AlphaSee

AlphaSee

Inspiration

We started by diving into raw data across BTC, ETH, and SOL prediction markets to understand how prices actually move and settle.

Very quickly, we noticed something important: most signals look convincing until they don’t.

Instead of chasing a high win rate, we took a game theory approach and optimized for taking the right trades, not more trades.

That led us to a simple idea: only act when multiple independent signals agree.

So we built AlphaSee around four filters:

Real price movement Momentum confirmation Market mispricing Cross-asset agreement

We don’t try to outsmart the market. We just wait until it stops contradicting itself.

What it does

AlphaSee is a multi-signal trading strategy that trades crypto prediction markets with a focus on high-confidence opportunities.

It operates on BTC and ETH markets using:

Chainlink for ground truth price Binance for momentum Polymarket for pricing inefficiencies Cross-asset signals for confirmation

There are two core components:

Arbitrage If YES + NO < $1, we take both sides and accept our free money with gratitude.

Directional trades We only enter when all four signals align and the edge is large enough to matter.

We also built an interactive dashboard where you can:

See each signal independently Turn signals on and off and watch decisions change Explore performance across different timeframes Actually understand what the strategy is doing without reading code

How we built it

We tested more than 15 strategies:

Mean reversion Momentum-only Order book imbalance Fair value models

Some traded too often. Some looked great… until they didn’t.

By analyzing 5-minute, 15-minute, and hourly markets, we learned:

Short timeframes move fast but are noisy Longer timeframes are calmer but slower

The turning point was realizing that no single signal was trustworthy on its own.

So instead of picking the “best” signal, we made them agree first.

From there, we tuned thresholds, volatility assumptions, and capital allocation until the system became selective instead of reactive.

Challenges we ran into

Running backtests, waiting, changing one line, and running them again… many times Strategies that looked incredible right up until we tested them properly Trying to interpret market behavior late at night and suddenly having strong opinions about which signals we trust Balancing between “this trades too much” and “this does nothing”

Accomplishments that we're proud of

Building something that actually works outside of ideal conditions Getting strong returns without taking on uncontrolled risk Turning a chaotic set of ideas into a clean, explainable system Making a dashboard that people can interact with instead of just stare at Feeling slightly more qualified to use the word “quant”

What we learned

One good signal is helpful. Multiple agreeing signals are powerful Markets are noisy by default Validation matters more than first impressions Different timeframes require different thinking A strategy is only as good as your ability to explain it

What's next for AlphaSee

Add macro and energy-based signals to give more context Improve how we model volatility and size positions Expand to more assets and market types Move the dashboard toward real-time data Keep refining across different market conditions Consider taking it further… not saying hedge fund, but also not not saying it