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loaddata.py
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loaddata.py
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#!/usr/bin/env python
# DATA,UTIL
import sqlite3 as lite
import pandas.io.sql as psql
from calc import *
UNIV_BASE_DIR = ""
SECDATA_BASE_DIR = ""
PRICE_BASE_DIR = ""
BARRA_BASE_DIR = ""
BAR_BASE_DIR = ""
EARNINGS_BASE_DIR = ""
LOCATES_BASE_DIR = ""
# ESTIMATES_BASE_DIR = ""
ESTIMATES_BASE_DIR = ""
# low price and high price to remove outliers
t_low_price = 2.0
t_high_price = 500.0
t_min_advp = 1000000.0
e_low_price = 2.25
e_high_price = 500.0
e_min_advp = 5000000.0
from_timestamp = lambda x: datetime.fromtimestamp(int(x) / 1000.)
def get_uni(start, end, lookback, uni_size=1400):
# get the universe list as combination of symbols and sector names
unidate = start - timedelta(days=lookback)
year = unidate.strftime("%Y")
unidate = unidate.strftime("%Y%m%d")
univ_dir = UNIV_BASE_DIR + year
univ_file = univ_dir + "/" + unidate + ".csv"
univ_df = pd.read_csv(univ_file, header=0, usecols=['sid', 'ticker_root', 'status', 'country', 'currency'],
index_col=['sid'])
print("Universe size (raw): {}".format(len(univ_df.index)))
# contains price data for given time frame in US
univ_df = univ_df[(univ_df['country'] == "USA") & (univ_df['currency'] == "USD")]
print("Universe size (US/USD): {}".format(len(univ_df.index)))
secdata_dir = SECDATA_BASE_DIR + year
secdata_file = secdata_dir + "/" + unidate + ".csv.gz"
# company data
secdata_df = pd.read_csv(secdata_file, header=0, compression='gzip',
usecols=['sid', 'symbol', 'sector', 'ind', 'group', 'sector_name', 'ind_name',
'estu_inter', 'estu_barra4s'], index_col=['sid'])
univ_df = pd.merge(univ_df, secdata_df, how='inner', left_index=True, right_index=True, sort=True)
print("Universe size (secdata): {}".format(len(univ_df.index)))
univ_df = univ_df[(univ_df['estu_barra4s'] == 1) | (univ_df['ind'] == 404020)]
del univ_df['estu_inter']
del univ_df['estu_barra4s']
print("Universe size (estu_inter): {}".format(len(univ_df.index)))
univ_df = univ_df[univ_df['group'] != 3520]
print("Universe size (bio): {}".format(len(univ_df.index)))
price_dir = PRICE_BASE_DIR + year
price_file = price_dir + "/" + unidate + ".csv"
price_df = pd.read_csv(price_file, header=0,
usecols=['sid', 'ticker', 'close', 'tradable_med_volume_21', 'mkt_cap'], index_col=['sid'])
univ_df = pd.merge(univ_df, price_df, how='inner', left_index=True, right_index=True, sort=True)
print("Universe size (prices): {}".format(len(univ_df.index)))
univ_df = univ_df[(univ_df['close'] > t_low_price) & (univ_df['close'] < t_high_price)]
print("Universe size (price range): {}".format(len(univ_df.index)))
univ_df['mdvp'] = univ_df['tradable_med_volume_21'] * univ_df['close']
univ_df = univ_df[univ_df['mdvp'] > t_min_advp]
print("Universe size (adv): {}".format(len(univ_df.index)))
univ_df['rank'] = univ_df['mkt_cap'].fillna(0).rank(ascending=False)
univ_df = univ_df[univ_df['rank'] <= uni_size]
print("Universe size (mktcap): {}".format(len(univ_df.index)))
return univ_df[['symbol', 'sector_name']]
# univ_df = univ_df[['ticker_root']]
# date = start
# result_df = pd.DataFrame()
# while( date < end ):
# dateStr = date.strftime("%Y%m%d")
# year = date.strftime("%Y")
# secdata_dir = SECDATA_BASE_DIR + year
# secdata_file = secdata_dir + "/" + dateStr + ".estu.csv.gz"
# try:
# secdata_df = pd.read_csv(secdata_file, header=0, compression='gzip', usecols=['sid', 'symbol', 'sector', 'ind', 'sector_name', 'ind_name'])
# except IOError:
# print "File not found: {}".format(secdata_file)
# date += timedelta(days=1)
# continue
# secdata_df.set_index('sid', inplace=True)
# secdata_df = pd.merge(univ_df, secdata_df, how='inner', left_index=True, right_index=True, sort=True, suffixes=['', '_dead'])
# secdata_df['ticker'] = secdata_df['symbol']
# del secdata_df['symbol']
# secdata_df['date'] = date
# secdata_df.reset_index(inplace=True)
# result_df = result_df.append(secdata_df)
# date += timedelta(days=1)
# result_df.set_index(keys=['date', 'sid'], inplace=True)
# result_df = result_df.unstack().asfreq('D', method='pad').stack()
# result_df.index.names = ['date', 'sid']
# return result_df
def load_barra(uni_df, start, end, cols=None):
if cols is not None:
cols.append('sid')
# if cols is None:
# cols = ['hbeta', 'pbeta', 'srisk_pct', 'trisk_pct', 'country', 'growth', 'size', 'sizenl', 'divyild', 'btop', 'earnyild', 'beta', 'resvol', 'betanl', 'momentum', 'leverage', 'liquidty', 'yld_pct', 'indname1', 'ind1', 'sid']
date = start
result_dfs = list()
while date < end:
dateStr = date.strftime('%Y%m%d')
year = dateStr[0:4]
barra_dir = BARRA_BASE_DIR + year
barra_file = barra_dir + "/" + dateStr + ".use4s_rsk.rev.csv"
print
"Loading {}".format(barra_file)
try:
if cols is not None:
barra_df = pd.read_csv(barra_file, usecols=cols)
else:
barra_df = pd.read_csv(barra_file)
except IOError:
print
"File not found: {}".format(barra_file)
date += timedelta(days=1)
