Quantize NewsPredict Alpha
Stop guessing. Use RL-aligned agents to predict volatility and automate portfolio rotation. A professional-grade platform for strategy development.
Backtest Results
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Live Monitor
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Strategy Editor
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Built for Modern Quants
A complete ecosystem for researching, backtesting, and deploying agent-based strategies.
News-Aligned LLMs
NewTrain models on price-aligned news data using RLVR rewards to forecast return quantiles with institutional precision.
Backtesting Engine
Event-driven simulations with realistic slippage, liquidity models, and transaction costs.
Portfolio Optimization
Mean-Variance and Black-Litterman models to find the efficient frontier.
Bandit Algorithms
Reinforcement learning to auto-balance exploration vs exploitation.
Market Screener
Filter universe by fundamental ratios and technical signals.
Risk Management
Hard constraints on drawdown, exposure, and beta.
Live Performance
Real-time leaderboard of deployed strategies.
| Strategy | Return | Sharpe | Avg Hold | Trend (1D) |
|---|---|---|---|---|
| Live performance is warming up. Check back shortly. | ||||
Price-Aligned News & Quantile Forecasting
Standard sentiment analysis is noisy. We use Reinforcement Learning from Verifiable Rewards (RLVR) to align large language models directly with market returns.
- 01Ingest news from 50+ financial sources
- 02Output probability distributions (quantiles)
- 03Execute on high-confidence P90/P10 divergences
# Initialize Strategy Ensemble
bandit = ContextualBandit(
arms=[
MeanReversion(lookback=20),
TrendFollowing(ema_span=50),
SentimentModel(source="reuters")
],
policy="LinUCB"
)
# Adaptive Allocation
allocation = bandit.select_arm(live_stream)Real-time Weight Adjustment
Updating every tickSolve the Non-Stationarity Problem
What worked yesterday often fails today. Markets shift between regimes: trends, volatility, liquidity. Instead of relying on a static strategy, we treat every signal as an "arm" in a Multi-Armed Bandit problem.
Smart Exploitation
The system automatically shifts heavy capital allocation to the "hot hand". Reward functions (e.g. Sharpe ratio) allow for regime specialization.
Continuous Exploration
Minimize regret by testing underperforming strategies with small capital, ensuring you never miss a regime shift.
Contextual Bandits
Unlike simple bandits, our agents observe market context (volatility, sentiment, volume) to predict which arm will perform best before the move happens.
Ready to get started?
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