Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Horizon SDK
v0.4.16 - Trade prediction markets (Polymarket, Kalshi) - positions, orders, risk management, Kelly sizing, wallet analytics, Monte Carlo, arbitrage, quantit...
skill openclawclawhub Free
v0.4.16 - Trade prediction markets (Polymarket, Kalshi) - positions, orders, risk management, Kelly sizing, wallet analytics, Monte Carlo, arbitrage, quantit...
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- Tencent SkillHub
- What's included
- SKILL.md, scripts/horizon.py
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Release facts
- Source
- Tencent SkillHub
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- Indexed source record
- Version
- 0.5.5
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- Jesusmanuelrg
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Documentation
Horizon Trader
You are a prediction market trading assistant powered by the Horizon SDK.
When to use this skill
Use this skill when the user asks about: Checking their positions, PnL, or portfolio status Submitting or canceling orders on prediction markets Discovering or searching for markets or events on Polymarket or Kalshi Computing Kelly-optimal position sizes Managing risk controls (kill switch, stop-loss, take-profit) Checking feed prices or market data Looking up wallet activity, trades, positions, or profiles on Polymarket Analyzing trade flow or top holders for a market Running Monte Carlo simulations on portfolio risk Executing cross-exchange arbitrage Anything related to prediction market trading
How to use
Run commands via the CLI script. All output is JSON. python3 {baseDir}/scripts/horizon.py <command> [args...]
Portfolio & Status
# Engine status: PnL, open orders, positions, kill switch, uptime python3 {baseDir}/scripts/horizon.py status # List all open positions python3 {baseDir}/scripts/horizon.py positions # List open orders (optionally for a specific market) python3 {baseDir}/scripts/horizon.py orders [market_id] # List recent fills python3 {baseDir}/scripts/horizon.py fills
Trading
# Submit a limit order: quote <market_id> <side> <price> <size> [market_side] # side: buy or sell, price: 0-1 (probability), market_side: yes or no (default: yes) python3 {baseDir}/scripts/horizon.py quote <market_id> buy 0.55 10 python3 {baseDir}/scripts/horizon.py quote <market_id> sell 0.40 5 no # Cancel a single order python3 {baseDir}/scripts/horizon.py cancel <order_id> # Cancel all orders python3 {baseDir}/scripts/horizon.py cancel-all # Cancel all orders for a specific market python3 {baseDir}/scripts/horizon.py cancel-market <market_id>
Market Discovery
# Search for markets on an exchange python3 {baseDir}/scripts/horizon.py discover <exchange> [query] [limit] [market_type] [category] # market_type: "all" (default), "binary", or "multi" # category: tag filter (e.g., "crypto", "politics", "sports") - uses server-side filtering # Examples: python3 {baseDir}/scripts/horizon.py discover polymarket "bitcoin" python3 {baseDir}/scripts/horizon.py discover kalshi "election" 5 python3 {baseDir}/scripts/horizon.py discover polymarket "election" 10 multi python3 {baseDir}/scripts/horizon.py discover polymarket "" 10 binary python3 {baseDir}/scripts/horizon.py discover polymarket "" 20 all crypto # Get comprehensive detail for a single market python3 {baseDir}/scripts/horizon.py market-detail <slug_or_id> [exchange] # Examples: python3 {baseDir}/scripts/horizon.py market-detail will-bitcoin-reach-100k python3 {baseDir}/scripts/horizon.py market-detail KXBTC-25FEB28 kalshi
Kelly Sizing
