BasicsIntroductory
The trader's equation: win rate, risk, and reward
The expected-value equation behind every trade and why nearly every strategy decision is downstream of it.
8 min readRead article
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Explore practical explanations, guides and reference material for building, testing and evaluating crypto trading strategies under real market conditions.
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Basics
Essential concepts for building and testing rule-based trading strategies.
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Core principles behind realistic testing, validation, risk and robust performance.
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Practical, step-by-step guidance for building, testing and improving strategies.
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Clear explanations of the metrics, methods and terminology used in strategy analysis.
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BasicsIntroductory
Realistic expectations: what systematic trading can and cannot do
Calibrating what a real edge looks like, and why expectations — not signals — end most accounts.
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FoundationsIntroductory
Building a strategy: what to consider before you write a single rule
The four parts of a complete strategy — entry, exit, regime and sizing — and the traps that catch most retail builds.
8 min readRead article
FoundationsIntroductory
Realistic backtesting: the hidden costs that kill paper edges
Why the curve in a standard backtester is fiction: fees, funding, slippage, partial fills and latency, and how to model them honestly.
10 min readRead article
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20 articles available
Basics
Essential concepts for building and testing rule-based trading strategies.
BasicsIntroductory
How crypto markets work: order books, spreads, and order types
How the order-book auction works: bids, asks, the spread as a real cost, and the order types every systematic trader needs to know.
8 min readRead article
BasicsIntroductory
Spot vs. perpetual futures: funding, margin, and liquidation
What a perpetual future actually is and how funding, margin and liquidation work in practice.
9 min readRead article
BasicsIntroductory
Trading styles: scalping, day trading, swing, position, and market-making
How holding time decides costs, required win rate, and whether a style is viable for a retail systematic trader.
9 min readRead article
BasicsIntroductory
The trader's equation: win rate, risk, and reward
The expected-value equation behind every trade and why nearly every strategy decision is downstream of it.
8 min readRead article
BasicsIntroductory
Realistic expectations: what systematic trading can and cannot do
Calibrating what a real edge looks like, and why expectations — not signals — end most accounts.
8 min readRead article
BasicsIntroductory
The psychology of running a system
Automation removes some bad habits and creates new ones. The discipline needed to keep a system running.
8 min readRead article
Foundations
Core principles behind realistic testing, validation, risk and robust performance.
FoundationsIntroductory
Building a strategy: what to consider before you write a single rule
The four parts of a complete strategy — entry, exit, regime and sizing — and the traps that catch most retail builds.
8 min readRead article
FoundationsIntroductory
Risk: what it actually means in systematic trading
Risk is not volatility. Drawdown, ruin, correlated risk, leverage and tail risk — the things that actually end accounts.
9 min readRead article
FoundationsIntroductory
Realistic backtesting: the hidden costs that kill paper edges
Why the curve in a standard backtester is fiction: fees, funding, slippage, partial fills and latency, and how to model them honestly.
10 min readRead article
FoundationsIntroductory
Overfitting: how good-looking numbers lie
The most common reason backtests succeed and live trading fails — how to detect overfitting and defend against it.
10 min readRead article
FoundationsIntermediate
Out-of-sample testing: protecting against luck
Splitting train and test, walk-forward, and why crypto needs longer windows than equities to mean anything.
9 min readRead article
FoundationsIntermediate
Optimisation: what it is, what it's worth, and when it hurts
What optimisation can and cannot do, and how to avoid the data-mining trap that turns a fine strategy into a fragile one.
9 min readRead article
FoundationsIntermediate
Statistical tools: Sharpe, Sortino, profit factor, and what each hides
Sharpe, Sortino, Calmar, profit factor, win rate, expectancy, max drawdown — what each measures and what each conceals.
11 min readRead article
FoundationsIntermediate
Market regimes: why one strategy can't fit all markets
Trending, choppy, high-vol, low-vol — why no single strategy works in all of them, and how to think about regime dependency.
8 min readRead article
FoundationsIntermediate
Strategy decay: why edges erode and how to spot it early
Why edges erode — crowding, regime change, structural shifts — and how to spot decay before it eats an account.
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FoundationsIntermediate
Multi-objective optimisation and Pareto frontiers
Why optimising one metric overfits, and how Pareto frontiers help find candidates that survive multiple objectives.
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FoundationsIntermediate
Trade quality: why fill quality matters more than fill count
Per-trade expectancy, fill quality and why more trades is not always better — especially in crypto.
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FoundationsIntermediate
Portfolio construction: why one strategy is fragile
Why running a single strategy is fragile, and how correlation, allocation and rebalancing build a more durable result.
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FoundationsIntroductory
The live headwind: how costs eat a paper edge, line by line
A line-by-line worked example of how fees, funding, spread and partial fills erode a paper edge.
9 min readRead article
FoundationsIntroductory
Why one pair is not proof: single-market backtests and selection bias
Why a single-pair backtest is just one sample, and how pair-shopping manufactures fake edges.
8 min readRead article
Guides
Practical, step-by-step guidance for building, testing and improving strategies.
Articles in this category are being written. Check back as the Learning Hub grows.
Reference
Clear explanations of the metrics, methods and terminology used in strategy analysis.
Articles in this category are being written. Check back as the Learning Hub grows.
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