continue
barra_df['date'] = date
# should be left join, but the nans create an issue downstream with unstacking...
barra_df = pd.merge(uni_df.reset_index(), barra_df, how='inner', left_on=['sid'], right_on=['sid'], sort=False)
barra_df.set_index(keys=['date', 'sid'], inplace=True)
result_dfs.append(barra_df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs, verify_integrity=True)
result_df = remove_dup_cols(result_df)
return result_df
def load_daybars(uni_df, start, end, cols=None, freq='30Min'):
if cols is None:
cols = ['dopen', 'dhigh', 'dlow', 'qhigh', 'qlow', 'dvwap', 'dvolume', 'dtrades']
cols.extend(['ticker', 'iclose_ts', 'iclose', 'dvolume', 'dopen'])
date = start
result_dfs = list()
uni_df = uni_df[['ticker']]
uni_df = uni_df.reset_index()
while (date < end):
dateStr = date.strftime("%Y%m%d")
bar_file = BAR_BASE_DIR + dateStr + "/daily.txt.gz"
print
"Loading {}".format(bar_file)
try:
bars_df = pd.read_csv(bar_file, compression='gzip', sep="|", header=None,
names=['ticker', 'iclose_ts', 'dopen', 'dhigh', 'dlow', 'iclose', 'qhigh', 'qlow',
'dvwap', 'dvolume', 'dtrades', 'open_c', 'open_c_volume', 'close_c',
'close_c_volume'], index_col=['iclose_ts'],
converters={'iclose_ts': from_timestamp}, usecols=cols)
except IOError:
print
"File not found: {}".format(bar_file)
date += timedelta(days=1)
continue
bars_df = bars_df.between_time('09:30:00', '16:00:01')
bars_df.set_index('ticker', append=True, inplace=True)
# change closed to 'left' maybe?
bars_df = bars_df.unstack(level=-1).resample(freq, how='last', closed='right', label='right').stack()
bars_df.reset_index(inplace=True)
bars_df['date'] = date
bars_df['giclose_ts'] = bars_df['iclose_ts']
bars_df = pd.merge(uni_df[uni_df['date'] == date], bars_df, how='inner', left_on=['date', 'ticker'],
right_on=['date', 'ticker'], sort=True, suffixes=['', '_dead'])
bars_df['gsid'] = bars_df['sid']
result_dfs.append(bars_df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs)
result_df.set_index(keys=['iclose_ts', 'sid'], inplace=True)
result_df = remove_dup_cols(result_df)
return result_df
def load_prices(uni_df, start, end, cols):
if cols is None:
cols = ['open', 'high', 'low', 'prim_volume', 'comp_volume', 'shares_out', 'ret', 'log_ret',
'prim_med_volume_21', 'prim_med_volume_60', 'comp_med_volume_21', 'comp_med_volume_60',
'tradable_med_volume_60', 'volat_21', 'volat_60', 'cum_log_ret', 'overnight_log_ret', 'today_log_ret',
'overnight_volat_21', 'today_volat_21', 'split', 'div']
cols.extend(['sid', 'ticker', 'close', 'tradable_med_volume_21', 'log_ret', 'tradable_volume', 'mkt_cap'])
date = start
result_dfs = list()
while (date < end):
dateStr = date.strftime('%Y%m%d')
year = dateStr[0:4]
price_dir = PRICE_BASE_DIR + year
price_file = price_dir + "/" + dateStr + ".equ_prices.rev.csv"
print
"Loading {}".format(price_file)
try:
prices_df = pd.read_csv(price_file, header=0, usecols=cols)
except IOError:
print
"File not found: {}".format(price_file)
date += timedelta(days=1)
continue
prices_df['date'] = prices_df['gdate'] = date
# print uni_df.head()
prices_df = pd.merge(uni_df.reset_index(), prices_df.reset_index(), how='inner', left_on=['sid'],
right_on=['sid'], sort=True, suffixes=['', '_dead'])
prices_df.set_index(keys=['date', 'sid'], inplace=True)
result_dfs.append(prices_df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs, verify_integrity=True)
result_df['tradable_med_volume_21'] = result_df['tradable_med_volume_21'].astype(float)
if 'shares_out' in result_df.columns:
result_df['shares_out'] = result_df['shares_out'].astype(float)
result_df['tradable_volume'] = result_df['tradable_volume'].astype(float)
if 'comp_voume' in result_df.columns:
result_df['comp_volume'] = result_df['comp_volume'].astype(float)
result_df['mdvp'] = result_df['close'] * result_df['tradable_med_volume_21']
result_df = lag_data(result_df)
result_df['expandable'] = (result_df['close_y'] > e_low_price) & (result_df['close_y'] < e_high_price) & (
result_df['mdvp_y'] > e_min_advp)
for col in ['ind_name_y', 'name_y', 'sector_name_y', 'sector_y', 'ticker_y', 'gdate_y', 'ticker_root_y']:
if col in result_df.columns:
del result_df[col]
return result_df
def load_volume_profile(uni_df, start, end, freq='30Min'):
date = start
result_dfs = list()
while (date < end):
dateStr = date.strftime('%Y%m%d')
year = dateStr[0:4]
price_dir = PRICE_BASE_DIR + year
volume_file = price_dir + "/" + dateStr + ".equ_volume_profiles_20d.rev.csv"
print
"Loading {}".format(volume_file)
try:
volume_df = pd.read_csv(volume_file, header=0, index_col=['sid'])
except IOError:
print
"File not found: {}".format(volume_file)
date += timedelta(days=1)
continue
print
"stacking..."
