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from glob import iglob
from db import dbengine
from pandas.tseries.offsets import MonthEnd
import os
import pandas as pd
import datetime
import numpy as np
import matplotlib.pyplot as plt
import pandas.tseries.offsets as off
def get_monthly_pnl(group_by = ['identifier']):
sql_string = "SELECT * FROM pnl_reports"
df_pnl = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['date'],
index_col=['date'])
df_pnl['identifier'] = df_pnl.invid.str.replace("_A$", "")
pnl_cols = ['bookunrealmtm', 'bookrealmtm', 'bookrealincome', 'bookunrealincome', 'totalbookpl']
monthend_pnl = df_pnl.groupby(pd.TimeGrouper('M')).apply(lambda df: df.loc[df.index[-1]])
return monthend_pnl.groupby(['date'] + group_by)[['mtd' + col for col in pnl_cols]].sum()
def get_portfolio(report_date):
sql_string = "SELECT * FROM valuation_reports where periodenddate = %s"
df = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['periodenddate'],
index_col=['periodenddate'], params=[report_date,])
df['identifier'] = df.invid.str.replace("_A$", "")
return df
def curr_port_PNL(date = datetime.date.today(), asset_class='Subprime'):
date = (date - off.MonthEnd(1)).date()
sql_string = "SELECT * FROM risk_positions(%s, %s) WHERE notional > 0"
df_positions = pd.read_sql_query(sql_string, dbengine('dawndb'), params=[date, asset_class])
df_pnl = get_monthly_pnl()[:date]
df_all = df_positions.merge(df_pnl.groupby('identifier').sum().reset_index(), on=['identifier'])
return df_all
def trade_performance():
sql_string = "SELECT * FROM bonds"
df_trades = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['lastupdate', 'trade_date', 'settle_date'])
df_trades = df_trades[df_trades.asset_class == 'Subprime']
df_pnl = get_monthly_pnl()
df_sell = df_trades[df_trades.buysell == False].groupby('identifier').last().reset_index()
df_sell.identifier = df_sell.identifier.str[:9]
df_sell['trade_pnl_date'] = df_sell.trade_date + off.MonthEnd(0)
df_buy = df_trades[df_trades.buysell == True].groupby('identifier').last().reset_index()
df_all = df_sell.merge(df_pnl.groupby('identifier').sum().reset_index(), on=['identifier'])
df_all = df_all.merge(df_pnl.reset_index()[['date', 'identifier', 'mtdtotalbookpl']],
left_on=['trade_pnl_date', 'identifier'],
right_on=['date', 'identifier'],
suffixes=('', '_at_trade_month'))
df_all = df_all.drop(['date', 'trade_pnl_date'], axis=1)
#now build up the table
g = df_buy.groupby('identifier').sum()
init_inv = g.principal_payment + g.accrued_payment
init_inv.name = 'initialinvestment'
first_buy_date = df_buy.groupby('identifier').first().trade_date
first_buy_date.name = 'firstbuydate'
df_all = df_all.join(init_inv, on='identifier')
df_all = df_all.join(first_buy_date, on='identifier')
df_all['percent_gain'] = df_all.mtdtotalbookpl / df_all.initialinvestment
df_all['days_held'] = df_all.trade_date - df_all.firstbuydate
df_all = df_all.sort_values('trade_date', ascending=False)
table = pd.DataFrame()
#table['average_days_held'] = df_all.days_held.mean()
return df_all
def get_net_navs():
sql_string = "SELECT * FROM valuation_reports"
df_val = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['periodenddate'])
nav = df_val[df_val.fund == 'SERCGMAST'].groupby('periodenddate')['endbooknav'].sum()
nav = nav.resample('M').last()