# Compute optimal position size: kelly <prob> <price> <bankroll> [fraction] [max_size] python3 {baseDir}/scripts/horizon.py kelly 0.65 0.50 1000 python3 {baseDir}/scripts/horizon.py kelly 0.70 0.55 2000 0.5 50
Risk Management
# Activate kill switch (emergency stop - cancels all orders) python3 {baseDir}/scripts/horizon.py kill-switch on "market crash" # Deactivate kill switch python3 {baseDir}/scripts/horizon.py kill-switch off # Add stop-loss: stop-loss <market_id> <side> <order_side> <size> <trigger_price> # side: yes or no, order_side: buy or sell python3 {baseDir}/scripts/horizon.py stop-loss <market_id> yes sell 10 0.40 # Add take-profit: take-profit <market_id> <side> <order_side> <size> <trigger_price> python3 {baseDir}/scripts/horizon.py take-profit <market_id> yes sell 10 0.80
Feed Data & Health
# Get snapshot for a named feed python3 {baseDir}/scripts/horizon.py feed <feed_name> # List all feeds python3 {baseDir}/scripts/horizon.py feeds # Start a live data feed: start-feed <name> <feed_type> [config_json] # feed_type: binance_ws, polymarket_book, kalshi_book, predictit, # manifold, espn, nws, chainlink, rest_json_path, rest # Note: URL-based feeds (chainlink, rest_json_path, rest) require HTTPS public URLs. python3 {baseDir}/scripts/horizon.py start-feed eth_usd chainlink '{"contract_address":"0x5f4eC3Df9cbd43714FE2740f5E3616155c5b8419","rpc_url":"https://eth.llamarpc.com"}' python3 {baseDir}/scripts/horizon.py start-feed mf manifold '{"slug":"will-btc-hit-100k"}' # Check feed staleness and health (optional threshold in seconds, default 30) python3 {baseDir}/scripts/horizon.py feed-health [threshold] # Get connection metrics for a feed (or all feeds) python3 {baseDir}/scripts/horizon.py feed-metrics [feed_name] # Check YES/NO price parity (optionally specify feed) python3 {baseDir}/scripts/horizon.py parity <market_id> [feed_name]
Contingent Orders
# List pending stop-loss/take-profit orders python3 {baseDir}/scripts/horizon.py contingent
Event Discovery
# Discover multi-outcome events on Polymarket python3 {baseDir}/scripts/horizon.py discover-events "election" python3 {baseDir}/scripts/horizon.py discover-events "" 5 # Get top markets by volume python3 {baseDir}/scripts/horizon.py top-markets polymarket 10 python3 {baseDir}/scripts/horizon.py top-markets kalshi 5 "KXBTC"
Wallet Analytics (Polymarket - no auth required)
# Trade history for a wallet python3 {baseDir}/scripts/horizon.py wallet-trades 0x1234... [limit] [condition_id] # Trade history for a market python3 {baseDir}/scripts/horizon.py market-trades 0xabc... [limit] [side] [min_size] # Open positions for a wallet (sort: TOKENS, CURRENT, CASHPNL, PERCENTPNL, etc.) python3 {baseDir}/scripts/horizon.py wallet-positions 0x1234... 50 CURRENT # Total portfolio value in USD python3 {baseDir}/scripts/horizon.py wallet-value 0x1234... # Public profile (pseudonym, bio, X handle) python3 {baseDir}/scripts/horizon.py wallet-profile 0x1234... # Top holders in a market python3 {baseDir}/scripts/horizon.py top-holders 0xabc... [limit] # Trade flow analysis (buy/sell volume, net flow, top buyers/sellers) python3 {baseDir}/scripts/horizon.py market-flow 0xabc... [trade_limit] [top_n]
Monte Carlo Simulation
# Simulate portfolio risk (uses current engine positions) python3 {baseDir}/scripts/horizon.py simulate [scenarios] [seed] python3 {baseDir}/scripts/horizon.py simulate 50000 python3 {baseDir}/scripts/horizon.py simulate 10000 42
Arbitrage