volume_df = volume_df.stack()
volume_df = volume_df.reset_index()
volume_df = volume_df[
(volume_df['level_1'] != 'med_open_volume') & (volume_df['level_1'] != 'med_close_volume') & (
volume_df['level_1'] != 'med_cum_pre_mkt_volume') & (
volume_df['level_1'] != 'med_cum_post_mkt_volume')]
timemap = dict()
print
"parsing dates..."
for rawtime in volume_df['level_1'].unique():
val = None
try:
val = dateparser.parse(dateStr + " " + rawtime[:-2] + ":" + rawtime[-2:])
except:
pass
timemap[rawtime] = val
print
"mapping dates..."
volume_df['iclose_ts'] = volume_df['level_1'].apply(lambda x: timemap[x])
volume_df['date'] = date
volume_df.set_index(keys=['date', 'sid'], inplace=True)
print
"merging..."
volume_df = pd.merge(uni_df, volume_df, how='inner', left_index=True, right_index=True, sort=True,
suffixes=['', '_dead'])
volume_df.reset_index(inplace=True)
grouped = volume_df.groupby('sid')
print
"accumulating volumes..."
for name, group in grouped:
group['med_cum_volume'] = pd.expanding_sum(group[0])
del group[0]
group['sid'] = name
group = group.reset_index()
# print group.head()
group.set_index('iclose_ts', inplace=True)
group_df = group.resample(freq, how='last', closed='right', label='right')
# print group_df.head()
result_dfs.append(group_df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs)
result_df = result_df.reset_index()
print
result_df.head()
result_df['iclose_ts'] = result_df['level_0']
del result_df['level_0']
result_df.set_index(keys=['iclose_ts', 'sid'], inplace=True)
result_df = remove_dup_cols(result_df)
return result_df
def aggregate_bars(bars_df, freq=30):
print
"Aggregating bars..."
# ASSUMES SORTED
start = bars_df['bopen_ts'].min()
t0 = start
t1 = t0 + timedelta(minutes=freq)
end = bars_df.index.max()
agg_bars_dfs = list()
while (t1 <= end):
print
"Grouping to {}".format(t1)
sub_df = bars_df.truncate(after=t1)
grouped = sub_df.groupby('ticker')
df_d = dict()
df_d['bopen_ts'] = start
df_d['iclose_ts'] = t1
df_d['bopen'] = grouped['bopen'].first()
df_d['bfirst'] = grouped['bfirst'].first()
df_d['bigh'] = grouped['bhigh'].max()
df_d['blow'] = grouped['blow'].min()
df_d['blast'] = grouped['blast'].last()
df_d['iclose'] = grouped['iclose'].last()
df_d['bvwap'] = grouped.apply(lambda x: (x['bvwap'] * x['bvolume'] / x['bvolume'].sum()).sum())
df_d['bvolume'] = grouped['bvolume'].sum()
df_d['btrades'] = grouped['btrades'].sum()
df_d['meanSpread'] = grouped['meanSpread'].mean()
df_d['meanEffectiveSpread'] = grouped['meanEffectiveSpread'].mean()
df_d['meanBidSize'] = grouped['meanBidSize'].mean()
df_d['meanAskSize'] = grouped['meanAskSize'].mean()
df_d['bidHitTrades'] = grouped['bidHitTrades'].sum()
df_d['midHitTrades'] = grouped['midHitTrades'].sum()
df_d['askHitTrades'] = grouped['askHitTrades'].sum()
df_d['bidHitDollars'] = grouped['bidHitDollars'].sum()
df_d['midHitDollars'] = grouped['midHitDollars'].sum()
df_d['askHitDollars'] = grouped['askHitDollars'].sum()
df_d['outsideTrades'] = grouped['outsideTrades'].sum()
df_d['outsideDollars'] = grouped['outsideDollars'].sum()
sub_df = bars_df.truncate(before=t0, after=t1)
grouped = sub_df.groupby('ticker')
df_d['bopen_b'] = grouped['bopen'].first()
df_d['bfirst_b'] = grouped['bfirst'].first()
df_d['bigh_b'] = grouped['bhigh'].max()
df_d['blow_b'] = grouped['blow'].min()
df_d['blast_b'] = grouped['blast'].last()
df_d['iclose_b'] = grouped['iclose'].last()
df_d['bvwap_b'] = grouped.apply(lambda x: (x['bvwap'] * x['bvolume'] / x['bvolume'].sum()).sum())
df_d['bvolume_b'] = grouped['bvolume'].sum()
df_d['btrades_b'] = grouped['btrades'].sum()
df_d['meanSpread_b'] = grouped['meanSpread'].mean()
df_d['meanEffectiveSpread_b'] = grouped['meanEffectiveSpread'].mean()
df_d['meanBidSize_b'] = grouped['meanBidSize'].mean()
df_d['meanAskSize_b'] = grouped['meanAskSize'].mean()
df_d['bidHitTrades_b'] = grouped['bidHitTrades'].sum()
df_d['midHitTrades_b'] = grouped['midHitTrades'].sum()
df_d['askHitTrades_b'] = grouped['askHitTrades'].sum()
df_d['bidHitDollars_b'] = grouped['bidHitDollars'].sum()
df_d['midHitDollars_b'] = grouped['midHitDollars'].sum()
df_d['askHitDollars_b'] = grouped['askHitDollars'].sum()
df_d['outsideTrades_b'] = grouped['outsideTrades'].sum()
df_d['outsideDollars_b'] = grouped['outsideDollars'].sum()
