Backtesting & Optimization

Critical Disclaimer

Past performance does not guarantee future results. Backtesting results are hypothetical and have inherent limitations:

• Overfitting Risk: Parameters that perform well historically may fail in live trading
• Market Dynamics: Conditions, liquidity, and correlations change over time
• Execution Reality: Slippage, fees, and market impact may exceed modeled estimates
• Survivorship Bias: Historical data may exclude delisted/failed assets
• Data Mining: Testing multiple strategies increases the chance of finding spurious patterns

Always validate strategies with out-of-sample data, paper trading, and start with minimal capital when going live.

Test your strategies on historical data before risking real capital. cc-liquid provides powerful backtesting tools to analyze performance and optimize parameters.

Data Requirements

You need to bring your own datasets for backtesting. cc-liquid requires two data files:

1. Historical price data (raw_data.parquet) - Daily closing prices for all assets
2. Historical predictions (predictions.parquet) - Your prediction scores/signals for each asset

See Data requirements section below for detailed format specifications.

Quick start

Basic backtest

Once you have prepared your data files, run a backtest with your current configuration:

# Ensure you have these files in your working directory:
# - raw_data.parquet (price history)  
# - predictions.parquet (your signals)

cc-liquid analyze

This uses price data from raw_data.parquet (must exist), predictions from predictions.parquet or your configured data source, and portfolio settings from cc-liquid-config.yaml.

Parameter optimization

Find optimal portfolio parameters using grid search:

cc-liquid optimize

Analyze command

The analyze command runs a single backtest with specified parameters.

Basic usage

# Use config defaults
cc-liquid analyze

# Override specific parameters
cc-liquid analyze --set portfolio.num_long=15 --set portfolio.num_short=5 --set portfolio.target_leverage=2.0

# Custom date range
cc-liquid analyze --start-date 2024-01-01 --end-date 2024-06-30

# Switch prediction column and rebalancing parameters
cc-liquid analyze --set data.prediction_column=pred_10d --set portfolio.rebalancing.every_n_days=10

# Show position details and save daily results
cc-liquid analyze --show-positions --save-daily results.csv

Parameters

Parameter	Default	Description
--prices	raw_data.parquet	Path to historical price data
--start-date	Auto	Backtest start date (YYYY-MM-DD)
--end-date	Auto	Backtest end date (YYYY-MM-DD)
--set	None	Override config values (e.g., --set portfolio.num_long=15)
--fee-bps	4.0	Trading fees in basis points
--slippage-bps	50.0	Slippage costs in basis points
--show-positions	False	Show detailed position data
--save-daily	None	Save daily returns to CSV
--verbose	False	Show detailed progress

Output metrics

The backtest displays comprehensive performance metrics including total return and CAGR, volatility and max drawdown, risk-adjusted metrics (Sharpe, Sortino, Calmar ratios), trading statistics (win rate, turnover), and final equity value.

Example output:

Optimize command

Optimization Warning

Parameter optimization is particularly susceptible to overfitting. The more parameters and combinations you test, the higher the risk of finding parameters that only work historically. Always reserve out-of-sample data for validation, test parameter stability across different periods, be skeptical of unrealistic returns, and consider transaction costs and market impact.

The optimize command runs parallel grid search to find optimal parameters.

Basic usage

# Requires raw_data.parquet and predictions.parquet to exist
# Default optimization
cc-liquid optimize

# Custom parameter ranges
cc-liquid optimize \
  --num-longs "10,20,30" \
  --num-shorts "0,5,10" \
  --leverages "1.0,2.0,3.0" \
  --rebalance-days "7,14,21" \
  --rank-powers "0.0,0.5,1.0,1.5,2.0"

