Create conda environment

conda env create -f environment.yml

Activate conda environment

conda activate crypto-forecast

Sync conda environment with yml (to update changes):

conda env update --file local.yml

Use scripts in ./scripts/preprocessing for data ingestion, resulting datasets and json indexes are stored in .data/preprocessed

Use python build_datasets.py to build the merged datasets and index.

Resulting built datasets are stored in ./data/datasets/

• Merged: features are not processed, just stuck together with no lagging. Data is resampled to 3, 7 and 30 periods. Parameters for technical analysis are read from ./config/ta_<periods>.json where is the sampling frequency.
• ATSA: features from "Merged" index are processed according to ATSA's publication (lagged OHLC + TA)
• Improved: features from "Merged" index are processed and engineered in order to ease model's interpretation, currently does not take resampled data into account (Includes lagged close + volume, TA-derived features and coinmetrics features)
• Faceted: features from "Merged" index are selected according to different facets (Price history, Price trend, Volatility, Volume, On-Chain activity). On-chain features are a cherry-picked subset of coinmetrics.io's features trasformed accordingly to their values, price data is processed by percent variation. (as explained in this paper)

Social data features only span 2 years so they are not included in the datasets for now.

• One or more indexes, containing information about file paths and available (groups of) features
• A set of CSV and Excel files for features, one for each symbol
• A set of CSV and Excel files for target features, one for each symbol

Target csv files include:

Informative columns:

• close: closing price for CURRENT data, DO NOT use it as target. It is included for trading simulation.
• labels: text labels for 'class' column
• binary_labels: text labels for 'binary' column
• bin_labels: text labels for 'bin' column
• binary_bin_labels: text labels for 'binary_bin' column

Target columns (integer encoded):

• pct: next day percent variation, frames a regression problem
• class: ATSA-like 3-class classification based on next day price percent variation. Changes > 0.1 result in BUY signal, changes < -0.01 result in SELL signals, in-between values result in HOLD signal.
• binary: 2-class classification based on next day price percent variation. Changes > 0 result in BUY signal, changes < 0 result in SELL signal.
• bin: 3-class classification based on next day price percent variation percentile splitting into 3 equal bins
• binary_bin: binary classification based on next day price percent variation percentile splitting into 2 equal bins

Use python build_model.py -d <dataset> -p <pipeline> to build models for the dataset index.

<dataset> is formatted as <dataset_name>.<dataset_index>

Additional parameters are:

• -e experiment name, default is experiment_<date>_<time>
• --cv number of folds to use for cross validation
• --njobs number of jobs to dispatch for cross validation, default is cpu_count()
• --scoring scoring function to use in cross validation, default is 'precision'
• --test-size test size for data split, default is 0.3
• --use-target Target to use for classification, default depends on pipeline being used
• --expanding-window Whether to use TimeSeriesSplit instead of StratifiedKFold as cross validation provider, default is 0/False
• --parameter-grid Path to a custom parameter grid in JSON format. Parameter naming MUST follow the pipeline naming convention!

Use python bench_models.py to benchmark pipelines with datasets and produce a report.

Reports are stored in ./benchmarks/<benchmark_name>

Additional parameters are:

• -n benchmark name, default is benchmark

A pipeline is a python file exposing TARGET, PARAMETER_GRID and estimator attributes.

• TARGET is the column from the target dataset to use for classification (problem definition)
• PARAMETER_GRID is the parameter grid used in grid search
• estimator is the estimator whose parameters are to be tuned

The pipeline's estimator can also be a wrapper method such as RFE or SelectFromModel, in this case the class lib.selection_pipeline.SelectionPipeline should be used instead of sklearn.pipeline.Pipeline to expose the required attributes.