df = pd.DataFrame(df_d)
df = df.reset_index()
agg_bars_dfs.append(df)
t0 = t1
t1 += timedelta(minutes=freq)
agg_bars_df = pd.concat(agg_bars_dfs)
agg_bars_df['insideness'] = np.log(agg_bars_df['meanEffectiveSpread'] / agg_bars_df['meanSpread'])
agg_bars_df['adj_trade_size'] = agg_bars_df['bvolume'] / agg_bars_df['btrades'] / agg_bars_df['bvwap']
agg_bars_df['spread_bps'] = agg_bars_df['meanSpread'] / agg_bars_df['bvwap']
agg_bars_df['insideness_b'] = np.log(agg_bars_df['meanEffectiveSpread_b'] / agg_bars_df['meanSpread_b'])
agg_bars_df['adj_trade_size_b'] = agg_bars_df['bvolume_b'] / agg_bars_df['btrades_b'] / agg_bars_df['bvwap_b']
agg_bars_df['spread_bps_b'] = agg_bars_df['meanSpread_b'] / agg_bars_df['bvwap_b']
return agg_bars_df
def load_bars(uni_df, start, end, cols=None, freq=30):
if cols is not None:
cols.extend(['ticker', 'iclose_ts', 'iclose', 'date', 'gdate', 'giclose_ts'])
date = start
result_dfs = list()
uni_df = uni_df.reset_index()
while (date < end):
dateStr = date.strftime("%Y%m%d")
bar_file = BAR_BASE_DIR + dateStr + "/bars.txt.gz"
print
"Loading {}".format(bar_file)
try:
bars_df = pd.read_csv(bar_file, compression='gzip', sep="|", header=None,
names=['ticker', 'bopen_ts', 'iclose_ts', 'bopen', 'bfirst', 'bhigh', 'blow', 'blast',
'iclose', 'bvwap', 'bvolume', 'btrades', 'meanSpread', 'meanEffectiveSpread',
'meanBidSize', 'meanAskSize', 'bidHitTrades', 'midHitTrades', 'askHitTrades',
'bidHitDollars', 'midHitDollars', 'askHitDollars', 'outsideTrades',
'outsideDollars'],
converters={'iclose_ts': from_timestamp, 'bopen_ts': from_timestamp},
na_values=['-1'])
except IOError:
print
"File not found: {}".format(bar_file)
date += timedelta(days=1)
continue
bars_df.set_index('iclose_ts', inplace=True)
bars_df = bars_df.between_time('09:30:00', '16:00:00')
bars_df = aggregate_bars(bars_df, freq)
bars_df['giclose_ts'] = bars_df['iclose_ts']
bars_df['date'] = bars_df['gdate'] = date
if cols is not None:
for col in bars_df.columns:
if col not in cols: del bars_df[col]
bars_df = pd.merge(uni_df[uni_df['date'] == date], bars_df, how='inner', left_on=['ticker'],
right_on=['ticker'], sort=True, suffixes=['', '_dead'])
result_dfs.append(bars_df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs)
result_df.set_index(keys=['iclose_ts', 'sid'], inplace=True)
result_df = remove_dup_cols(result_df)
return result_df
def load_earnings_dates(uni_df, start, end):
date = start
result_dfs = list()
uni_df = uni_df.reset_index()
while (date < end):
# year = date.strftime("%Y")
dateStr = date.strftime("%Y%m%d")
earnings_dir = EARNINGS_BASE_DIR
earnings_file = earnings_dir + "/" + dateStr + ".csv"
try:
df = pd.read_csv(earnings_file)
except IOError:
print
"File not found: {}".format(earnings_file)
date += timedelta(days=1)
continue
df['date'] = date
df = pd.merge(uni_df[uni_df['date'] == date], df, how='inner', left_on=['sid'], right_on=['sid'], sort=True,
suffixes=['', '_dead'])
result_dfs.append(df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs)
result_df['earndate'] = pd.to_datetime(result_df['earn_rpt_date'].apply(str))
f = lambda x: len(pd.bdate_range(x['date'], x['earndate']))
result_df['daysToEarn'] = result_df.apply(f, axis=1)
result_df.ix[result_df['earn_rpt_time_norm'] == "AFTMKT", 'daysToEarn'] = result_df.ix[result_df[
'earn_rpt_time_norm'] == "AFTMKT", 'daysToEarn'] + 1
result_df.set_index(keys=['date', 'sid'], inplace=True)
result_df = remove_dup_cols(result_df)
return result_df
def load_past_earnings_dates(uni_df, start, end):
date = start
result_dfs = list()
uni_df = uni_df.reset_index()
while (date < end):
# year = date.strftime("%Y")
dateStr = date.strftime("%Y%m%d")
earnings_dir = EARNINGS_BASE_DIR
earnings_file = earnings_dir + "/" + dateStr + ".latest_earnings_date.rev.csv"
print
earnings_file
try:
df = pd.read_csv(earnings_file)
except IOError:
print
"File not found: {}".format(earnings_file)
date += timedelta(days=1)
continue
df['date'] = date
df = pd.merge(uni_df[uni_df['date'] == date], df, how='inner', left_on=['sid'], right_on=['sid'], sort=True,
suffixes=['', '_dead'])
result_dfs.append(df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs)
result_df = result_df.dropna(subset=['latest_earnings_date'])
