def share_table2fund(share_table):
"""
@summary converts data frame of shares into fund values
@param share_table: data frame containing shares on days transactions occured
@return fund : time series containing fund value over time
@return leverage : time series containing fund value over time
"""
# Get the data from the data store
dataobj = de.DataAccess('mysql')
startday = share_table.index[0]
endday = share_table.index[-1]
symbols = list(share_table.columns)
symbols.remove('_CASH')
# print symbols
# Get desired timestamps
timeofday = dt.timedelta(hours=16)
timestamps = du.getNYSEdays(startday - dt.timedelta(days=5),
endday + dt.timedelta(days=1), timeofday)
historic = dataobj.get_data(timestamps, symbols, ["close"])[0]
historic.fillna(method='ffill', inplace=True)
historic["_CASH"] = 1
closest = historic[historic.index <= share_table.index[0]].ix[:]
ts_leverage = pandas.Series(0, index=[closest.index[-1]])
# start shares/fund out as 100% cash
first_val = closest.ix[-1] * share_table.ix[0]
fund_ts = pandas.Series([first_val.sum(axis=1)], index=[closest.index[-1]])
prev_row = share_table.ix[0]
for row_index, row in share_table.iterrows():
# print row_index
trade_price = historic.ix[row_index:].ix[0:1]
trade_date = trade_price.index[0]
# print trade_date
# 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]
def calculate_efficiency(dt_start_date, dt_end_date, s_stock):
"""
@summary calculates the exit-entry/high-low trade efficiency of a stock from historical data
@param start_date: entry point for the trade
@param end_date: exit point for the trade
@param stock: stock to compute efficiency for
@return: float representing efficiency
"""
# Get the data from the data store
dataobj = de.DataAccess('mysql')
# Get desired timestamps
timeofday = dt.timedelta(hours=16)
timestamps = du.getNYSEdays(dt_start_date,
dt_end_date + dt.timedelta(days=1), timeofday)
historic = dataobj.get_data(timestamps, [s_stock], ["close"])[0]
# print "######"
# print historic
hi = numpy.max(historic.values)
low = numpy.min(historic.values)
entry = historic.values[0]
exit_price = historic.values[-1]
return (((exit_price - entry) / (hi - low))[0])
def get_data(ls_symbols, ls_keys):
'''
@summary: Gets a data chunk for backtesting
@param dt_start: Start time
@param dt_end: End time
@param ls_symbols: symbols to use
@note: More data will be pulled from before and after the limits to ensure
valid data on the start/enddates which requires lookback/forward
@return: data dictionry
'''
print("Getting Data from MySQL")
# Modify dates to ensure enough data for all features
dt_start = dt.datetime(2005, 1, 1)
dt_end = dt.datetime(2012, 8, 31)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours=16))
c_da = de.DataAccess('mysql')
ldf_data = c_da.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(list(zip(ls_keys, ldf_data)))
return d_data