ts)
report.print_stats(fund_ts, ["SPY"],
plot_name,
directory="./" + plot_name,
commissions=commissions,
i_start_cash=i_start_cash)
print("analyze transactions")
#Generate new plot based off transactions alone
analyze_transactions(filename, plot_name, share_table, True)
print("done")
ldt_timestamps = du.getNYSEdays(startday, endday, timeofday)
dataobj = da.DataAccess('Yahoo')
df_close = dataobj.get_data( \
ldt_timestamps, l_symbols, "close", verbose=False)
df_alloc = pand.DataFrame(index=[dt.datetime(2008, 2, 1)], data=[[0.5,0.5]], columns=l_symbols)
for day in ldt_timestamps:
randomfloat=rgen.random()
df_alloc = df_alloc.append( \
pand.DataFrame(index=[day], \
data=[[randomfloat, 1-randomfloat]], columns=l_symbols))
df_alloc['_CASH'] = 0
''' Tests tradesim buy-on-open functionality '''
(df_funds, ts_leverage, f_commision, f_slippage) = qstksim.tradesim( df_alloc, df_close, 1000000, 1, True, 0.02, 5, 0.02, log="transactions2.csv")
df_goog = pand.DataFrame(index=df_funds.index, data=df_funds.values, columns=["FUND"])
ls_hedge_params={"Weight of Hedge" : 0.5, \
"Target Beta" : 1,\
"Type of Hedge" : "Long and Short",\
"Rebalancing" : "Weekly",\
"Number of equities" : 30, \
"Lookback period" : "365 days"}
report.print_stats(df_aapl, ["$SPX","MSFT"], "AAPL", s_fund_name="Test Portfolio", original=df_goog, s_original_name="UnHedged", d_trading_params={"Number of trades":200,"Trading Period":"Weekly"},d_hedge_params=ls_hedge_params, s_comments="This is a sample report generated by tutorial 5.", commissions=f_commision, slippage=f_slippage, directory="./AAPL/")
#print "Analyzing Transactions..."
#csv2fund.analyze_transactions("transactions.csv","AAPL")
print "Done"
