degiro_analytics 0.3.2

DeGiro Analytics and API tools

Intro

This is a DeGiro analytics platform. DeGiro is a low-fee Dutch investment broker that, sadly, offers limited account analytics functionality. This project leverages DeGiro's private API and provides some investment performance analytics tools.

Requirments

pandas>=1.4.2 requests>=2.27.1

Installation

pip install degiro_analytics

Docker

If you prefer to run the project in Docker, I made an image available in Docker hub. It contains a Jupyter Notebook with getting-started example.

docker pull degiro-analytics-notebook
docker run -p 8888:8888 degiro-analytics-notebook

Just copy the link with an automatically generated token and plug it to your browser to run Jupyter.

Hint: Make sure you don't have any existing notebooks running on port 8888.

Description

degiro_analytics/DegiroWrapper.py contains API to retrieve current portfolio information and product search. It does not include trading API. There are open-source projects implementing trading API.

degiro_analytics/utils.py contains various methods for portfolio analytics.

Examples.ipynb Refer to this Jupyter Notebook for examples.

Examples

import pandas as pd
from degiro_analytics import DeGiroWrapper
from degiro_analytics.utils import irr, analytics
DGW = DeGiroWrapper(True, True, base_curr='EUR') # you will have to provide your credentials (and possibly multi-factor auth code)

Insights into your portfolio

pf = DGW.get_current_portfolio() # retrieves your current portfolio

Get price history of your portfolio constituents

P = DGW.get_prices(pf.index)
P = P.pivot(index='date', columns='name', values='price')
P = P.apply(lambda x: x/x.dropna().iloc[0]) # normalize by starting price
P.plot(title='Portfolio constituents perfomance', figsize=(15, 7))

Perfomance of your account

start_date = '01/01/2020' # use any date
T = DGW.get_transactions(start_date) # trasactions history
P = DGW.get_prices(T.productId.unique()) # get price history of all products from T
P = P[P.date>=T.date.min()]
P = pd.pivot_table(P, index='date', columns='productId', values='price', aggfunc='mean') # product per columns
P.fillna(method='ffill', inplace=True)

Q = pd.pivot_table(T, index='date', columns='productId', values='quantity', aggfunc='sum') # number of shares purchased, product per column
Q = Q.reindex(P.index.append(Q.index)).sort_index().fillna(0).cumsum() # portfolio quantities per day

idx = P.index.get_indexer(Q.index, method='nearest')
matched_P = P.iloc[idx].values

The following cell computes portfolio returns as $R_t = \frac{Q_{t-1}'P_t}{Q_{t-1}'P_{t-1}}$.

numerator = Q.shift().multiply(matched_P).sum(axis=1)
denominator = Q.multiply(matched_P).sum(axis=1).shift()
R = numerator/denominator

IDX = R.fillna(1).cumprod() # index (or normalized price) of the portfolio
IDX.plot(title='Account Portfolio Perfomance', figsize=(15, 7))

Some analytics

analytics(IDX) 

{'Mean return': 0.00027660251717962994,
 'Std': 0.010483131903391498,
 'Risk Adjusted Return': 0.02638548477007559,
 'Number of drawdowns': 28,
 'Maximum Drawdown': 0.25730401117784857,
 'Max drawdown duration (days)': 320,
 'Mean drawdown duration (days)': 26.035714285714285,
 'T': 570}

Cash Flows analysis and money weighted return (IRR)

cf_df = DGW.get_account_cash_flows('01/01/2000', fees=True, dividends=True)
cf = cf_df.set_index('date').CF
cf.loc[pd.to_datetime(DGW.today, dayfirst=True, utc=True)] = pf.Q.multiply(pf.price_base_curr).sum() # current portfolio value

mwr = irr(cf)
print('Money weighted return is', mwr)

Money weighted return is 0.008600000000000003

Search products

search_text = 'Microsoft'
product = DGW.lookup(search_text, limit=1)[0]
print('The current price of', product.name, 'is', product.closePrice, product.currency)
p = product.get_price_hist(convert=False).set_index('date').price
p.plot(title=product.name, figsize=(15,7))

The current price of Microsoft Corp is 229.25 USD

search_text = 'Apple'
product = DGW.lookup(search_text, limit=1)[0]
print('The current price of', product.name, 'is', product.closePrice, product.currency)
p = product.get_price_hist(history='1D', resolution='T1M', convert=False).set_index('date').price
p.plot(title=product.name + ' (intraday pricing)', figsize=(15,7))

The current price of Apple Inc is 140.42 USD

Search and analyze ETFs from core selection

etfs = DGW.search_etfs(only_free=True, limit=200) # returns all ETFs from DeGiro core selection

Loop through the ETFs and analyze the prices

out = []
for etf in etfs:
    p = etf.get_price_hist(resolution='1M')
    a = analytics(p.set_index('date').price)
    a['name'] = etf.name
    out.append(a)

pd.DataFrame(out).sort_values('Risk Adjusted Return')

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	Mean return	Std	Risk Adjusted Return	Number of drawdowns	Maximum Drawdown	Max drawdown duration (days)	Mean drawdown duration (days)	T	name
185	-0.003599	0.024421	-0.147393	6	0.315074	1278	375.333333	80	Xtrackers USD Corporate Bond UCITS ETF 2D HEUR
100	-0.001715	0.012107	-0.141665	5	0.163109	852	329.000000	59	iShares Core Gl Aggregate Bd UCITS ETF EUR Hgd...
190	-0.012569	0.095570	-0.131513	2	0.624981	699	380.000000	27	The Medical Cannabis and Wellness UCITS ETF Acc
178	-0.012005	0.106949	-0.112248	2	0.649046	699	380.000000	27	Rize Medical Cannabis and Life Sciences UCITS ETF
106	-0.001573	0.015111	-0.104113	4	0.207632	1064	578.250000	80	Vanguard EUR Eurozone Government Bd UCITS ETF EUR
...	...	...	...	...	...	...	...	...	...
72	0.015837	0.052422	0.302098	14	0.243017	396	139.357143	78	SPDR MSCI World Technology UCITS ETF
63	0.012074	0.038280	0.315414	23	0.176644	518	166.782609	149	Amundi S&P 500 UCITS ETF- EUR (C)
14	0.015172	0.046238	0.328118	21	0.239507	365	175.523810	142	iShares NASDAQ 100 UCITS ETF USD (Acc)
172	0.023167	0.070230	0.329876	3	0.271599	365	284.333333	31	Lyx Msci Future Etf
149	0.025988	0.065900	0.394360	3	0.238976	365	294.333333	32	WisdomTree Battery Solutions UCITS ETF USD Acc

198 rows × 9 columns