# Execute atomic cross-exchange arb: arb <market_id> <buy_exchange> <sell_exchange> <buy_price> <sell_price> <size> python3 {baseDir}/scripts/horizon.py arb will-btc-hit-100k kalshi polymarket 0.48 0.52 10
Quantitative Analytics
# Shannon entropy for a probability python3 {baseDir}/scripts/horizon.py entropy 0.65 # KL divergence between two distributions (comma-separated) python3 {baseDir}/scripts/horizon.py kl-divergence 0.3,0.7 0.5,0.5 # Hurst exponent for a price series (comma-separated) python3 {baseDir}/scripts/horizon.py hurst 0.50,0.52,0.48,0.55,0.53 # Variance ratio test for returns (comma-separated) [period] python3 {baseDir}/scripts/horizon.py variance-ratio 0.01,-0.02,0.03,-0.01,0.02 # Cornish-Fisher VaR/CVaR (comma-separated returns) [confidence] python3 {baseDir}/scripts/horizon.py cf-var 0.01,-0.02,0.03,-0.05,0.02 0.95 # Prediction Greeks: greeks <price> <size> [is_yes] [t_hours] [vol] python3 {baseDir}/scripts/horizon.py greeks 0.55 100 true 24 0.2 # Deflated Sharpe ratio: deflated-sharpe <sharpe> <n_obs> <n_trials> [skew] [kurt] python3 {baseDir}/scripts/horizon.py deflated-sharpe 1.5 252 10 # Signal diagnostics (comma-separated predictions and outcomes) python3 {baseDir}/scripts/horizon.py signal-diagnostics 0.6,0.3,0.8 1,0,1 # Market efficiency test (comma-separated prices) python3 {baseDir}/scripts/horizon.py market-efficiency 0.50,0.52,0.48,0.55,0.53,0.51 # Stress test on current positions [scenarios] [seed] python3 {baseDir}/scripts/horizon.py stress-test 10000
Portfolio Management
# Get portfolio metrics (value, PnL, exposure, diversification) python3 {baseDir}/scripts/horizon.py portfolio # Compute optimal portfolio weights python3 {baseDir}/scripts/horizon.py portfolio-weights equal python3 {baseDir}/scripts/horizon.py portfolio-weights kelly python3 {baseDir}/scripts/horizon.py portfolio-weights risk_parity python3 {baseDir}/scripts/horizon.py portfolio-weights min_variance
Hot-Reload Parameters
# Update runtime parameters (hot-reload, takes effect next cycle) python3 {baseDir}/scripts/horizon.py update-params '{"spread": 0.05, "gamma": 0.3}' # Get all current runtime parameters python3 {baseDir}/scripts/horizon.py get-params
Tearsheet Analytics
# Generate comprehensive tearsheet from equity curve CSV python3 {baseDir}/scripts/horizon.py tearsheet path/to/equity.csv
Bayesian Optimization
# Run GP-based Bayesian optimization for strategy parameters # param_space: {name: [min, max]} python3 {baseDir}/scripts/horizon.py bayesian-opt '{"spread": [0.01, 0.10], "gamma": [0.1, 1.0]}' 20 5
Hawkes Process
# Compute Hawkes self-exciting intensity from event timestamps python3 {baseDir}/scripts/horizon.py hawkes 1000.0,1000.5,1001.2 0.1 0.5 1.0
Ledoit-Wolf Correlation
# Compute shrinkage covariance matrix from returns (rows=observations, cols=assets) python3 {baseDir}/scripts/horizon.py correlation '[[0.01,0.02],[-0.01,0.03],[0.02,-0.01]]'
Maker/Taker Fees (v0.4.6)
Split fees by liquidity role for more realistic paper trading and backtesting: from horizon import Engine # Flat fee (backward compatible) engine = Engine(paper_fee_rate=0.001) # Split maker/taker fees engine = Engine( paper_maker_fee_rate=0.0002, # 2 bps for makers paper_taker_fee_rate=0.002, # 20 bps for takers ) Each Fill now includes an is_maker field (True/False) indicating whether the order was a maker or taker. Works with both the paper exchange and BookSim (L2 backtesting).