df = pd.read_csv('/home/serenitas/edwin/Python/subscription_fee_data.csv', parse_dates=['date'], index_col =['date'])
df.index = df.index.to_period('M').to_timestamp('M')
df = df.join(nav)
df['begbooknav'] = (df.endbooknav + df.net_flow).shift(1)
df.at[('2013-01-31', 'begbooknav')] = 12500000
return df
def alloc(report_date, alloc = 'pnl'):
""" Takes strategy grouping """
"Alloc: pnl or capital"
if alloc == 'pnl':
nav = get_net_navs()
df = get_monthly_pnl(['strat', 'custacctname'])
df = df.join(nav.begbooknav)
df['strat_return'] = df.mtdtotalbookpl / df.begbooknav
df = df.loc[report_date.date()]
elif alloc == 'capital':
df = get_portfolio(report_date)
df.set_index(['strat','custacctname'], inplace=True)
#get strategy lookup table: group-by a merged DF to spot unmapped strategies
strats = pd.read_csv('/home/serenitas/edwin/Python/strat_map.csv', index_col=['strat','custacctname'])
#Check for empty sets: df.set_index(['strat','custacctname']).groupby(['strat','custacctname'])
df = df.merge(strats, left_index=True, right_index=True)
df = df.fillna(-1)
return df.set_index(alloc).groupby(alloc).sum()
def pnl_alloc_plot(df):
""" Takes the alloc('pnl') dataframe """
y = df.strat_return
x = df.index
x_loc = np.arange(len(df.index))
width = .35 #width of the bar
fig, ax = plt.subplots(figsize = (6,6))
ax.bar(x_loc, y, width)
ax.set_xlabel('Strategy')
ax.set_xticks(x_loc + width /2)
ax.set_xticklabels(x, rotation='45')
#set y-axis as percentage
ax.set_ylabel('Return (%)')
y_ticks = ax.get_yticks()
ax.set_yticklabels(['{:.2f}%'.format(y*100) for y in y_ticks])
plt.tight_layout()
def cap_alloc_plot(df):
""" Takes the alloc('capital') dataframe"""
# create piechart and add a circle at the center
df['alloc'] = df.endbooknav/df.endbooknav.sum()
fig, ax = plt.subplots(figsize=(8,4))
ax.pie(df.alloc, labels=df.index, autopct='%1.1f%%',
pctdistance=1.25, labeldistance=1.5)
ax.add_artist(plt.Circle((0,0), 0.7, color='white'))
ax.axis('equal')
plt.tight_layout()
def avg_turnover():
#Total Bond Sales Proceeds/Average starting 12 months NAV
avg_nav = get_net_navs().begbooknav[-12:].mean()
last_monthend = datetime.date.today() - off.MonthEnd(1)
sql_string = "SELECT * FROM bonds where buysell = 'False'"
df = pd.read_sql_query(sql_string, dbengine('dawndb'),
parse_dates=['lastupdate', 'trade_date', 'settle_date'])
df = df[(df.trade_date > last_monthend - off.MonthEnd(12))
& (df.trade_date <= last_monthend)]
return (df.principal_payment + df.accrued_payment).sum()/avg_nav
def calc_trade_performance_stats():
df = trade_performance().set_index('trade_date')
df.days_held = df.days_held.dt.days
df['winners'] = df.apply(lambda df: True if df.percent_gain > 0 else False, axis = 1)
df['curr_face'] = df.principal_payment/(df.price/100)
index = ['All','2017','2016','2015','2014','2013']
results = pd.DataFrame(index = index)
win_per = len(df[df.winners].index)/len(df)
loss_per = 1- win_per
temp = {}
temp1 = {}
for x, df1 in df.groupby('winners'):
for y, df2 in df1.groupby(pd.TimeGrouper(freq='A')):
import pdb; pdb.set_trace()
y = y.date().year
results.loc[y] = df2[df2.days_held.notnull()].mean()[['curr_face','initialinvestment', 'days_held']]
#results.loc[] = len(df2[df2.winners == x].index)/len(df)
df[df.days_held.notnull()]['days_held'].groupby(pd.TimeGrouper(freq='A')).mean()
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