Spécifications
We have a working price prediction engine for (BTC, ETH, SOL) at fixed 5-minute intervals and want to build a robust backtesting system that evaluates these predictions against MECX futures prices. The goal is to measure baseline directional accuracy and then explore conditional strategies that improve accuracy beyond a 60% milestone.
Data Overview
Predictions Table (public.price_predictions_5m)
One row per asset (BTC/ETH/SOL) and 5-minute interval.
Fields:
asset (text, constrained to BTC/ETH/SOL)
sample_time (when prediction is made)
target_time (the 5-minute future point it predicts)
predicted_price (numeric)
created_at (ingest timestamp, ~1s after sample_time)
id (bigint PK).
No ground truth is stored here.
Ground Truth (to be ingested):
- MECX futures prices for BTC/ETH/SOL at 5-minute resolution (OHLCV).
- For evaluation, target_time in the predictions table must align with the close price of the corresponding futures bar.
Requirements:
Baseline Evaluation
Match each prediction to the MECX futures close at target_time.
Classify up/down relative to the open price at sample_time.
Report unconditional directional accuracy for each asset (5m horizon).
Conditional Backtesting:
Build a framework to test filters that conditionally accept/reject predictions, such as:
Magnitude filters: relative predicted change vs. open price.
Volatility-adjusted filters: predicted move scaled by realized volatility.
Trend confirmation: prediction direction agrees with EMA/ADX indicators.
Consistency: agreement with prior predictions or cross-asset signals.
Time-of-day filters.
Produce accuracy vs coverage curves to show how accuracy improves when fewer, stronger predictions are selected.
Validation & Reporting:
Use rolling walk-forward validation to avoid look-ahead bias.
Provide outputs per asset and horizon: baseline accuracy, conditional accuracy, coverage %, confusion metrics, and significance intervals.
Deliver accuracy-versus-coverage charts to visualize trade-offs.
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