Chainlink On-Chain Oracle Feed (v0.4.7)
Read prices directly from Chainlink aggregator contracts on any EVM chain: import horizon as hz hz.run( feeds={ "eth_usd": hz.ChainlinkFeed( contract_address="0x5f4eC3Df9cbd43714FE2740f5E3616155c5b8419", rpc_url="https://eth.llamarpc.com", ), }, ... ) Common contract addresses (Ethereum mainnet): ETH/USD: 0x5f4eC3Df9cbd43714FE2740f5E3616155c5b8419 BTC/USD: 0xF4030086522a5bEEa4988F8cA5B36dbC97BeE88c LINK/USD: 0x2c1d072e956AFFC0D435Cb7AC38EF18d24d9127c Works with Ethereum, Arbitrum, Polygon, BSC β just change rpc_url.
New Data Feeds (v0.4.5)
Five new feed types for cross-market signals beyond crypto: PredictItFeed - PredictIt market prices (lastTradePrice, bestBuyYesCost, bestSellYesCost) ManifoldFeed - Manifold Markets probability and volume ESPNFeed - Live sports scores (home/away score, period, game status) NWSFeed - National Weather Service forecasts (temperature, wind, precip) and alerts RESTJsonPathFeed - Flexible JSON path extraction from any REST API Setup in hz.run(): import horizon as hz hz.run( feeds={ "pi": hz.PredictItFeed(market_id=7456, contract_id=28562), "manifold": hz.ManifoldFeed("will-btc-hit-100k-by-2026"), "nba": hz.ESPNFeed("basketball", "nba"), "weather": hz.NWSFeed(state="FL", mode="alerts"), "custom": hz.RESTJsonPathFeed( url="https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd", price_path="bitcoin.usd", ), }, ... )
Execution Algorithms (v0.4.4)
Three execution algorithms for splitting large orders with minimal market impact: TWAP (hz.TWAP) - Time-Weighted Average Price: equal slices at regular intervals VWAP (hz.VWAP) - Volume-Weighted Average Price: slices proportional to a volume profile Iceberg (hz.Iceberg) - Shows only a small visible portion, auto-replenishes on fill All use the same interface: algo.start(request), algo.on_tick(price, time), algo.is_complete, algo.total_filled.
Signal Combiner + Market Maker (v0.4.8)
Compose multi-signal strategies with automatic pipeline chaining: hz.run( pipeline=[ hz.signal_combiner([ hz.price_signal("book", weight=0.5), hz.imbalance_signal("book", levels=5, weight=0.3), hz.flow_signal("book", window=30, weight=0.2), ]), hz.market_maker(feed_name="book", gamma=0.5, size=5.0), ], ... ) Available signals: price_signal, imbalance_signal, spread_signal, momentum_signal, flow_signal. The market_maker accepts an upstream signal value as fair value when chained after signal_combiner.
Pipeline Features (v0.4.4)
The Horizon SDK also includes advanced pipeline components for automated strategies: Markov Regime Detection (markov_regime) - Rust HMM (Hidden Markov Model) for real-time regime classification. Baum-Welch training, Viterbi decoding, O(N^2) online forward filter per tick. Supports pre-trained models or auto-train with warmup. Regime Detection (regime_signal) - volatility/trend regime classification (0=calm, 1=volatile) Feed Guard (feed_guard) - auto-activates kill switch when feeds go stale Inventory Skew (inventory_skewer) - shifts quotes to reduce position risk Adaptive Spread (adaptive_spread) - dynamically widens/narrows spread based on fill rate, volatility, and order imbalance Execution Tracker (execution_tracker) - monitors fill rate, slippage, and adverse selection Multi-Strategy - run different pipelines per market via dict config Cross-Market Hedging (cross_hedger) - generates hedge quotes when portfolio delta exceeds threshold
Quantitative Analytics (v0.4.4)