# Optimize for specific metric
cc-liquid optimize --metric calmar

# Apply drawdown constraint
cc-liquid optimize --max-drawdown 0.20  # Max 20% drawdown

# Run best parameters immediately
cc-liquid optimize --apply-best

Parameters

Parameter	Default	Description
--prices	raw_data.parquet	Path to historical price data
--set	None	Override config values (e.g., --set data.source=numerai)
--num-longs	"10,20,30,40,50"	Long positions to test
--num-shorts	"10,20,30,40,50"	Short positions to test
--leverages	"1.0,2.0,3.0,4.0,5.0"	Leverage levels to test
--rebalance-days	"8,10,12"	Rebalance frequencies to test
--rank-powers	"0.0,0.5,1.0,1.5,2.0"	Rank power values to test (0=equal weight)
--metric	sharpe	Optimization target: sharpe, cagr, calmar
--max-drawdown	None	Maximum drawdown constraint
--top-n	20	Show top N results
--apply-best	False	Run full analysis with best params
--save-results	None	Save results to CSV
--plot	False	Show contour plots
--max-workers	Auto	Parallel workers (default: CPU count)
--clear-cache	False	Clear optimization cache
--verbose	False	Show detailed progress

Optimization metrics

Choose your optimization target based on your goals: sharpe for best risk-adjusted returns (default), cagr for maximum absolute returns, or calmar for best returns relative to drawdown.

Position Weighting in Optimization

The optimizer tests different position weighting concentrations using --rank-powers. All optimizations use the rank_power scheme where:

• 0.0 = equal weighting (all positions same size)
• 0.5-1.0 = mild concentration in top-ranked positions
• 1.5-2.0 = moderate concentration
• 2.0+ = heavy concentration

This replaces the need for a separate weighting_scheme parameter. See Portfolio Weighting for visual explanations of concentration effects.

Parallel execution

The optimizer uses multiprocessing to test parameter combinations in parallel. It automatically detects CPU cores (capped at 24), caches results to speed up repeated runs, and shows real-time progress with best parameters found.

Example output:

Caching

Results are cached in .cc_liquid_optimizer_cache.json to speed up repeated optimizations and preserve results across sessions. Clear with the --clear-cache flag when needed.

Applying best parameters

After optimization, apply the best parameters:

# Option 1: Run immediately
cc-liquid optimize --apply-best

# Option 2: Update config manually
# Copy best parameters from output to cc-liquid-config.yaml

Data requirements

Bring Your Own Data

cc-liquid does not provide historical data. You must source your own price history and prediction signals for backtesting.

Price data (raw_data.parquet)

Historical daily price data for all assets you want to trade. This file must contain:

Column	Type	Description	Example
date	datetime	Price observation date	2024-01-15
id	string	Asset identifier (must match Hyperliquid symbols)	"BTC", "ETH", "SOL"
close	float	Closing price in USD	42500.00

Example structure:

# DataFrame should look like:
#        date    id      close
# 0  2024-01-01  BTC   42000.00
# 1  2024-01-01  ETH    2250.00
# 2  2024-01-01  SOL      95.50
# 3  2024-01-02  BTC   43500.00
# ...

Data requirements:

• Must cover the full backtest period for all assets
• Daily frequency (one price per asset per day)
• Asset IDs must exactly match Hyperliquid ticker symbols
• Prices should be in USD
• Can include more assets than you have predictions for (extras will be ignored)

Advanced usage

Custom data sources

Test with different prediction files or prediction timeframes:

# Test Numerai predictions
cc-liquid analyze --set data.source=numerai

# Test 10d predictions
cc-liquid analyze --set data.prediction_column=pred_10d

Cost analysis

Adjust trading costs:

cc-liquid analyze --fee-bps 5 --slippage-bps 10

Red flags

Watch for signs of overfitting:

• Sharpe ratio > 3 (too good to be true?)
• Performance degrades sharply outside optimum
• Large discrepancy between periods
• Performance doesn't generalize when adding prediction-lags

Disclaimer

Past performance does not guarantee future results. Backtests are simplified models that may not capture all market dynamics, execution challenges, or black swan events.