Machine learning for portfolio management and trading with scikit-learn
This repo contains a set of python notebooks that cover topics related to machine-learning for portfolio management as illustrated by the figure below.
The concatenation of all notebooks as a single pdf file can be found here.
An example to run a simple backtest with learning using a Ridge estimator:
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from skfin import Ridge, MeanVariance, Backtester
from skfin.datasets import load_kf_returns
from skfin.plot import line
estimator = make_pipeline(StandardScaler(with_mean=False),
Ridge(),
MeanVariance())
returns_data = load_kf_returns(cache_dir='data')
ret = returns_data['Monthly']['Average_Value_Weighted_Returns'][:'1999']
transform_X = lambda x: x.rolling(12).mean().fillna(0).values
transform_y = lambda x: x.shift(-1).values
features = transform_X(ret)
target = transform_y(ret)
bt = Backtester(estimator, ret).train(features, target)
line(bt.pnl_, cumsum=True, title='Ridge')git clone https://github.com/schampon/skfin.git
cd skfin
./create_env.sh