btc-usd-consensus-1d

maverick90024/btc-usd-consensus-1d

btc-usd-consensus-1d is a machine learning model from maverick90024. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for temporal-forecasting.

This repository contains a consensus ensemble of 5 temporal transformer models for BTC-USD price forecasting.

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At a glance

Library: temporal-forecasting
Access: Public
Created: Nov 9, 2025
Updated: Nov 9, 2025
SHA: 1215c95e

Model card

From the Hugging Face model README

BTC-USD Consensus Ensemble (1d)

This repository contains a consensus ensemble of 5 temporal transformer models for BTC-USD price forecasting.

Overview

Instead of relying on a single model, this ensemble combines multiple models with different characteristics to produce robust, diverse forecasts. Each model specializes in different market patterns:

Momentum models: Capture trending behavior with shorter lookback windows
Balanced models: General-purpose forecasting with medium lookback windows
Mean reversion models: Detect overbought/oversold conditions with longer windows

Ensemble Members

How It Works

1. Independent Forecasting

Each model generates independent 7-period forecasts using its specific lookback window and focus strategy.

2. Consensus Analysis

Eight different consensus strategies analyze the ensemble's forecasts:

Gradient Strategy: Analyzes forecast momentum and direction changes
Confidence Strategy: Weighs predictions by model confidence scores
Timeframe Strategy: Balances short-term vs long-term predictions
Volatility Strategy: Adjusts for market volatility conditions
Mean Reversion Strategy: Identifies reversal opportunities
Acceleration Strategy: Detects accelerating price movements
Swing Strategy: Targets swing trading opportunities
Risk-Adjusted Strategy: Optimizes risk-reward ratios

3. Unified Signal

All strategy recommendations are aggregated into a final consensus signal with:

Action: BUY, SELL, or HOLD
Confidence Score: 0.0 to 1.0
Expected Return: Basis points (bps)

Usage

Import Individual Models

from strategies.model_cache import get_model_cache

cache = get_model_cache()

# Import a specific ensemble member
model_path, scaler_path, metadata = cache.import_from_huggingface(
    repo_id="maverick90024/btc-usd-consensus-1d",
    symbol="BTC-USD",
    interval="1d",
    lookback=30,  # Choose appropriate lookback
    focus="momentum",  # Choose appropriate focus
    forecast_horizon=7,
    path_in_repo="momentum_lookback30"  # Specify subdirectory
)

Run Consensus Analysis

from backend.main import run_consensus_analysis_worker
from backend.database import Database

async def run_analysis():
    db = Database()
    await db.connect()

    result = await run_consensus_analysis_worker(
        symbol="BTC-USD",
        horizons=[7],
        interval="1d",
        analysis_id="my-analysis",
        db=db
    )

    print(f"Consensus: {result['consensus']}")
    print(f"Action: {result['action']}")
    print(f"Confidence: {result['confidence']}")

Model Details

Symbol: BTC-USD
Interval: 1d
Forecast Horizon: 7 periods
Ensemble Size: 5 models
Architecture: Temporal Transformer
Training Framework: PyTorch
Export Date: 2025-11-09

License

GPL-3.0-or-later

Citation

@software{temporal_trading_ensemble,
  title = {BTC-USD Consensus Ensemble},
  author = {Unidatum Integrated Products LLC},
  year = {2025},
  url = {https://github.com/OptimalMatch/temporal-trading-agents}
}