Learn algorithmic trading, no-code automation, and quantitative strategies. From beginner guides to advanced ML techniques.
Pillar guides that answer the big questions: safety, backtesting, indicators, templates, and live ops.
Crypto backtesting explained end-to-end: data quality, fees, slippage, funding rates, walk-forward validation, Monte Carlo stress testing, and the exact workflow to go from idea → backtest → paper trade → live.
A practical safety guide for crypto trading bots: trade-only API keys, permissions, encryption expectations, and the operational risk controls that prevent one bad day from wiping you out.
A beginner hub for building no-code crypto trading bots: what to build first, how to backtest realistically, how long to paper trade, and the safest path from idea → validation → live.
A practical library of crypto bot strategy templates: trend, breakout, mean reversion, and momentum. Learn which template fits which market regime and how to validate each before going live.
A practical guide to indicators for crypto bots: trend filters, momentum, volatility, and risk sizing. Learn indicator combos that make sense, when they fail, and how to validate them without overfitting.
Live trading is where strategies break. Learn what to monitor (fills, latency, DD), what to alert on, and how to design kill-switches and playbooks for exchange/API failures.
Learn how to build profitable automated trading bots without writing code. Complete step-by-step guide to visual trading platforms, backtesting strategies, and deploying crypto trading bots for beginners.
Crypto backtesting explained end-to-end: data quality, fees, slippage, funding rates, walk-forward validation, Monte Carlo stress testing, and the exact workflow to go from idea → backtest → paper trade → live.
A practical safety guide for crypto trading bots: trade-only API keys, permissions, encryption expectations, and the operational risk controls that prevent one bad day from wiping you out.
A simple checklist for exchange API key permissions for trading bots. Learn what “trade-only” really means, why withdrawal permissions are a dealbreaker, and how to reduce blast radius.
IP whitelisting can significantly improve security for trading bots, but it can also break deployments if you don’t understand your runtime. Here’s when to use it and how to avoid downtime.
A practical checklist of risk limits every crypto trading bot needs: max drawdown, max daily loss, exposure caps, volatility circuit breakers, and what to do when limits are hit.
API keys are secrets. Learn key rotation, scoping, environment separation, and what to do immediately if a key is leaked or you suspect compromise.
A buyer’s checklist to evaluate any crypto trading bot platform: custody model, API permissions, encryption expectations, risk controls, and operational resilience.
In strong crypto trends, RSI doesn’t behave like 30/70. Learn RSI range shift (40–80 / 20–60), when to use it, and how to backtest it without curve fitting.
ATR is one of the most useful tools in crypto bots: volatility-aware stops and position sizing. Learn sane defaults, what breaks, and how to validate with realistic costs.
MACD alone is noisy in ranges. Combine it with a trend filter to avoid chop and reduce false signals. Here’s a simple ruleset and validation checklist.
Bollinger mean reversion can work in ranges and fail catastrophically in expansions. Learn regime filters, risk limits, and the backtesting traps specific to mean reversion.
If a tiny parameter tweak kills performance, you don’t have an edge. Learn sensitivity checks for indicator strategies and how to validate without curve fitting.
A simple trend-following template for crypto bots: trend filter, entry trigger, exit logic, and risk limits. Includes a validation checklist to avoid overfitting.
Breakouts are where crypto bots win big and lose big. Use this breakout template with volatility filters, position sizing, and realistic backtesting assumptions.
A mean reversion template for crypto bots designed for ranges: entry zones, exits, volatility guardrails, and the risk limits that prevent blowups during trend transitions.
Grid, DCA, and trend bots behave very differently. Learn what regime each bot likes, the failure modes, and the validation checks to run before risking capital.
A strategy template can be correct but mistimed. Learn simple regime filters (trend/volatility/range) to decide when to run trend, breakout, or mean reversion bots.
A practical monitoring checklist for crypto bots: order success rate, fill quality, slippage, drawdown, exposure, and when to pause a strategy.
Alerts are your early-warning system. Learn the high-signal alerts for crypto bots: error rates, no fills, DD thresholds, spread widening, and position anomalies.
Exchanges go down. Learn how bots should behave during outages: idempotent retries, pause policies, cancel/replace strategies, and recovery steps to avoid double orders.
Most live bot bugs are not “strategy bugs”—they are retry bugs. Learn rate-limit handling, backoff, idempotency keys, and safe order workflows.
A kill switch is your last line of defense. Learn practical kill-switch triggers (DD, volatility, API errors), how to resume safely, and common mistakes.
Crypto tokens die, delist, and rebrand. If your backtest ignores dead tokens, you may be measuring a fantasy. Learn survivorship bias and how to reduce it.
A practical data quality checklist for crypto backtests: missing candles, bad ticks, exchange differences, symbol changes, and how these issues create fake edges.
Stop judging crypto strategies by return alone. Learn the metrics that predict survivability: max drawdown, exposure, trade count, profit factor, and expectancy.
Fees, spreads, liquidity, and market microstructure vary by exchange. Learn how to re-test across venues and avoid false confidence from single-exchange backtests.
Look-ahead bias is the #1 invisible error in crypto backtests: using future information to make past decisions. Learn the common ways it sneaks in (indicators, candle-close fills, leaks), how to detect it, and how to fix it.
Backtesting, paper trading, and forward testing are not interchangeable. Learn the purpose of each, what each can and cannot prove in crypto markets, and the safest path from idea → validation → live.
Most crypto strategies fail live for one reason: costs. Learn how slippage, spreads, taker fees, and funding rates silently destroy backtests—and how to model them realistically.
Crypto regimes change fast. Walk-forward validation is how you stop curve-fitting and prove your strategy survives out-of-sample. Here’s the exact workflow and what “good” looks like.
Monte Carlo backtesting helps you understand fragility: sequence risk, slippage variance, and drawdown probability. Here’s what to simulate and how to interpret results for crypto strategies.
Overfitting is the #1 reason crypto backtests don’t survive live. Learn the warning signs, how to validate properly, and how to design strategies that generalize across regimes.
A complete guide to backtesting trading strategies. Learn Monte Carlo simulation, walk-forward optimization, and how to avoid the deadly trap of overfitting that destroys most algorithmic traders.
Master RSI-based trading strategies using visual builders. From basic overbought/oversold signals to divergence detection, multi-timeframe analysis, and RSI + MACD combinations that actually work.
Demystifying ML in trading. Learn how XGBoost, LSTM, and feature engineering actually work for predicting markets—and how to use them without writing Python code.
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