Learn about trading strategies, data engineering, and ML applications from the APIndicators team.
A walkthrough of the Rails + Redis rate limiting pattern powering APIndicators: daily counters, 24h TTL, per-plan quotas, and how to benchmark it under load.
Run backtests via API without wiring up a whole framework. This walkthrough uses APIndicators simulated positions to fetch signals, track PnL, and compute win rate, Sharpe, and max drawdown.
Binance lists 473 perpetual futures pairs. Most retail algo traders only need 15-30 of them. Here is how to filter by volume, spread, and dead-pair signals with SQL and Python.
APIndicators uses Claude Sonnet 4.6 to explain signals in plain English, produce market briefings, and power a strategy chat. Here is how to integrate those endpoints into your trading workflow.
TradingView alerts are great for manual traders. Programmatic webhook-based signals are what production bots need. Here is how to migrate, with Pine Script to Python examples.
A strategy that wins in trends often loses in chop. Classify the market regime first, then route to the right strategy. Here is how APIndicators detects regimes via the market-type endpoint.
A good strategy with bad sizing blows up. Kelly, fixed fractional, and volatility-adjusted sizing each have specific strengths. Here is how to pick and implement in Python.
Random k-fold leaks future data into training, producing inflated metrics that collapse in production. Here is why walk-forward validation is the only honest way to evaluate time-series ML models.
Textbook rules were written for stock markets that close. Here is how three classic indicators actually behave in 24/7 crypto futures, with real BTCUSDT examples.
A complete walkthrough: sign up for APIndicators, grab an API key, fetch RSI and EMA signals for any Binance Futures pair, and wire up a webhook handler. Real code, no fluff.
How APIndicators compares to TradingView API, CoinAPI, and raw Binance data for building algo crypto trading bots. 20K calls/day, 470+ pairs, ML predictions, AI explainer.
Real numbers from APIndicators V2 since Feb 18, 2026: 973 trades, 59.0% BUY WR, +0.319% avg BUY. How walk-forward training, EV filters, and honest SELL metrics shaped production.
A developer tutorial on HMAC-SHA256 webhook signing, retry policies, delivery logs, and signature validation in Python, Node, and Ruby. Ship webhooks that clients can actually trust.
A counterintuitive production finding: AUC 0.50 is misleading because signal lives in the tails, and isotonic calibration destroys it. Use raw sigmoid 1/(1+exp(-logit)) instead.
A deep dive into APIndicators V2: LightGBM + XGBoost + CatBoost trained on walk-forward folds, weighted by OOS AUC, with NaN fallback. Live since Feb 18, 2026.
Autonomous AI agents now execute thousands of trades on prediction markets, outperform human traders, and manage DeFi portfolios 24/7. Here is what is working, what broke, and what it means for your trading stack.
Stop guessing where to put 'use client'. Learn the seven Server Component patterns that eliminate unnecessary JavaScript, speed up your pages, and simplify your data fetching in Next.js.
Funding rates are the mechanism that keeps perpetual futures prices anchored to spot. Learn how they work, why they matter for your PnL, and how to use them as a trading signal.
Rolling window features transform raw price and volume data into predictive signals for ML models. Learn how to build robust rolling features in Python using pandas and NumPy, avoid lookahead bias, and create features that actually generalize.
Slow API responses kill user trust. Learn how to detect and fix N+1 queries, use multi-layer caching, and apply database-level optimizations that cut Rails API response times by 10x or more.