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backtest.py
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backtest.py
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# System libraries
import sys, os, gc
import datetime
# Math libraries
import math, random
import pandas as pd, numpy as np
import scipy
from scipy import stats
from datetime import timedelta
from datetime import datetime
import itertools
# Data storage libraries
import pickle, sqlite3, simpledbf, boto3
# Custom financial data libraries
import utils.findata_utils as fd
import utils.ml_utils as ml_utils
# Plotting libraries
import matplotlib.pyplot as plt
from matplotlib import rcParams
import warnings
#if not sys.warnoptions:
# warnings.simplefilter("ignore")
from importlib import reload
fd = reload(fd)
import sklearn as sk
#import tensorflow as tf
import xgboost as xgb
#import keras
from imblearn.over_sampling import RandomOverSampler
from sklearn import svm
from sklearn import preprocessing
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.linear_model import ElasticNet, LogisticRegression
from sklearn.metrics import explained_variance_score, mean_squared_error, confusion_matrix, classification_report, accuracy_score
from sklearn.model_selection import cross_val_score, KFold, GridSearchCV
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.pipeline import Pipeline
from sklearn.externals import joblib
#from keras.models import Sequential
#from keras.optimizers import SGD
#from keras.layers import Dense, Dropout
#from keras.wrappers.scikit_learn import KerasRegressor
#from yellowbrick.regressor import ResidualsPlot, PredictionError
# Connect to databases
db = 'C:\\Datasets\\thesis.db'
overleaf = ['C:','Users','bryce','OneDrive','Documents','Overleaf','Thesis','assets','exports']
conn = sqlite3.connect(db)
c = conn.cursor()
hdf_path = 'C:\\Datasets\\thesis.h5'
hdf = pd.HDFStore(hdf_path)
import warnings
if not sys.warnoptions:
warnings.simplefilter("ignore")
holding_period = 30
look_back = 30
short = True
signals = pd.read_sql('''
SELECT * FROM signals
''',
conn)
signals['trade_date'] = pd.to_datetime(signals['trade_date'])
returns = pd.read_sql('''
SELECT * FROM daily_abnormal_returns
WHERE ticker in ('{Tickers}')
'''.format(Tickers="','".join(signals['ticker'].unique())),
conn)
returns['datetime'] = returns['date'].apply(lambda date: datetime.strptime(date, '%Y-%m-%d'))
port_return = []
skipped_signals = []
action = 'Sell' if short else 'Buy'
try:
for i, signal in signals.iterrows():
print('%s %s (p=%f)' % (action, signal['ticker'], signal['probability']))
look_back_to = signal['trade_date']-timedelta(days=look_back)
# Choose the day to open the position on
# if the first day of the month is a weekend, find the next monday
open_position_on = signal['trade_date']
while open_position_on.strftime('%Y-%m-%d') not in returns['date'].values:
open_position_on = open_position_on+timedelta(days=1)
print(' > Open position on %s'%open_position_on.strftime('%Y-%m-%d'))
close_position_on = signal['trade_date']+timedelta(days=holding_period)
ss_return = returns.loc[(returns['ticker']==signal['ticker']) & (returns['datetime'].between(look_back_to, close_position_on))]
# Choose the most appropriate abnormal return calculation
rebal_dates = returns.loc[returns['ticker']==signal['ticker']]['rebal_date'].unique()
try:
rebal_date_to_use = datetime.strptime(max(rebal_dates)[:10],'%Y-%m-%d')
except:
print(' > No abnormal return data for %s; skipping' % signal['ticker'])
signal['reason'] = 'No factor model data for this security'
skipped_signals.append(pd.DataFrame(signal).T)
continue
