Ml_investment

Machine learning tools for investment tasks. The purpose of these tools is to obtain deeper analytics about companies using fundamental, global market and other data. This repo is core of http://fattakhov.site.

Table of content

📔 Documentation
🛠 Installation
⏳ Quick Start
- Use application model
- Create your own pipeline
📦 Applications
⭐ Contributing
- Run tests
- Run tests in Docker

📔 Documentation

Visit Read the Docs to know more about Ml_investment library.

🛠 Installation

PyPI version

$ pip install ml-investment

Latest version from source

$ pip install git+https://github.com/fartuk/ml_investment

Configuration

You may use config file ~/.ml_investment/config.json to change repo parameters i.e. downloading datasets pathes, models pathes etc.

Private information (i.e. api tokens for private datasets downloading) should be located at ~/.ml_investment/secrets.json

⏳ Quick Start

Use application model

There are several pre-defined fitted models at ml_investment.applications. It incapsulating data and weights downloading, pipeline creation and model fitting. So you can just use it without knowing internal structure.

from ml_investment.applications.fair_marketcap_yahoo import FairMarketcapYahoo

fair_marketcap_yahoo = FairMarketcapYahoo()
fair_marketcap_yahoo.execute(['AAPL', 'FB', 'MSFT'])

ticker	date	fair_marketcap_yahoo
AAPL	2020-12-31	5.173328e+11
FB	2020-12-31	8.442045e+11
MSFT	2020-12-31	4.501329e+11

Create your own pipeline

1. Download data

You may download default datasets by ml_investment.download_scripts

from ml_investment.download_scripts import download_yahoo
from ml_investment.utils import load_config

# Config located at ~/.ml_investment/config.json
config = load_config()

download_yahoo.main(config['yahoo_data_path'])

>>> 1365it [03:32,  6.42it/s]
>>> 1365it [01:49,  12.51it/s]

2. Create dict with dataloaders

You may choose from default ml_investment.data_loaders or wrote your own. Each dataloader should have load(index) interface.

from ml_investment.data_loaders.yahoo import YahooQuarterlyData, YahooBaseData

data = {}
data['quarterly'] = YahooQuarterlyData(config['yahoo_data_path'])
data['base'] = YahooBaseData(config['yahoo_data_path'])

3. Define and fit pipeline

You may specify all steps of pipeline creation. Base pipeline consist of the folowing steps:

Create data dict(it was done in previous step)
Define features. Features is a number of values and characteristics that will be calculated for model trainig. Default feature calculators are located at ml_investment.features
Define targets. Target is a final goal of the pipeline, it should represent some desired useful property. Default target calculators are located at ml_investment.targets
Choose model. Model is machine learning algorithm, core of the pipeline. It also may incapsulate validation and other stuff. You may use wrappers from ml_investment.models

import lightgbm as lgbm
from ml_investment.utils import load_config, load_tickers
from ml_investment.features import QuarterlyFeatures, BaseCompanyFeatures,\
                                   FeatureMerger
from ml_investment.targets import BaseInfoTarget
from ml_investment.models import LogExpModel, GroupedOOFModel
from ml_investment.pipelines import Pipeline
from ml_investment.metrics import median_absolute_relative_error

fc1 = QuarterlyFeatures(data_key='quarterly',
                        columns=['netIncome',
                                 'cash',
                                 'totalAssets',
                                 'ebit'],
                        quarter_counts=[2, 4, 10],
                        max_back_quarter=1)

fc2 = BaseCompanyFeatures(data_key='base', cat_columns=['sector'])

feature = FeatureMerger(fc1, fc2, on='ticker')

target = BaseInfoTarget(data_key='base', col='enterpriseValue')

base_model = LogExpModel(lgbm.sklearn.LGBMRegressor())
model = GroupedOOFModel(base_model=base_model,
                        group_column='ticker',
                        fold_cnt=4)

pipeline = Pipeline(data=data,
                    feature=feature,
                    target=target,
                    model=model,
                    out_name='my_super_model')

tickers = load_tickers()['base_us_stocks']
pipeline.fit(tickers, metric=median_absolute_relative_error)

>>> {'metric_my_super_model': 0.40599471294301914}

4. Inference your pipeline

Since ml_investment.models.GroupedOOFModel was used, there are no data leakage and you may use pipeline on the same company tickers.

pipeline.execute(['AAPL', 'FB', 'MSFT'])

ticker	date	my_super_model
AAPL	2020-12-31	8.170051e+11
FB	2020-12-31	3.898840e+11
MSFT	2020-12-31	3.540126e+11

📦 Applications

Collection of pre-trained models

FairMarketcapYahoo [docs]
FairMarketcapSF1 [docs]
FairMarketcapDiffYahoo [docs]
FairMarketcapDiffSF1 [docs]
MarketcapDownStdYahoo [docs]
MarketcapDownStdSF1 [docs]

⭐ Contributing

Run tests

$ cd /path/to/ml_investmant && pytest

Run tests in Docker

$ docker build . -t tests
$ docker run tests

DanielSerdyukov/ml_investment