# ugh...
result_df['latest_earnings_date'] = result_df['latest_earnings_date'].apply(str).str.replace("\.0", "")
result_df['latest_earnings_date'] = pd.to_datetime(result_df['latest_earnings_date'])
f = lambda x: len(pd.bdate_range(x['date'], x['latest_earnings_date']))
result_df['daysFromEarn'] = result_df.apply(f, axis=1)
result_df.set_index(keys=['date', 'sid'], inplace=True)
result_df = remove_dup_cols(result_df)
return result_df
def load_locates(uni_df, start, end):
date = start
result_dfs = list()
uni_df = uni_df.reset_index()
while (date < end):
# year = date.strftime("%Y")
dateStr = date.strftime("%Y%m%d")
locates_dir = LOCATES_BASE_DIR
locates_file = locates_dir + dateStr + ".csv"
try:
df = pd.read_csv(locates_file, usecols=['sid', 'qty', 'fee_rate'])
except IOError:
print
"File not found: {}".format(locates_file)
date += timedelta(days=1)
continue
df['date'] = date
df = pd.merge(uni_df[uni_df['date'] == date], df, how='inner', left_on=['sid'], right_on=['sid'], sort=True,
suffixes=['', '_dead'])
result_dfs.append(df)
date += timedelta(days=1)
result_df = pd.concat(result_dfs)
result_df.set_index(['date', 'sid'], inplace=True)
result_df['borrow_qty'] = -1 * result_df['qty']
del result_df['qty']
result_df = remove_dup_cols(result_df)
return result_df
def load_merged_results(fdirs, start, end, cols=None):
merged_df = None
for fdir in fdirs:
df = load_all_results(fdir, start, end, cols)
if merged_df is None:
merged_df = df
else:
merged_df = pd.merge(merged_df, df, left_index=True, right_index=True, suffixes=['', '_dead'])
merged_df = remove_dup_cols(merged_df)
return merged_df
def load_mus(mdir, fcast, start, end):
print
"Looking in {}".format(mdir)
fcast_dfs = list()
for ff in sorted(glob.glob(mdir + "/" + fcast + "/alpha.*")):
m = re.match(r".*alpha\." + fcast + "\.(\d{8})_(\d{4}).*", str(ff))
fdate = m.group(1)
ftime = m.group(2)
if int(ftime) <= 930 or int(ftime) >= 1600: continue
if start is not None:
if int(fdate) < int(start) or int(fdate) > int(end): continue
print
"Loading {} for {}".format(ff, fdate)
ts = dateparser.parse(fdate + " " + ftime)
df = pd.read_csv(ff, header=0, parse_dates=True, sep=",")
df['iclose_ts'] = ts
df.set_index(['iclose_ts', 'sid'], inplace=True)
fcast_dfs.append(df)
fcast_df = pd.concat(fcast_dfs, verify_integrity=True)
return fcast_df
def load_qb_implied_orders(mdir, start, end):
print
"Looking in {}".format(mdir)
fcast_dfs = list()
files = sorted(glob.glob(mdir + "/*.PORTFOLIO.csv"))
for ff in files:
m = re.match(r".*(\d{8}).PORTFOLIO.csv", str(ff))
fdate = m.group(1)
if start is not None:
if int(fdate) < int(start) or int(fdate) > int(end): continue
print
"Loading {} for {}".format(ff, fdate)
df = pd.read_csv(ff, header=0, parse_dates=True, sep=",",
usecols=['time', 'sid', 'ref_price', 'net_qty', 'open_long_amt', 'open_short_amt'],
index=['time', 'sid'])
df['open_order'] = (df['open_long_amt'] - df['open_short_amt']) / df['ref_price']
result_l = list()
for timeslice in ['10:02', '10:32', '11:02', '11:32', '12:02', '12:32', '13:02', '13:32', '14:02', '14:32',
'15:02', '15:32']:
timeslice_df = df.unstack().between_time(timeslice, timeslice).stack()
result_l.append(timeslice_df)
df = pd.concat(result_l)
df['iclose_ts'] = pd.to_datetime(fdate + " " + df['time'])
df['iclose_ts'] = df['iclose_ts'] - timedelta(minutes=2)
df = df.reset_index().set_index(['iclose_ts', 'sid'])
fcast_dfs.append(df)
fcast_df = pd.concat(fcast_dfs, verify_integrity=True)
return fcast_df
def load_qb_positions(mdir, start, end):
print
"Looking in {}".format(mdir)
fcast_dfs = list()
files = sorted(glob.glob(mdir + "/*.csv"))
for ff in files:
m = re.match(r".*(\d{8}).PORTFOLIO.csv", str(ff))
fdate = m.group(1)
if start is not None:
if int(fdate) < int(start) or int(fdate) > int(end): continue
print
"Loading {} for {}".format(ff, fdate)
df = pd.read_csv(ff, header=0, parse_dates=True, sep=",",
usecols=['time', 'sid', 'ref_price', 'net_qty', 'open_long_amt', 'open_short_amt'])
