Quick Start
This section covers some quick examples of FinQuant’s features. For a full overview please continue with the documentation, and/or have a look at Examples.
Building a Portfolio
Getting an object of Portfolio that holds stock prices of four different stocks, as well as its properties and interfaces to optimisation methods is as simple as:
from finquant.portfolio import build_portfolio
names = ['GOOG', 'AMZN', 'MCD', 'DIS']
pf = build_portfolio(names=names)
The above uses Quandl in the background to download the requested data. For more information on Quandl, please refer to quandl.
If preferred, FinQuant also allows to fetch stock price data from Yahoo Finance. The code snippet below is the equivalent to the above, but using yfinance instead (default value for data_api is "quandl"):
from finquant.portfolio import build_portfolio
names = ['GOOG', 'AMZN', 'MCD', 'DIS']
pf = build_portfolio(names=names, data_api="yfinance")
Alternatively, if you already are in possession of stock prices you want to analyse/optimise, you can do the following.
import pathlib
from finquant.portfolio import build_portfolio
df_data_path = pathlib.Path() / 'data' / 'ex1-stockdata.csv'
df_data = pd.read_csv(df_data_path, index_col='Date', parse_dates=True)
# building a portfolio by providing stock data
pf = build_portfolio(data=df_data)
For this to work, the data is required to be a pandas.DataFrame with stock prices as columns.
Properties of the Portfolio
The portfolio’s properties are automatically computed as it is being built. One can have a look at them with
pf.properties()
which shows
----------------------------------------------------------------------
Stocks: GOOG, AMZN, MCD, DIS
Time window/frequency: 252
Risk free rate: 0.005
Portfolio Expected Return: 0.266
Portfolio Volatility: 0.156
Portfolio Sharpe Ratio: 1.674
Skewness:
GOOG AMZN MCD DIS
0 0.124184 0.087516 0.58698 0.040569
Kurtosis:
GOOG AMZN MCD DIS
0 -0.751818 -0.856101 -0.602008 -0.892666
Information:
Allocation Name
0 0.25 GOOG
1 0.25 AMZN
2 0.25 MCD
3 0.25 DIS
----------------------------------------------------------------------
Moving Averages
Moving Averages and Bollinger Bands can be computed and visualised with the help of the module finquant.moving_average.
Note
When computing/visualising a band of Moving Averages, compute_ma automatically finds the buy/sell signals based on the minimum/maximum Moving Average that were computed and highlights those with arrow up/down markers.
from finquant.moving_average import compute_ma, ema
# get stock data for Disney
dis = pf.get_stock("DIS").data.copy(deep=True)
spans = [10, 50, 100, 150, 200]
# computing and visualising a band of moving averages
ma = compute_ma(dis, ema, spans, plot=True)
print(ma.tail())
which results in
DIS 10d 50d 100d 150d 200d
Date
2017-12-22 108.67 109.093968 104.810423 103.771618 103.716741 103.640858
2017-12-26 108.12 108.916883 104.940210 103.857724 103.775063 103.685426
2017-12-27 107.64 108.684722 105.046085 103.932621 103.826254 103.724775
2017-12-28 107.77 108.518409 105.152905 104.008608 103.878489 103.765026
2017-12-29 107.51 108.335062 105.245340 104.077943 103.926588 103.802290
Portfolio Optimisation
FinQuant allows the optimisation of financial portfolios along the Efficient Frontier by minimising a cost/objective function. FinQuant uses the Python package scipy for the minimisation. Alternatively, a Monte Carlo approach is implemented as well. The below demonstrates how FinQuant performs such an optimisation and visualisation of the results.
# Monte Carlo optimisation
opt_w, opt_res = pf.mc_optimisation(num_trials=5000)
pf.mc_plot_results()
# minimisation to compute efficient frontier and optimal portfolios along it
pf.ef_plot_efrontier()
pf.ef.plot_optimal_portfolios()
# plotting individual stocks
pf.plot_stocks()
