Get quote for swapping USDC to $AGQNT via Uniswap Args: usdc_amount: Amount of USDC to swap

Calculate overall prediction performance metrics

Get predictions from last N days

Fetch Sentinel-2 NDVI via Sentinel Hub API. 10m resolution, 5-day revisit. Best for field-level analysis.

NASA SMAP (Soil Moisture Active Passive) soil moisture. Critical for drought detection 2-4 weeks before visible crop stress.

Fetch all $AGQNT token holders and their balances Returns: List of {address: str, balance: int, percentage: floa

Initialize system components

Load all historical freeze/hurricane events in date range

Test database operations

Test the Claude analysis engine

Run backtest from command line

Main entry point

Process complete Sentinel-2 scene for grove health Args: red_band: Red band data (Band 4) nir_band:

Record a trade in the history Args: prediction_id: Link to weather prediction entry_date: Trade entr

Full ENSO monitoring cycle

Execute full backtest

Run continuous monitoring on schedule

Save model and normalization parameters

Save trained models to disk

Save backtest results to JSON file

Simulate what the forecast would have looked like N days before event In production, would use archived NOAA forecasts

Store AI-generated prediction

Store weather forecast

Train the LSTM model Args: train_data: Training dataset val_data: Validation dataset epo

Record new quote

Record new trade

Meta-analysis: Have Claude validate its own prediction quality

Verify a prediction against actual USDA report

Check if any predictions can now be verified against USDA data This would run daily to check for new USDA reports

Analyze correlation between weather variables and OJ returns Args: temperature: Daily temperature series

Analyze weather patterns for crop disease disease pressure Based on Li et al. (2020) research on psyllid population dynamics

Create volume profile analysis (Volume-at-Price) Args: price_bins: Number of price levels to analyze

Generalized weather event analysis for any commodity/region. Wraps commodity-specific methods (analyze_freeze_event, analyze_

Assess crop disease disease risk Asian citrus psyllid (disease vector) thrives in: - Warm temps (75-85°F) -

Assess crop damage from hurricane Factors: - Wind damage (fruit drop, tree damage) - Flooding risk -

Assess price impact of a supply revision. Larger revisions in tighter markets = larger price moves.

Calculate beta (systematic risk) of OJ vs broader market Args: asset_returns: OJ returns market_retu

Calculate overall supply/demand balance Returns market tightness indicators

Calculate temperature anomaly from thermal infrared Used to detect heat/cold stress

Convert price prediction into trading signal Args: prediction: Output from predict_price_movement()

Calculate market volatility to adjust confidence High volatility → lower confidence in predictions

Collect all US grain region data

Collect weather data for all Brazilian coffee regions. Called every 15 minutes by main orchestrator.

Collect all West Africa cocoa region data

Analyze correlations with related soft commodities Args: oj_prices: ag commodity futures prices suga

Detect unusually aggressive buying or selling Args: sensitivity: Standard deviations for threshold

Detect freeze damage by comparing pre/post imagery Args: pre_freeze_ndvi: NDVI before freeze post_fr

Identify areas with significant vegetation stress Compares current NDVI to historical baseline Returns:

Download Planet Labs daily imagery Requires subscription (~$500/month)

Download Sentinel-2 imagery Args: bbox: Bounding box (min_lon, min_lat, max_lon, max_lat) date_range

Estimate days until price impact peaks Different events have different timelines: - Freeze: 3-7 days (quick reaction

Export all trades in CSV format for CPA audit

Export NDVI as color-coded health map For visualization and reporting

Negative log-likelihood for GARCH(1,1) estimation Args: params: [omega, alpha, beta] returns: Return

Generate complete multi-factor analysis Checks for freeze, hurricane, disease, drought simultaneously

Generate time series of grove health metrics For trend analysis and anomaly detection

Get USDT airdrop distribution history

Get token buyback history

Generate cross-commodity correlation report for dashboard. Returns the full matrix + highest/lowest pairs.

Estimate feature importance using gradient analysis Returns: Feature importance scores

Get airdrop history for a specific holder Args: wallet_address: Base wallet address Returns

Get summary of buybacks for a specific month Args: year: Year (e.g., 2026) month: Month (1-12)

Get all currently open positions

Get overall platform statistics

Get performance trend over time

Get historical price data

Get recent weather predictions

Get current token supply data

Get trading performance metrics

System health check

Load pre-trained model