df['iclose_ts'] = pd.to_datetime(fdate + " " + df['time'])
df.set_index(['iclose_ts', 'sid'], inplace=True)
fcast_dfs.append(df)
fcast_df = pd.concat(fcast_dfs, verify_integrity=True)
return fcast_df
def load_qb_eods(mdir, start, end):
print
"Looking in {}".format(mdir)
fcast_dfs = list()
files = sorted(glob.glob(mdir + "/.csv"))
for ff in files:
m = re.match(r".*(\d{8}).EOD.csv", str(ff))
fdate = m.group(1)
if start is not None:
if int(fdate) < int(start) or int(fdate) > int(end): continue
print
"Loading {} for {}".format(ff, fdate)
df = pd.read_csv(ff, header=0, parse_dates=True, sep=",",
usecols=['time', 'sid', 'ref_price', 'today_long', 'today_short'])
df['iclose_ts'] = pd.to_datetime(fdate + " 16:00:00")
df.set_index(['iclose_ts', 'sid'], inplace=True)
fcast_dfs.append(df)
fcast_df = pd.concat(fcast_dfs, verify_integrity=True)
fcast_df['dtradenot'] = fcast_df['today_long'] - fcast_df['today_short']
return fcast_df
def load_qb_orders(ofile, date):
df = pd.read_csv(ofile, usecols=['time', 'symbol', 'sid', 'id', 'side', 'qty', 'px', 'state'])
df = df[df['state'] == "NEW"]
df['ts'] = pd.to_datetime(date + " " + df['time'])
df['ts'] = df['ts'] - timedelta(minutes=1, seconds=1)
df.set_index(['ts', 'sid'], inplace=True)
del df['time']
del df['state']
df.ix[df['side'] == "Short", 'qty'] = -1 * df['qty']
df.ix[df['side'] == "SellShort", 'qty'] = -1 * df['qty']
del df['side']
return df
def load_qb_exec(efile, date):
df = pd.read_csv(efile, usecols=['time', 'symbol', 'sid', 'order_id', 'side', 'qty', 'px'])
df['ts'] = pd.to_datetime(date + " " + df['time'])
del df['time']
df.ix[df['side'] == "Short", 'qty'] = -1 * df['qty']
df.ix[df['side'] == "SellShort", 'qty'] = -1 * df['qty']
del df['side']
return df
def load_factor_cache(start, end):
dfs = list()
for ff in sorted(glob.glob(os.environ['CACHE_DIR'] + "/all.factors.*")):
m = re.match(r".*all\.factors\.(\d{8})-(\d{8}).h5", str(ff))
if m is None: continue
d1 = dateparser.parse(m.group(1))
d2 = dateparser.parse(m.group(2))
if d2 < start or d1 > end: continue
print
"Loading {}".format(ff)
df = pd.read_hdf(ff, 'table')
df = df.truncate(before=start - timedelta(days=30), after=end)
if len(df) > 0:
dfs.append(df)
result_df = pd.DataFrame()
for df in dfs:
result_df = result_df.combine_first(df)
result_df.index.names = ['date', 'factor']
print
result_df.columns
return result_df
def load_cache(start, end, cols=None):
dfs = list()
for ff in sorted(glob.glob(os.environ['CACHE_DIR'] + "/all.2*")):
m = re.match(r".*all\.(\d{8})-(\d{8}).h5", str(ff))
if m is None: continue
d1 = dateparser.parse(m.group(1))
d2 = dateparser.parse(m.group(2))
if d2 < start or d1 > end: continue
print
"Loading {}".format(ff)
df = pd.read_hdf(ff, 'table')
if cols is not None:
df = df[cols]
# df = df.truncate(before=start, after=end)
if len(df) > 0:
dfs.append(df)
result_df = pd.DataFrame()
for df in dfs:
result_df = result_df.combine_first(df)
# result_df = result_df.truncate(before=start, after=end)
result_df.index.names = ['iclose_ts', 'sid']
return result_df
def load_all_results(fdir, start, end, cols=None):
fdir += "/all/"
print
"Looking in {}".format(fdir)
fcast_dfs = list()
for ff in sorted(glob.glob(fdir + "/alpha.*")):
m = re.match(r".*alpha\.all\.(\d{8})_(\d{4}).*", str(ff))
fdate = int(m.group(1))
ftime = int(m.group(2))
if ftime < 1000 or ftime > 1530: continue
if fdate < int(start) or fdate > int(end): continue
print
"Loading {} for {}".format(ff, fdate)
if cols is not None:
df = pd.read_csv(ff, index_col=['iclose_ts', 'sid'], header=0, parse_dates=True, sep=",", usecols=cols)
else:
df = pd.read_csv(ff, index_col=['iclose_ts', 'sid'], header=0, parse_dates=True, sep=",")
fcast_dfs.append(df)
fcast_df = pd.concat(fcast_dfs, verify_integrity=True)
return fcast_df
BARRA_INDS = ['indname1', 'wgt1', 'indname2', 'wgt2', 'indname3', 'wgt3', 'indname4', 'wgt4', 'indname5', 'wgt5']
def transform_barra(barra_df):
print
"Transforming barra data..."
