options_backtester/asset_backtester/examples/example.ipynb

In [1]:

import os
import sys

backtester_dir = os.path.realpath(os.path.join(os.getcwd(), '..', '..'))
sys.path.append(backtester_dir) # Add backtester base dir to $PYTHONPATH

In [2]:

from asset_backtester import Backtest, Portfolio, Asset
from asset_backtester.datahandler import HistoricalAssetData
from asset_backtester.charts import *

In [3]:

import pandas_datareader as pdr
import datetime

As a first example, we run a backtest of a portfolio with the following tickers during the year 2019. Data is taken from Tiingo.

In [39]:

%env TIINGO_API_KEY=your_tiingo_api_key

env: TIINGO_API_KEY=your_tiingo_api_key

In [21]:

api_key = os.environ["TIINGO_API_KEY"]

start = datetime.datetime(2019, 1, 1)
end = datetime.datetime(2019, 12, 31)
tickers = ["VOO", "TUR", "RSX", "EWY", "EWS", "VTIP", "TLT", "BWX", "PDBC", "IAU", "VNQI"]

symbols = pdr.get_data_tiingo(tickers, api_key=api_key, start=start, end=end)

In [22]:

symbols

Out [22]:

		close	high	low	open	volume	adjClose	adjHigh	adjLow	adjOpen	adjVolume	divCash	splitFactor
symbol	date
VOO	2019-01-02 00:00:00+00:00	229.99	230.85	226.02	226.18	4891329	225.367394	226.210108	221.477187	221.633971	4891329	0.0	1.0
	2019-01-03 00:00:00+00:00	224.50	228.42	223.97	228.10	3330026	219.987738	223.828949	219.468391	223.515381	3330026	0.0	1.0
	2019-01-04 00:00:00+00:00	231.91	232.62	227.15	227.54	5100088	227.248803	227.944533	222.584475	222.966637	5100088	0.0	1.0
	2019-01-07 00:00:00+00:00	233.65	235.23	231.32	232.29	3706014	228.953831	230.502074	226.670662	227.621166	3706014	0.0	1.0
	2019-01-08 00:00:00+00:00	235.92	236.46	233.43	236.05	3546649	231.178206	231.707352	228.738253	231.305593	3546649	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
VNQI	2019-12-24 00:00:00+00:00	58.22	58.23	58.08	58.12	637517	58.220000	58.230000	58.080000	58.120000	637517	0.0	1.0
	2019-12-26 00:00:00+00:00	58.54	58.54	58.25	58.28	355091	58.540000	58.540000	58.250000	58.280000	355091	0.0	1.0
	2019-12-27 00:00:00+00:00	58.96	58.96	58.77	58.78	322343	58.960000	58.960000	58.770000	58.780000	322343	0.0	1.0
	2019-12-30 00:00:00+00:00	58.71	59.02	58.71	59.00	294134	58.710000	59.020000	58.710000	59.000000	294134	0.0	1.0
	2019-12-31 00:00:00+00:00	59.09	59.09	58.85	58.95	219935	59.090000	59.090000	58.850000	58.950000	219935	0.0	1.0

2772 rows × 12 columns

In [4]:

data_dir = os.path.join(backtester_dir, 'data')
save_path = os.path.join(data_dir, 'portfolio_data.csv')
symbols.to_csv(save_path)

Use HistoricalAssetData to load your csv. Data must include date, adjClose and symbol columns to work.

In [5]:

data = HistoricalAssetData(save_path)
schema = data.schema

In [6]:

data

Out [6]:

