Blog
Insights
Technical writing from our team. We cover what we are building, what worked, and what did not.
Why We Moved From LSTMs to Gradient-Boosted Trees for Equity Signals
Our LSTM models looked great on backtest but degraded fast in live trading. Here is why XGBoost with hand-crafted features outperformed deep learning for our ASX signal pipeline.
Read articleMeasuring Crypto Sentiment: What Actually Predicts Price Movement
We tested 14 sentiment features derived from Reddit, X, and on-chain data. Only 3 had statistically significant predictive power after transaction costs. Here are the results.
Read articleHow We Handle Model Drift in a Market That Never Sits Still
Markets change regimes. Our models retrain nightly, but that is not enough. We built a drift detection system that triggers emergency retraining when feature distributions shift beyond thresholds.
Read articleShipping Echo: Lessons From Building a Native Finance App in 10 Weeks
We built Echo (iOS + Android) in 10 weeks with a team of three. Here is what we got right, what we got wrong, and what we would do differently next time.
Read articleOur Kafka Setup for Sub-200ms Market Data Ingestion
A walkthrough of our streaming architecture: Kafka topics per exchange, Avro schemas, exactly-once semantics, and the backfill system that saved us during the March outage.
Read articleFeature Engineering for Financial ML: What We Use and Why
The 23 features that actually matter in our equity signal models. Includes rolling volatility ratios, order flow imbalance, and why we stopped using RSI.
Read articleWant to work with our team?
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