barra1_df = barra_df[['indname1', 'wgt1']].dropna()
barra2_df = barra_df[['indname2', 'wgt2']].dropna()
barra3_df = barra_df[['indname3', 'wgt3']].dropna()
barra4_df = barra_df[['indname4', 'wgt4']].dropna()
barra5_df = barra_df[['indname5', 'wgt5']].dropna()
barra1_df.set_index('indname1', append=True, inplace=True)
barra2_df.set_index('indname2', append=True, inplace=True)
barra3_df.set_index('indname3', append=True, inplace=True)
barra4_df.set_index('indname4', append=True, inplace=True)
barra5_df.set_index('indname5', append=True, inplace=True)
pivot1_df = barra1_df['wgt1'].unstack('indname1').fillna(0)
pivot2_df = barra2_df['wgt2'].unstack('indname2').fillna(0)
pivot3_df = barra3_df['wgt3'].unstack('indname3').fillna(0)
pivot4_df = barra4_df['wgt4'].unstack('indname4').fillna(0)
pivot5_df = barra5_df['wgt5'].unstack('indname5').fillna(0)
# pivot_df = barra_df.pivot( index=['date', 'sid'], columns='indname1', values='wgt1')
pivot_df = pivot1_df.add(pivot2_df, fill_value=0)
pivot_df = pivot_df.add(pivot3_df, fill_value=0)
pivot_df = pivot_df.add(pivot4_df, fill_value=0)
pivot_df = pivot_df.add(pivot5_df, fill_value=0)
barra_df = pd.merge(barra_df, pivot_df, how='left', left_index=True, right_index=True)
del barra_df['indname2']
del barra_df['indname3']
del barra_df['indname4']
del barra_df['indname5']
del barra_df['wgt1']
del barra_df['wgt2']
del barra_df['wgt3']
del barra_df['wgt4']
del barra_df['wgt5']
return barra_df
def load_ratings_hist(uni_df, start, end, intra=False):
if intra:
times = ['09:45', '10:00', '10:15', '10:30', '10:45', '11:00', '11:15', '11:30', '11:45', '12:00', '12:15',
'12:30', '12:45', '13:00', '13:15', '13:30', '13:45', '14:00', '14:15', '14:30', '14:45', '15:00',
'15:15', '15:30', '15:45', '16:00']
else:
if end.hour > 0:
times = [str(end.hour) + ":" + str(end.minute)]
else:
times = ['16:00']
window = timedelta(days=252)
con = lite.connect(ESTIMATES_BASE_DIR + "ibes.db")
date = start
df_list = list()
uni_df = uni_df.reset_index()
while (date < end):
if date.day == end.day:
times = [str(end.hour - 3) + ":" + str(end.minute)]
else:
times = ['16:00']
endDateStr = date.strftime('%Y%m%d')
startDateStr = (date - window).strftime('%Y%m%d')
for time in times:
# timeAdjusted = str(int(time.split(":")[0]) - 3) + ":" + time.split(":")[1]
sql = "select * from t_ibes_hist_rec_snapshot where timestamp between '{} {}' and '{} {}' group by sid, ibes_ticker, estimator having timestamp = max(timestamp)".format(
startDateStr, time, endDateStr, time)
print
sql
df = psql.frame_query(sql, con)
df = df[df['ibes_rec_code'] != '']
# df['ts'] = pd.to_datetime( date.strftime("%Y%m%d") + " " + time )
df['date'] = pd.to_datetime(date.strftime("%Y%m%d"))
df['ibes_rec_code'] = df['ibes_rec_code'].astype(int)
df['timestamp'] = pd.to_datetime(df['timestamp'])
print
df.columns
df = pd.merge(uni_df[uni_df['date'] == date], df, how='inner', left_on=['sid'], right_on=['sid'], sort=True,
suffixes=['', '_dead'])
if intra:
df['iclose_ts'] = pd.to_datetime(endDateStr + " " + time)
df = df.set_index(['iclose_ts', 'sid'])
else:
df = df.set_index(['date', 'sid'])
df_list.append(df)
date += timedelta(days=1)
df = pd.concat(df_list)
if intra:
tstype = 'iclose_ts'
else:
tstype = 'date'
# consensus
result_df = df.groupby(level=[tstype, 'sid']).agg(
{'ibes_rec_code': [np.mean, np.median, np.std, 'count', np.max, np.min], 'timestamp': 'last'})
result_df.columns = result_df.columns.droplevel(0)
for column in result_df.columns:
result_df.rename(columns={column: 'rating_' + column}, inplace=True)
df = df.set_index('estimator', append=True)
df2 = df['ibes_rec_code'].unstack(['estimator', 'sid']).fillna(0).diff().iloc[1:].stack(['sid', 'estimator'])
# should drop first date here
df2 = df2[df2 != 0]
df2 = df2.reset_index('estimator').groupby(level=[tstype, 'sid']).agg(np.mean)
df2.columns = ['rating_diff_mean']
result_df = pd.merge(result_df, df2, left_index=True, right_index=True, how='left')
return result_df
def load_target_hist(uni_df, start, end, intra=False):
window = timedelta(days=252)
con = lite.connect(ESTIMATES_BASE_DIR + "ibes.db")