	symbol	date	close	high	low	open	volume	adjClose	adjHigh	adjLow	adjOpen	adjVolume	divCash	splitFactor
0	VOO	2019-01-02 00:00:00+00:00	229.99	230.85	226.02	226.18	4891329	225.367394	226.210108	221.477187	221.633971	4891329	0.0	1.0
1	VOO	2019-01-03 00:00:00+00:00	224.50	228.42	223.97	228.10	3330026	219.987738	223.828949	219.468391	223.515381	3330026	0.0	1.0
2	VOO	2019-01-04 00:00:00+00:00	231.91	232.62	227.15	227.54	5100088	227.248803	227.944533	222.584475	222.966637	5100088	0.0	1.0
3	VOO	2019-01-07 00:00:00+00:00	233.65	235.23	231.32	232.29	3706014	228.953831	230.502074	226.670662	227.621166	3706014	0.0	1.0
4	VOO	2019-01-08 00:00:00+00:00	235.92	236.46	233.43	236.05	3546649	231.178206	231.707352	228.738253	231.305593	3546649	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2767	VNQI	2019-12-24 00:00:00+00:00	58.22	58.23	58.08	58.12	637517	58.220000	58.230000	58.080000	58.120000	637517	0.0	1.0
2768	VNQI	2019-12-26 00:00:00+00:00	58.54	58.54	58.25	58.28	355091	58.540000	58.540000	58.250000	58.280000	355091	0.0	1.0
2769	VNQI	2019-12-27 00:00:00+00:00	58.96	58.96	58.77	58.78	322343	58.960000	58.960000	58.770000	58.780000	322343	0.0	1.0
2770	VNQI	2019-12-30 00:00:00+00:00	58.71	59.02	58.71	59.00	294134	58.710000	59.020000	58.710000	59.000000	294134	0.0	1.0
2771	VNQI	2019-12-31 00:00:00+00:00	59.09	59.09	58.85	58.95	219935	59.090000	59.090000	58.850000	58.950000	219935	0.0	1.0

2772 rows × 14 columns

To create a portfolio, use the Portfolio class and then create assets with Asset(name, percentage), where name should match the name given in the symbol column and percentage is the percentage (from 0 to 1) allocated to that specific asset.

In [7]:

portfolio = Portfolio()

In [8]:

VOO = Asset('VOO', 0.1)
TUR = Asset('TUR', 0.05)
RSX = Asset('RSX', 0.05)
EWY = Asset('EWY', 0.05)
EWS = Asset('EWS', 0.05)
VTIP = Asset('VTIP', 0.10)
TLT = Asset('TLT', 0.20)
BWX = Asset('BWX', 0.10)
PDBC = Asset('PDBC', 0.05)
IAU = Asset('IAU', 0.15)
VNQI = Asset('VNQI', 0.10)

In [9]:

portfolio.add_assets([
    VOO, TUR, RSX, EWY, EWS, VTIP, TLT, BWX,
    PDBC, IAU, VNQI
])

Out [9]:

<asset_backtester.portfolio.portfolio.Portfolio at 0x123375f90>

Create a Backtest object passing it the schema of your data, add the portfolio and data to it and then run it. The run method takes the initial capital (default value 1.000.000) and periods as arguments. periods defines how often, in months, a rebalancing of the portfolio is made, so that a value of '1' means a monthly rebalancing and '6' a bi-annual one. Note that its value should be a string and it defaults to '1'. For a backtest with no rebalancing use None.

In [10]:

bt = Backtest(schema)
bt.portfolio = portfolio
bt.data = data

In [11]:

bt.run(initial_capital=1_000_000, periods=None)

Out [11]:

0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:10

	capital	cash	total_value	% change	accumulated return
2019-01-01 00:00:00+00:00	1.000000e+06	1000000.000000	NaN	NaN	NaN
2019-01-02 00:00:00+00:00	1.000000e+06	365.102886	9.996349e+05	0.000000	1.000000
2019-01-03 00:00:00+00:00	9.987002e+05	365.102886	9.983351e+05	-0.001300	0.998700
2019-01-04 00:00:00+00:00	1.008706e+06	365.102886	1.008341e+06	0.010019	1.008706
2019-01-07 00:00:00+00:00	1.010930e+06	365.102886	1.010565e+06	0.002205	1.010930
...	...	...	...	...	...
2019-12-24 00:00:00+00:00	1.157945e+06	365.102886	1.157580e+06	0.002707	1.157945
2019-12-26 00:00:00+00:00	1.163165e+06	365.102886	1.162800e+06	0.004508	1.163165
2019-12-27 00:00:00+00:00	1.165863e+06	365.102886	1.165498e+06	0.002319	1.165863
2019-12-30 00:00:00+00:00	1.163344e+06	365.102886	1.162979e+06	-0.002161	1.163344
2019-12-31 00:00:00+00:00	1.163594e+06	365.102886	1.163229e+06	0.000215	1.163594

253 rows × 5 columns

When done, the backtester returns a balance sheet with the daily returns and wealth values. Pass this balance dataframe to the functions returns_chart, returns_histogram and monthly_returns_heatmap for better visualization.