for possible_date in rebal_dates:
#print(possible_date)
if signal['trade_date'] < datetime.strptime(possible_date[:10],'%Y-%m-%d'):
rebal_date_to_use = possible_date
ss_return = ss_return.loc[ss_return['rebal_date']==rebal_date_to_use]
print(' > Using CAR calculations for %s' % str(rebal_date_to_use)[:10])
ss_return['d'] = pd.to_datetime(ss_return['date']) - signal['trade_date']
ss_return['culm_return'] = (ss_return['r_daily']+1).cumprod()
ss_return['car'] = (ss_return['ar_daily']+1).cumprod()
try:
return_index = ss_return.loc[ss_return['datetime']==open_position_on].iloc[0]
except:
if str(open_position_on)[:10] < returns.loc[(returns['ticker']==ticker)]['date'].min():
signal['reason'] = 'No market history to this date'
print(' > Do not have market history to this date; skipping')
skipped_signals.append(pd.DataFrame(signal).T)
continue
else:
raise(' > Something went wrong')
signal['reason'] = 'Unknown error'
skipped_signals.append(pd.DataFrame(signal).T)
#break
continue
for field in ['culm_return','car']:
ss_return[field] = ss_return[field] - return_index[field]
for field in ['culm_return','car']:
ss_return[field] = ss_return[field]*-1 if short else ss_return[field]
ss_return[field]=(ss_return[field]+1)*10000
ss_return = ss_return[['ticker','date','d','culm_return', 'car']]
ss_return['d'] = ss_return['d'].apply(lambda d: d.days)
ss_return['trade'] = i
ss_return['probability'] = signal['probability']
port_return.append(ss_return)
except Exception as e:
print(ss_return)
print(signal)
print()
port_return = pd.concat(port_return)
try:
skipped_signals = pd.concat(skipped_signals)
print('Skipped signals:')
print(skipped_signals)
except:
pass
#probabilities = port_return['probability']
pivot = port_return.pivot_table(index=['d'], columns=['trade'], values=['culm_return','car','probability'], aggfunc=np.mean)
pivot.fillna(method='ffill', inplace=True)
pivot.fillna(method='bfill', inplace=True)
for trade in pivot.columns.get_level_values(1):
pivot['weight',trade] = pivot['probability',trade]/pivot['probability'].sum(axis=1)
for metric in ['culm_return','car']:
pivot[metric,'portfolio'] = (pivot[metric]*pivot['weight']).sum(axis=1)
pivot[metric,'portfolio_std'] = pivot[metric].std(axis=1)
print(pivot)
# Plot the results
font = {'family' : 'Arial',
'weight' : 'normal',
'size' : 12}
plt.rc('font', **font)
fig = plt.figure(figsize=(10,5))
fig.patch.set_facecolor('white')
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_visible(True)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(True)
ax.grid(True,axis='both',linestyle=':')
colors = {'car':'navy',
'culm_return':'darkgreen'}
nice_names = {'car':'Cumulative Abnormal Return',
'culm_return':'Cumulative Return'}
for metric in ['culm_return','car']:
ax.plot(pivot.index, pivot[metric,'portfolio'], label=nice_names[metric], color=colors[metric])
ax.fill_between(pivot.index,
pivot[metric,'portfolio']-pivot[metric,'portfolio_std'],
pivot[metric,'portfolio']+pivot[metric,'portfolio_std'],
color=colors[metric],
alpha=0.1)
ax.fill_between([4000,14000], [1,1], color='grey', alpha=0.1)
ax.fill_between([4000,14000], [-1,-1], color='grey', alpha=0.1)
#ax.xticks(np.linspace(-30,30, ))
plt.legend(frameon=False, loc='lower right')
plt.title('Portfolio Returns')
plt.ylabel('Culmulative Returns\n(indexed to day-0)')
plt.xlabel('Days Since (To) the Rebal Day')
plt.xlim(-look_back, holding_period)
plt.ylim(4000,14000)
plt.show()
fig.savefig('\\'.join(overleaf+['portfolio_backtest.png']))
plt.close()
print('Portfolio alpha:')
a = pivot['car','portfolio'].iloc[-1]
print(a)
print('Portfolio risk:')
s = pivot['car','portfolio'].std()
print(s)
print(a/s)