intent-solutions-io/nixtla-liquidity-forecaster

Liquidity Forecaster

Predicts future orderbook depth and bid-ask spreads using historical market data and TimeGPT.

Overview

This skill analyzes historical trade data and orderbook snapshots from Polymarket to forecast liquidity conditions. It predicts near-term changes in orderbook depth and bid-ask spreads, helping determine optimal trade execution timing. The workflow fetches data via Polymarket API, preprocesses it for TimeGPT compatibility, and generates forecasts with visualizations and reports.

When to use: Determining optimal trade execution timing based on expected liquidity conditions, predicting orderbook depth changes, estimating bid-ask spread evolution.

Trigger phrases: "forecast liquidity", "predict orderbook depth", "estimate spread changes", "analyze market liquidity", "forecast trading conditions".

Prerequisites

Required environment variables:

• NIXTLA_TIMEGPT_API_KEY - Your Nixtla TimeGPT API key

Python packages:

pip install nixtla pandas requests matplotlib

Required tools: Read, Write, Bash, Glob, Grep, WebFetch

Minimum Python version: 3.8+

Instructions

Step 1: Fetch orderbook data

Fetch historical orderbook data from Polymarket API using the market ID. The script retrieves bids and asks, combines them into a single dataset, and saves to CSV format.

Script: {baseDir}/scripts/fetch_data.py

Usage:

python {baseDir}/scripts/fetch_data.py --market_id <MARKET_ID> [--output orderbook_data.csv]

Parameters:

• --market_id (required): Polymarket market identifier
• --output (optional): Output CSV file path (default: orderbook_data.csv)

Output: Raw orderbook data CSV with columns: price, quantity, side

Step 2: Preprocess data

Clean and format orderbook data for TimeGPT input. The script calculates mid-price, spread, and depth metrics, then formats the data according to Nixtla's schema requirements.

Script: {baseDir}/scripts/preprocess_data.py

Usage:

python {baseDir}/scripts/preprocess_data.py --input_file orderbook_data.csv [--output preprocessed_data.csv]

Parameters:

• --input_file (required): Path to raw orderbook CSV from Step 1
• --output (optional): Output CSV file path (default: preprocessed_data.csv)

Output: Preprocessed data CSV with Nixtla format (unique_id, ds, y, spread, depth)

Step 3: Execute forecast

Run TimeGPT forecast on preprocessed data. The script generates predictions for the specified horizon, creates visualizations, and produces a summary report.

Script: {baseDir}/scripts/forecast_liquidity.py

Usage:

python {baseDir}/scripts/forecast_liquidity.py --input_file preprocessed_data.csv --horizon <PERIODS> [--output depth_forecast.csv] [--plot_prefix depth]

Parameters:

• --input_file (required): Path to preprocessed CSV from Step 2
• --horizon (required): Number of periods to forecast
• --output (optional): Output forecast CSV path (default: depth_forecast.csv)
• --plot_prefix (optional): Prefix for plot filename (default: depth)

Output:

• Forecast CSV with predicted values
• PNG plot showing historical data and forecast
• Text report summarizing the forecasting process

Step 4: Interpret results

Review the generated outputs to understand predicted liquidity conditions. The forecast CSV contains time-indexed predictions, the plot visualizes trends, and the report provides metadata about the forecasting run.

Output

Generated files:

• depth_forecast.csv - Time-series predictions for orderbook depth and mid-price
• depth_forecast.png - Visualization showing historical data and forecast overlay
• report.txt - Summary report with market ID, horizon, and output file paths

CSV format: Columns include unique_id, ds (timestamp), y (predicted mid-price), and optional spread/depth metrics.

Error Handling

Invalid Polymarket Market ID Cause: Market ID not recognized by Polymarket API Solution: Verify the market ID at https://polymarket.com or check API documentation

TimeGPT API Key missing Cause: NIXTLA_TIMEGPT_API_KEY environment variable not set Solution: Export your API key: export NIXTLA_TIMEGPT_API_KEY=your_key_here

Insufficient data from Polymarket API Cause: Empty or incomplete orderbook data for the specified market Solution: Check data availability for the market ID, try a different market, or verify API endpoint

TimeGPT forecast failed Cause: Input data format issues or API connection problems Solution: Verify preprocessed data has required columns (unique_id, ds, y), check API status, ensure data types are correct

Missing required columns Cause: Raw orderbook data lacks price, quantity, or side columns Solution: Verify Polymarket API response structure matches expected format, check for API changes

Examples

Example 1: Forecast depth for presidential election market

Predict orderbook depth 6 periods ahead for a political prediction market.

Commands:

python {baseDir}/scripts/fetch_data.py --market_id trump_election_2024
python {baseDir}/scripts/preprocess_data.py --input_file orderbook_data.csv
python {baseDir}/scripts/forecast_liquidity.py --input_file preprocessed_data.csv --horizon 6

Expected output: depth_forecast.csv with 6 forecasted depth values, plot showing trend, summary report.

Example 2: Forecast spread for cryptocurrency market

Predict bid-ask spread 24 periods ahead for an Ethereum price prediction market.

Commands:

python {baseDir}/scripts/fetch_data.py --market_id eth_price_3000 --output eth_orderbook.csv
python {baseDir}/scripts/preprocess_data.py --input_file eth_orderbook.csv --output eth_preprocessed.csv
python {baseDir}/scripts/forecast_liquidity.py --input_file eth_preprocessed.csv --horizon 24 --output eth_spread_forecast.csv --plot_prefix eth_spread

Expected output: eth_spread_forecast.csv with 24 forecasted values, plot named eth_spread_forecast.png, summary report.

Example 3: Quick workflow for sports outcome market

End-to-end workflow for a sports prediction market using default file names.

Commands:

python {baseDir}/scripts/fetch_data.py --market_id superbowl_winner_2025
python {baseDir}/scripts/preprocess_data.py --input_file orderbook_data.csv
python {baseDir}/scripts/forecast_liquidity.py --input_file preprocessed_data.csv --horizon 12

Expected output: Standard output files (depth_forecast.csv, depth_forecast.png, report.txt) with 12-period forecast.

Resources

Nixtla documentation:

• TimeGPT API reference: https://docs.nixtla.io/
• Data format requirements: https://docs.nixtla.io/docs/tutorials-forecasting_with_timegpt

Polymarket API:

• API documentation: https://docs.polymarket.com/
• Market explorer: https://polymarket.com/markets

Related skills:

• nixtla-timegpt-lab - General TimeGPT forecasting workflows
• nixtla-schema-mapper - Data format transformation utilities