In [12]:

returns_chart(bt.balance)

Out [12]:

In [13]:

returns_histogram(bt.balance)

Out [13]:

In [14]:

monthly_returns_heatmap(bt.balance)

Out [14]:

We now run a backtest of a portfolio from the book "The Ivy Portfolio" by Faber and Richardson, consisting of an even allocation in domestic (US) stocks (VTI), foreign stocks (VEU), bonds (BND), real estate (VNQ) and commodities (DBC).

The data we'll use consists of the last 10 years (2010-2019), once again taken from Tiingo.

In [18]:

tickers =  ["VTI", "VEU", "BND", "VNQ", "DBC"]
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2019, 12, 31)

ivy_data = pdr.get_data_tiingo(tickers, api_key=api_key, start=start, end=end)

In [23]:

ivy_data_path = os.path.join(data_dir, 'ivy_portfolio_data.csv')
ivy_data.to_csv(ivy_data_path)

In [24]:

data = HistoricalAssetData(ivy_data_path)
schema = data.schema

In [25]:

data

Out [25]:

	symbol	date	close	high	low	open	volume	adjClose	adjHigh	adjLow	adjOpen	adjVolume	divCash	splitFactor
0	VTI	2010-01-04 00:00:00+00:00	57.31	57.3799	56.84	56.86	2251461	47.189147	47.246703	46.802149	46.818617	2251461	0.0	1.0
1	VTI	2010-01-05 00:00:00+00:00	57.53	57.5400	57.11	57.34	1597643	47.370296	47.378530	47.024467	47.213849	1597643	0.0	1.0
2	VTI	2010-01-06 00:00:00+00:00	57.61	57.7150	57.41	57.50	2120206	47.436168	47.522625	47.271488	47.345594	2120206	0.0	1.0
3	VTI	2010-01-07 00:00:00+00:00	57.85	57.8890	57.29	57.55	1656639	47.633784	47.665897	47.172679	47.386764	1656639	0.0	1.0
4	VTI	2010-01-08 00:00:00+00:00	58.04	58.0461	57.56	57.70	1649919	47.790231	47.795253	47.394998	47.510274	1649919	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
12575	DBC	2019-12-24 00:00:00+00:00	15.93	15.9500	15.86	15.86	466153	15.930000	15.950000	15.860000	15.860000	466153	0.0	1.0
12576	DBC	2019-12-26 00:00:00+00:00	16.05	16.0500	15.97	15.97	679206	16.050000	16.050000	15.970000	15.970000	679206	0.0	1.0
12577	DBC	2019-12-27 00:00:00+00:00	16.08	16.0900	16.02	16.06	1250014	16.080000	16.090000	16.020000	16.060000	1250014	0.0	1.0
12578	DBC	2019-12-30 00:00:00+00:00	16.05	16.1800	15.98	16.16	976284	16.050000	16.180000	15.980000	16.160000	976284	0.0	1.0
12579	DBC	2019-12-31 00:00:00+00:00	15.95	16.0453	15.91	15.95	806115	15.950000	16.045300	15.910000	15.950000	806115	0.0	1.0

12580 rows × 14 columns

In [26]:

portfolio = Portfolio()

In [27]:

VTI = Asset("VTI", 0.2)
VEU = Asset("VEU", 0.2)
BND = Asset("BND", 0.2)
VNQ = Asset("VNQ", 0.2)
DBC = Asset("DBC", 0.2)

In [28]:

portfolio.add_assets([VTI, VEU, BND, VNQ, DBC])

Out [28]:

<asset_backtester.portfolio.portfolio.Portfolio at 0x124662b50>

In [29]:

bt = Backtest(schema)
bt.portfolio = portfolio
bt.data = data

In [30]:

# No rebalancing
bt.run(initial_capital=1_000_000, periods=None)

Out [30]:

0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:46

	capital	cash	total_value	% change	accumulated return
2010-01-03 00:00:00+00:00	1.000000e+06	1000000.000000	NaN	NaN	NaN
2010-01-04 00:00:00+00:00	1.000000e+06	96.545508	9.999035e+05	0.000000	1.000000
2010-01-05 00:00:00+00:00	1.001321e+06	96.545508	1.001225e+06	0.001321	1.001321
2010-01-06 00:00:00+00:00	1.005603e+06	96.545508	1.005507e+06	0.004276	1.005603
2010-01-07 00:00:00+00:00	1.004723e+06	96.545508	1.004627e+06	-0.000875	1.004723
...	...	...	...	...	...
2019-12-24 00:00:00+00:00	2.039175e+06	96.545508	2.039079e+06	0.001159	2.039175
2019-12-26 00:00:00+00:00	2.048066e+06	96.545508	2.047969e+06	0.004360	2.048066
2019-12-27 00:00:00+00:00	2.050801e+06	96.545508	2.050705e+06	0.001336	2.050801
2019-12-30 00:00:00+00:00	2.045131e+06	96.545508	2.045035e+06	-0.002765	2.045131
2019-12-31 00:00:00+00:00	2.051265e+06	96.545508	2.051168e+06	0.002999	2.051265

2517 rows × 5 columns

In [31]:

returns_chart(bt.balance)

Out [31]:

In [32]:

monthly_returns_heatmap(bt.balance)

Out [32]:

For comparison, you can run the same backtest on Portfolio Visualizer.

In [33]:

# Monthly rebalancing
bt.run(initial_capital=1_000_000, periods=1)

Out [33]:

0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:51

	capital	cash	total_value	% change	accumulated return
2010-01-03 00:00:00+00:00	1.000000e+06	1000000.000000	NaN	NaN	NaN
2010-01-04 00:00:00+00:00	1.000000e+06	96.545508	9.999035e+05	0.000000	1.000000
2010-01-05 00:00:00+00:00	1.001321e+06	96.545508	1.001225e+06	0.001321	1.001321
2010-01-06 00:00:00+00:00	1.005603e+06	96.545508	1.005507e+06	0.004276	1.005603
2010-01-07 00:00:00+00:00	1.004723e+06	96.545508	1.004627e+06	-0.000875	1.004723
...	...	...	...	...	...
2019-12-24 00:00:00+00:00	1.697197e+06	189.028410	1.697008e+06	0.001499	1.697197
2019-12-26 00:00:00+00:00	1.704983e+06	189.028410	1.704794e+06	0.004587	1.704983
2019-12-27 00:00:00+00:00	1.707689e+06	189.028410	1.707500e+06	0.001587	1.707689
2019-12-30 00:00:00+00:00	1.703069e+06	189.028410	1.702880e+06	-0.002706	1.703069
2019-12-31 00:00:00+00:00	1.704913e+06	189.028410	1.704724e+06	0.001083	1.704913

2517 rows × 5 columns

In [34]:

returns_chart(bt.balance)

Out [34]:

In [35]:

# Bi-annual rebalancing
bt.run(initial_capital=1_000_000, periods=6)

Out [35]:

0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:53

	capital	cash	total_value	% change	accumulated return
2010-01-03 00:00:00+00:00	1.000000e+06	1000000.000000	NaN	NaN	NaN
2010-01-04 00:00:00+00:00	1.000000e+06	96.545508	9.999035e+05	0.000000	1.000000
2010-01-05 00:00:00+00:00	1.001321e+06	96.545508	1.001225e+06	0.001321	1.001321
2010-01-06 00:00:00+00:00	1.005603e+06	96.545508	1.005507e+06	0.004276	1.005603
2010-01-07 00:00:00+00:00	1.004723e+06	96.545508	1.004627e+06	-0.000875	1.004723
...	...	...	...	...	...
2019-12-24 00:00:00+00:00	1.766451e+06	168.362295	1.766283e+06	0.001476	1.766451
2019-12-26 00:00:00+00:00	1.774559e+06	168.362295	1.774390e+06	0.004590	1.774559
2019-12-27 00:00:00+00:00	1.777363e+06	168.362295	1.777194e+06	0.001580	1.777363
2019-12-30 00:00:00+00:00	1.772492e+06	168.362295	1.772324e+06	-0.002740	1.772492
2019-12-31 00:00:00+00:00	1.774520e+06	168.362295	1.774352e+06	0.001144	1.774520

2517 rows × 5 columns

In [36]:

returns_chart(bt.balance)

Out [36]:

In [37]:

monthly_returns_heatmap(bt.balance)

Out [37]:

In [38]:

returns_histogram(bt.balance)

Out [38]:

In [ ]: