COLAB: setup for strategy backtests

1. install

from __future__ import print_function

!pip install git+https://github.com/scubamut/fintools.git
!pip install cvxopt
!pip install pyfolio
!pip install ffn
!pip install itable

!pip install git+https://github.com/pydata/pandas-datareader.git

2. import

import pandas_datareader.data as web
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime
import itable
import ffn

from fintools.Parameters import Parameters
from fintools.set_start_end import set_start_end
from fintools.get_yahoo_prices import get_yahoo_prices
from fintools.compute_weights_RS_DM import compute_weights_RS_DM
from fintools.compute_weights_PMA import compute_weights_PMA
from fintools.endpoints import endpoints
from fintools.backtest import backtest
from fintools.monthly_return_table import monthly_return_table
from fintools.show_return_table import show_return_table

3. portfolios

portfolios = {
    'RS0001': { 'symbols': ['VCVSX','VWEHX','VFIIX','FGOVX','VWAHX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,
               'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 2, 'frequency': 'M',
              'cash_proxy': 'CASHX', 'risk_free': 0},
    'RS0002': {'symbols': ['MMHYX','FAGIX','VFIIX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,               
               'rs_lookback': 3, 'risk_lookback': 2, 'n_top': 1, 'frequency': 'M',
              'cash_proxy': 'CASHX', 'risk_free': 0},
    'RS0003': {'symbols': ['MMHYX','FAGIX','VFIIX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,
               'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 1, 'frequency': 'Q',
              'cash_proxy': 'CASHX', 'risk_free': 0},    
    'DM0001': {'symbols': ['VCVSX','VWINX','VWEHX','VGHCX','VUSTX','VFIIX','VWAHX','FGOVX','FFXSX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,
               'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 3, 'frequency': 'M',
              'cash_proxy': 'CASHX', 'risk_free': 'FFXSX'},
    'DM0002': {'symbols': ['VCVSX','VUSTX','VWEHX','VFIIX','VGHCX','FRESX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,
               'rs_lookback': 1, 'risk_lookback': 1, 'n_top': 5, 'frequency': 'M',
              'cash_proxy': 'VFIIX', 'risk_free': 'FFXSX'},
    'PMA001': {'symbols': ['VCVSX', 'VFIIX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,  
               'risk_lookback': 3, 'frequency': 'M', 'allocations': [0.6, 0.4],
              'cash_proxy': 'VUSTX'},
    'PMA002': {'symbols': ['VCVSX', 'VWINX', 'VWEHX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,
               'risk_lookback': 3, 'frequency': 'M', 'allocations': [0.6, 0.2, 0.2],
              'cash_proxy': 'VUSTX'},
    'PMA003': {'symbols': ['VCVSX', 'FAGIX', 'VGHCX'], 
               'prices':data_source, 'start':start_date, 'end':end_date,
               'risk_lookback': 2, 'frequency': 'M', 'allocations': [1./3., 1./3., 1./3.],
              'cash_proxy': 'VUSTX'}
    }

4. dates

# Define which online source one should use
data_source = 'yahoo'

# We would like all available data from 01/01/2000 until today.

# start_date = '1986-01-01'
start_date = '2000-01-01'
# end_date = '2016-12-31'
end_date = datetime.today().strftime('%Y-%m-%d')

5. run_strategy

name = 'RS0001'

if 'PMA' in name :
    p_value, p_holdings, p_weights, prices =  compute_weights_PMA (name, portfolios[name])
else :
    p_value, p_holdings, p_weights, prices =  compute_weights_RS_DM (name, portfolios[name])
p_value.plot(figsize=(15, 10), grid=True)

6. performance_statistics

ffn.calc_perf_stats(p_value).display()

7. monthly_return_table

def highlight_pos_neg (s) :
    is_positive = s > 0    
    return ['background-color : rgb(127,255,0)' if v else 'background-color : rgb(255,99,71)' for v in is_positive]

df = monthly_return_table (p_value)

df.style.\
    apply(highlight_pos_neg)

8. bar_plot

frame = df['Annual Returns'].to_frame()
frame['positive'] = df['Annual Returns'] >= 0
frame['Annual Returns'].plot(figsize=(15,10),kind='bar',color=frame.positive.map({True: 'g', False: 'r'}), grid=True)