ForexGrand Strategy Modelling Core

ForexGrand Strategy Modelling Core is a Python package for building forex strategy modelling workflows. It includes utilities for loading market data from Cloudflare R2, generating technical indicators, preparing TFRecord datasets, training models, evaluating model quality, and packaging trained models for deployment.

The distribution name is fg-strategy-modelling-core; the Python import package is forexgrand_core.

Features

• Cloudflare R2-backed storage access through the S3-compatible API.
• A runtime configuration helper so users do not have to manually export environment variables.
• Data loading and local caching for symbol market data.
• TensorFlow-based technical indicators for feature engineering.
• Dataset generation utilities for train, evaluation, and test workflows.
• Training pipelines for neural-network, KNN, XGBoost, and no-train target models.
• Model evaluation, performance checks, and model publishing helpers.
• Extensible preprocessing classes for custom feature pipelines.

Installation

Install from PyPI:

pip install fg-strategy-modelling-core

Install from source for development:

git clone https://github.com/forexgrand/fg-strategy-modelling-core.git
cd fg-strategy-modelling-core
pip install -e ".[dev]"

Configure Cloudflare R2

The package currently supports Cloudflare R2 storage. Configure it at the start of your script with configure_r2:

from forexgrand_core import configure_r2

settings = configure_r2(
    account_id="your-cloudflare-account-id",
    access_key_id="your-r2-access-key-id",
    secret_access_key="your-r2-secret-access-key",
    bucket_name="forexgrand-data",
    train_bucket_name="forexgrand-train",
    eval_bucket_name="forexgrand-eval",
    test_bucket_name="forexgrand-test",
    model_upload_bucket="forexgrand-models",
)

You can pass endpoint="https://<account-id>.r2.cloudflarestorage.com" instead of account_id if you already have the full endpoint.

configure_r2 sets these environment variables for the current Python process and returns a fresh Settings object:

If bucket-specific names are omitted, bucket_name is reused for all buckets.

Load Market Data

from forexgrand_core import configure_r2
from forexgrand_core.data_manager import DataManager

configure_r2(
    account_id="your-cloudflare-account-id",
    access_key_id="your-r2-access-key-id",
    secret_access_key="your-r2-secret-access-key",
    bucket_name="forexgrand-data",
)

manager = DataManager(base_bucket_name="forexgrand-data")
df, properties = manager.load_data(
    symbol_pair="EURUSD",
    instrument_group="forex",
)

Use Technical Indicators

from forexgrand_core.indicators import tf_atr, tf_ma, tf_rsi

df["MA_20"] = tf_ma(df, period=20, column="close")
df["ATR_14"] = tf_atr(df, period=14)
df["RSI_14"] = tf_rsi(df, period=14, column="close")

Available indicators include moving average, slope, ATR, RSI, standard deviation, Bollinger Bands, Garman-Klass volatility, wick-to-range ratio, and normalization helpers.

Train Models

from forexgrand_core import configure_r2
from forexgrand_core.main import run_training
from forexgrand_core.pipeline.preprocessing.example_preprocessor import ExamplePreprocessor

configure_r2(
    account_id="your-cloudflare-account-id",
    access_key_id="your-r2-access-key-id",
    secret_access_key="your-r2-secret-access-key",
    bucket_name="forexgrand-data",
    model_upload_bucket="forexgrand-models",
)

results = run_training(
    symbols=["EURUSD", "GBPUSD", "USDJPY"],
    model_types=["conservative", "simple", "complex"],
    preprocessor_class=ExamplePreprocessor,
    sequence_length=60,
)

Custom Preprocessing

Create your own preprocessing class by extending PreprocessBase:

from forexgrand_core.pipeline.preprocessing.base_preprocessor import PreprocessBase


class MyPreprocessor(PreprocessBase):
    def preprocess(self, inputs, training: bool = False):
        return inputs

    def features_metadata(self):
        return {}

Use the class in the training pipeline:

results = run_training(
    symbols=["EURUSD"],
    model_types=["simple"],
    preprocessor_class=MyPreprocessor,
    sequence_length=60,
)

Validate Configuration

Configuration is not validated on package import, so import forexgrand_core works before credentials are available. Validate explicitly when you want a clear setup error:

from forexgrand_core.env_validator import validate_environment_on_import

validate_environment_on_import()

Build And Publish To PyPI

Install build tools:

python -m pip install --upgrade build twine

Build the source distribution and wheel:

python -m build

Check the package:

python -m twine check dist/*

Upload to TestPyPI first:

python -m twine upload --repository testpypi dist/*

Upload to PyPI:

python -m twine upload dist/*

Development

Run tests:

pytest

Build locally:

python -m build

Check imports:

import forexgrand_core
from forexgrand_core import configure_r2, Settings

License

MIT