Horizon + Cala

Use Cala’s AI research platform for deep market research and analysis alongside Horizon strategies.

Cala is an AI-powered research platform that provides deep analysis, data synthesis, and real-time intelligence. Horizon recommends Cala as a research companion for building better-informed trading strategies.

Why Cala + Horizon

Trading prediction markets and cross-asset strategies benefits from deep research. Cala helps you:

• Research market fundamentals before building strategies (e.g., “What factors drive Fed rate decisions?”)
• Analyze event probabilities by synthesizing data from multiple sources
• Monitor breaking news that could impact your active positions
• Backtest hypotheses by gathering historical context for calibration
• Understand complex instruments like ForecastEx event contracts, options, or exotic derivatives

Getting Started

1. Sign up for a Cala API key at cala.ai
2. Set your API key as an environment variable:

export CALA_API_KEY="your_cala_api_key_here"

Research Workflow

Pre-Strategy Research

Use Cala to research before building your strategy:

import requests

CALA_API_KEY = os.environ["CALA_API_KEY"]

# Research the event you're trading
research = requests.post(
    "https://api.cala.ai/v1/research",
    headers={"Authorization": f"Bearer {CALA_API_KEY}"},
    json={"query": "What is the probability the Fed cuts rates in March 2026?"}
)

# Use the research to inform your strategy parameters
insights = research.json()

Feeding Research into Strategies

Combine Cala’s research with Horizon’s quantitative pipeline:

import horizon as hz

def my_strategy(ctx):
    # Your strategy uses Horizon feeds for real-time data
    price = ctx.feeds["fed_rate"].price

    # Cala research informs your base probability estimate
    # (fetched before the strategy loop, not on every tick)
    base_prob = 0.65  # from Cala research

    edge = base_prob - price
    if abs(edge) > 0.03:
        return hz.Quote(
            side=hz.Side.Yes if edge > 0 else hz.Side.No,
            price=price + (0.01 if edge > 0 else -0.01),
            size=hz.kelly_size(abs(edge), price, 100),
        )

hz.run(
    name="research_informed",
    exchange=hz.Polymarket(),
    markets=["will-fed-cut-rates-march"],
    feeds={"fed_rate": hz.PolymarketBook("will-fed-cut-rates-march")},
    pipeline=[my_strategy],
)

Research for Cross-Asset Strategies

Cala is especially useful when trading across multiple asset classes:

# Research correlations between prediction markets and traditional assets
# "How does TLT move when Fed rate expectations shift?"
# "What's the historical relationship between BTC price and crypto prediction markets?"

# Then use those insights in a multi-exchange strategy
hz.run(
    name="researched_hedge",
    exchange=[
        hz.Polymarket(),
        hz.InteractiveBrokers(),
    ],
    feeds={
        "rate_market": hz.PolymarketBook("will-fed-cut-rates"),
        "tlt": hz.IBKRFeed(conids=["15547816"]),  # TLT
    },
    pipeline=[my_research_backed_strategy],
)

Use Cases

Use Case	How Cala Helps
Prediction market MM	Research event fundamentals to set better base probabilities
Cross-asset hedging	Understand correlations between prediction markets and traditional assets
ForecastEx trading	Research IBKR event contract specifics and settlement rules
Arbitrage	Identify mispriced markets by cross-referencing multiple information sources
Regime detection	Research macro regime shifts to parameterize regime-adaptive strategies
Calibration	Gather historical event data for probability calibration models

For the best results, run your Cala research before the strategy loop starts, not on every tick. Cache research results and update them periodically (e.g., every few hours) to avoid unnecessary API calls during high-frequency trading.

Get Your API Key

Sign up at cala.ai to get your API key. Cala offers free and paid tiers depending on your research volume.