fintuna 0.1.5

Parameter optimization for finance

Fintuna: Parameter optimization for finance

Fintuna is a framework that uses machine learning for asset management. It enables fast prototyping for multi-asset applications such as stock-picking.

Features:

• model training

• hyper-parameters tuning

• walk-forward backtesting

• strategy evaluation

It is a lightweight framework that combines LightGBM, Optuna, Quantstats and Shap to develop ML-based stock-picking strategies.

Multi Asset

Looking at multiple assets is supposed to reveal more alpha-opportunities than looking at a single one. Also, the more assets the more data which is beneficial for machine learning tasks. Therefore, Fintuna is designed for multi-asset applications. The data structure is a Pandas Multiindex Dataframe where the index is time, the first column-level is the asset and the second is the feature (= panel or longitudinal data). Internally features are stacked and a model is trained to learn cross-asset patterns.

#	Asset 1	Asset 2	Asset 3	Asset 4
#	feature1	feature2	feature1	feature2	feature1	feature2	feature1	feature2
t0	float	category	float	category	float	category	float	NaN
t1	float	category	float	category	float	category	float	NaN

Strategy Agnostic

Fintuna is not tied to one specific trading strategy. Strategies are implemented as fintuna.model.ModelBase. It defines the classification task (= extract_label) as well as a a classification-to-returns mapping (= realized_returns). A simple example is to predict the directional change and buy the asset with the most confident prediction (see fintuna.model.LongOnly).

Backtesting

Fintuna uses walk-forward backtesting.

• Train data is used to train the classifier.

• Tune data is used for hyper-parameter optimization.

• Eval data is used for backtesting

Executing the fintuna.Finstudy.explore method multiple times on same data introduces the risk of overfitting. Use feature importance and shap values, rather than merely looking at trading performance.

Calling fintuna.Finstudy.finish prepares the model for deployment. It sub-selects models that also perform well on evaluation data. and refits them on all data.

Data First

A good trading strategy demands good and possibly unique data. Fintuna does NOT help you in finding the right data. But consider the following guidelines:

• Have at least a few hundreds of observations.

• Use multiple assets.

• Use assets with similar characteristics (e.g. cryptos, tech-stocks, etc.).

• Make sure features across assets have similar properties (otherwise use zscore).

• Use lagged features to boost performance.

Usage

Install fintuna via pip.

pip install fintuna

Run the most basic example below. For detailed guidance look at examples at docs or at docs/source/examples.

import fintuna as ft

# get data
data, specs = ft.data.get_crypto_features()

# explore
crypto_study = ft.FinStudy(ft.model.LongOnly, data, data_specs=specs)
results = crypto_study.explore(n_trials=50, ensemble_size=3)

# analyze
ft.utils.plot_backtest(results['performance'], results['benchmark'])

TODO

• Binance Trading Sink

• MajorityVoteEnsemble

• Backtest plots with shap values