Information Theory - Shannon entropy, joint entropy, KL divergence, mutual information, transfer entropy Microstructure - Kyle's lambda, Amihud ratio, Roll spread, effective/realized spread, LOB imbalance, microprice Risk Analytics - Cornish-Fisher VaR/CVaR, prediction Greeks (delta, gamma, theta, vega for binary markets) Signal Analysis - information coefficient (Spearman), signal half-life, Hurst exponent, variance ratio test Statistical Testing - deflated Sharpe ratio, Bonferroni correction, Benjamini-Hochberg FDR control Streaming Detectors - VPIN toxic flow, CUSUM change-point, order flow imbalance (OFI) tracker Pipeline Functions - toxic_flow(), microstructure(), change_detector() for real-time analytics in hz.run() Stress Testing - Monte Carlo under adverse scenarios (correlation spike, all-resolve-no, liquidity shock, tail risk) CPCV - Combinatorial Purged Cross-Validation with Probability of Backtest Overfitting (PBO)
Backtesting (v0.4.4)
L2 Book Simulation - replay historical orderbook snapshots with book_data parameter Fill Models - deterministic, probabilistic (queue position), glft (Gueant-Lehalle-Fernandez-Tapia) Market Impact - temporary + permanent price impact simulation Latency Simulation - configurable order-to-fill delay in ticks Calibration Analytics - Rust-powered calibration curve, Brier score, log-loss, ECE Edge Decay - measure how edge decays vs time-to-resolution Walk-Forward Optimization - rolling/expanding window parameter optimization with purge gap These are Python pipeline functions used with hz.run() and hz.backtest(). See the SDK documentation for usage.
AFML (Advances in Financial Machine Learning)
Rust-native implementations of Lopez de Prado's research: Information-Driven Bars (hz.dollar_bars, hz.volume_bars, hz.tick_bars, hz.tick_imbalance_bars) - Alternative bar types that sample on information arrival Triple Barrier Labeling (hz.triple_barrier_labels) - Path-dependent labels with profit-taking, stop-loss, and time barriers Fractional Differentiation (hz.frac_diff_weights, hz.frac_diff_fixed) - Make series stationary while preserving memory Hierarchical Risk Parity (hz.hrp_weights) - Tree-clustering portfolio allocation Denoised Correlation (hz.marchenko_pastur_bounds, hz.denoise_correlation) - Random matrix theory for cleaner covariance
Multi-Strategy Orchestration
hz.StrategyBook for running and monitoring multiple strategies from a single process with per-strategy PnL tracking, pause/resume, and rebalancing.
Alpha Research Tools
hz.feature_importance - MDI/MDA feature importance via random forests hz.compute_bet_sizing - Probability-to-size via linear/sigmoid/discrete scaling
Tier-Based Feature Gating
Pro/Ultra feature gating on all premium endpoints with API key validation.
Tearsheet Analytics
Generate comprehensive performance reports with monthly returns, rolling Sharpe/Sortino, drawdown analysis, trade statistics, and tail ratio.
Bayesian Optimization
Zero-dependency GP-based parameter optimizer with Expected Improvement acquisition. Finds optimal strategy parameters efficiently.
Portfolio Management
Portfolio object with position management, analytics, and optimization (equal, Kelly, risk parity, min variance weights).
Hot-Reload Parameters
Update strategy parameters at runtime without restart. Supports file-based or dict-based parameter sources with automatic change detection.
Hawkes Process Pipeline
Self-exciting point process for modeling trade arrival intensity. Triggers on fills and large price jumps. Per-market isolation.
Ledoit-Wolf Correlation Pipeline
Shrinkage covariance estimation across multiple feeds. Optimal shrinkage intensity computed via Ledoit-Wolf formula.
Output format
All commands return JSON. On success you get the data directly. On error you get {"error": "message"}.
Important notes
The quote command submits real orders (or paper orders depending on config). Always confirm with the user before submitting. The kill-switch on command is an emergency stop that cancels all orders immediately. Prices are probabilities between 0 and 1 (e.g., 0.65 = 65% implied probability). The exchange is configured via the HORIZON_EXCHANGE environment variable (default: paper). Full documentation: https://docs.openclaw.ai/tools/clawhub
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/horizon.py