date = start
df_list = list()
uni_df = uni_df.reset_index()
while (date < end):
if date.day == end.day:
times = [str(end.hour - 3) + ":" + str(end.minute)]
else:
times = ['16:00']
endDateStr = date.strftime('%Y%m%d')
startDateStr = (date - window).strftime('%Y%m%d')
for time in times:
# timeAdjusted = str(int(time.split(":")[0]) - 3) + ":" + time.split(":")[1]
sql = "select * from t_ibes_hist_ptg_snapshot where timestamp between '{} {}' and '{} {}' and horizon in ('', 12) and value > 0 group by sid, ibes_ticker, estimator having timestamp = max(timestamp)".format(
startDateStr, time, endDateStr, time)
print
sql
df = psql.frame_query(sql, con)
df['value'] = df['value'].astype(str)
df = df[df['value'] != '']
# df['ts'] = pd.to_datetime( date.strftime("%Y%m%d") + " " + time )
df['date'] = pd.to_datetime(date.strftime("%Y%m%d"))
df['value'] = df['value'].astype(float)
df['timestamp'] = pd.to_datetime(df['timestamp'])
del df['horizon']
print
df.columns
df = pd.merge(uni_df[uni_df['date'] == date], df, how='inner', left_on=['sid'], right_on=['sid'], sort=True,
suffixes=['', '_dead'])
df = df.set_index(['date', 'sid'])
df_list.append(df)
date += timedelta(days=1)
df = pd.concat(df_list)
# consensus
result_df = df.groupby(level=['date', 'sid']).agg(
{'value': [np.mean, np.median, np.std, 'count', np.max, np.min], 'timestamp': 'last'})
result_df.columns = result_df.columns.droplevel(0)
for column in result_df.columns:
result_df.rename(columns={column: 'target_' + column}, inplace=True)
# detailed
df = df.set_index('estimator', append=True)
df2 = df['value'].unstack(['estimator', 'sid']).fillna(0).diff().iloc[1:].stack(['sid', 'estimator'])
df2 = df2[df2 != 0]
df2 = df2.reset_index('estimator').groupby(level=['date', 'sid']).agg(np.mean)
df2.columns = ['target_diff_mean']
result_df = pd.merge(result_df, df2, left_index=True, right_index=True, how='left')
return result_df
def load_estimate_hist(uni_df, start, end, estimate):
window = timedelta(days=252)
con = lite.connect(ESTIMATES_BASE_DIR + "ibes.db")
date = start
df_list = list()
uni_df = uni_df.reset_index()
while (date < end):
if date.day == end.day:
times = [str(end.hour - 3) + ":" + str(end.minute)]
else:
times = ['16:00']
endDateStr = date.strftime('%Y%m%d')
startDateStr = (date - window).strftime('%Y%m%d')
for time in times:
minPeriod = str(int(endDateStr[2:4])) + endDateStr[4:6]
maxPeriod = str(int(endDateStr[2:4]) + 2) + "00"
sql = "select * from t_ibes_det_snapshot where timestamp between '{} {}' and '{} {}' and measure = '{}' and forecast_period_ind = 1 and forecast_period_end_date > {} and forecast_period_end_date < {} group by sid, ibes_ticker, estimator, forecast_period_ind, forecast_period_end_date having timestamp = max(timestamp) order by sid, forecast_period_end_date;".format(
startDateStr, time, endDateStr, time, estimate, minPeriod, maxPeriod)
print
sql
df = psql.frame_query(sql, con)
df['value'] = df['value'].astype(str)
df = df[df['value'] != '']
# df['ts'] = pd.to_datetime( date.strftime("%Y%m%d") + " " + time )
df['date'] = pd.to_datetime(date.strftime("%Y%m%d"))
df['value'] = df['value'].astype(float)
df['timestamp'] = pd.to_datetime(df['timestamp'])
print
df.columns
df = pd.merge(uni_df[uni_df['date'] == date], df, how='inner', left_on=['sid'], right_on=['sid'], sort=True,
suffixes=['', '_dead'])
df = df[~df.duplicated(cols=['date', 'sid', 'estimator'])]
df = df.set_index(['date', 'sid'])
df_list.append(df)
date += timedelta(days=1)
df = pd.concat(df_list)
print
"DFEPS"
print
df
# consensus
result_df = df.groupby(level=['date', 'sid']).agg(
{'value': [np.mean, np.median, np.std, 'count', np.max, np.min], 'timestamp': 'last'})
result_df.columns = result_df.columns.droplevel(0)
for column in result_df.columns:
result_df.rename(columns={column: estimate + '_' + column}, inplace=True)
# detailed
df = df.set_index('estimator', append=True)
print
"SEAN2"
print
df.head()
df2 = df['value'].unstack(['estimator', 'sid']).fillna(0).diff().iloc[1:].stack(['sid', 'estimator'])
df2 = df2[df2 != 0]
df2 = df2.reset_index('estimator').groupby(level=['date', 'sid']).agg(np.mean)