Risk management7 min read
Core guide
Start here
Risk management for algo traders
A trading system is only useful when its position size and drawdown rules are clear enough to survive a bad run.
Read articleBlackPeak / Knowledge
Trade the process,
not the hype.
A practical knowledge base for algorithmic traders. Learn how to evaluate a system, manage risk, set up MT5, and ask better questions before putting capital at risk.
44 guides17 topicsBeginner → advanced
Research you can use
Clear guides on backtests, expectancy, drawdown, and the parts of a strategy report that deserve scrutiny.
Risk before returns
Position sizing, prop-firm limits, and operational checks come before chasing a headline win rate.
Questions welcome
Each page has a discussion space so support questions can grow into a useful trader community over time.
Library
Algo trading, explained clearly.
Risk managementStrategyMT5 setupTrading foundationsData & researchEngineeringPortfolio design
Strategy8 min read
Core guide
Research basics
How to read a backtest without fooling yourself
Profit factor and win rate are useful clues, not a verdict. Learn which parts of a report deserve your attention.
Read articleMT5 setup6 min read
Core guide
Practical guide
MT5 EA setup checklist
A clean installation prevents many problems that look like strategy problems but are actually symbol, connection, or configuration issues.
Read articleTrading foundations5 min read
Core guide
Foundations
What an Expert Advisor actually does
An MT5 Expert Advisor is software that turns a defined trading process into repeatable decisions and orders.
Read articleTrading foundations6 min read
Core guide
Process
Build an algorithmic trading journal
A useful journal records more than wins and losses: it helps you separate strategy behaviour from operational mistakes.
Read articleStrategy8 min read
Core guide
Research process
How to develop an algorithmic trading strategy
A repeatable research process turns a trading idea into a testable hypothesis instead of a collection of indicators.
Read articleStrategy7 min read
Core guide
Validation
Walk-forward analysis explained
Learn how rolling in-sample and out-of-sample windows reveal whether an algorithm survives beyond the period that shaped it.
Read articleTrading foundations6 min read
Core guide
Real-world trading
Spread, slippage, and execution costs
Small trading costs compound. Here is how to measure the difference between a clean backtest and a real MT5 fill.
Read articleRisk management7 min read
Core guide
Evaluation accounts
Prop-firm EA rules checklist
Before running automation in an evaluation, translate the firm's current rules into checks your setup can actually enforce.
Read articleMT5 setup6 min read
Core guide
Operations
VPS and MT5 uptime checklist
Automation needs an operating environment. Use this checklist to catch connection, restart, and monitoring failures before they affect a position.
Read articleStrategy7 min read
Core guide
Strategy families
Trend following vs. mean reversion
These two strategy families respond to different market behaviours. Understanding the difference helps you choose honest tests and expectations.
Read articleRisk management6 min read
Core guide
Market conditions
News events and automated trading
Economic releases can change spread, liquidity, and execution. Learn how to decide whether a strategy should trade through them.
Read articleTrading foundations7 min read
Beginner
Foundations
How markets and orders actually work
Understand bids, asks, market orders, pending orders, and why an algorithm's decision is only the start of a trade.
Read articleTrading foundations6 min read
Beginner
Foundations
Candles, timeframes, and trading sessions
Learn what a bar really represents and why session boundaries, timezones, and incomplete candles matter to an EA.
Read articleStrategy7 min read
Beginner
Research basics
How to define a trading hypothesis
Turn a vague market belief into a precise, falsifiable question that an algorithmic research process can test.
Read articleStrategy6 min read
Beginner
Strategy design
Use indicators as measurements, not magic signals
Indicators transform price and volume into measurements. Learn how to use them without confusing a formula with an edge.
Read articleStrategy7 min read
Beginner
Strategy design
Design the entry, exit, and position lifecycle
A complete strategy is more than an entry condition. Map every transition from signal to position close before coding.
Read articleData & research7 min read
Intermediate
Performance analysis
Expectancy and the distribution of returns
Learn why the average trade, dispersion, streaks, and tail outcomes matter more than a single headline percentage.
Read articleData & research6 min read
Beginner
Performance analysis
Win rate, profit factor, and payoff ratio
Read common trading metrics together so one attractive number cannot hide an unstable or expensive system.
Read articleRisk management8 min read
Intermediate
Risk management
Risk of ruin, drawdown, and recovery time
Translate losing streaks into account-level consequences and understand why recovery requires more than the percentage first lost.
Read articleRisk management8 min read
Intermediate
Risk management
Position-sizing methods for algorithmic systems
Compare fixed lots, fixed cash risk, volatility sizing, and account-based sizing before selecting a rule your broker can support.
Read articlePortfolio design8 min read
Intermediate
Portfolio design
Correlation and portfolio risk
Two bots can look diversified by name while taking the same macro risk. Learn how to measure shared exposure before combining systems.
Read articlePortfolio design7 min read
Advanced
Portfolio design
How to combine multiple strategies
Build a portfolio from distinct return drivers, not a pile of bots that all respond to the same market move.
Read articleStrategy8 min read
Advanced
Strategy research
Regime awareness without overfitting
Use market conditions to understand where a system works without building a fragile switch that only recognises the past.
Read articleData & research7 min read
Beginner
Data engineering
The data-quality checklist for trading research
Bad timestamps, duplicated bars, missing sessions, and inconsistent symbols can create an edge that exists only in the dataset.
Read articleData & research7 min read
Intermediate
Data engineering
Bars, ticks, and choosing data resolution
Match the data resolution to the strategy's decisions so the backtest does not invent precision the live system cannot use.
Read articleData & research6 min read
Intermediate
Data engineering
Missing data, outliers, and bad timestamps
A practical process for finding the data defects that silently change signals, session filters, and backtest outcomes.
Read articleData & research8 min read
Intermediate
Research hygiene
Look-ahead bias and data snooping
The fastest way to improve a backtest is often to accidentally use information that would not have been known at the time.
Read articleData & research7 min read
Intermediate
Validation
How to design an honest out-of-sample test
Freeze the rules, protect the holdout, and evaluate the strategy in a way that resembles how it will face the future.
Read articleData & research8 min read
Intermediate
Robustness
Monte Carlo stress testing for trading systems
Use resampled trade sequences and perturbed costs to explore how much of a backtest depends on a favourable path.
Read articleEngineering7 min read
Beginner
Python & tooling
A practical Python research project structure
Keep data, experiments, reports, and production candidates separate so your research can be reproduced months later.
Read articleEngineering8 min read
Intermediate
Python & tooling
Build reproducible backtests with Python
A reproducible backtest gives the same answer from the same inputs and makes disagreements easier to diagnose.
Read articleEngineering6 min read
Beginner
Engineering discipline
Version control for strategy research
Use commits, experiment notes, and configuration hashes to know exactly which code produced a trading result.
Read articleEngineering7 min read
Intermediate
Engineering discipline
Unit testing trading logic before live deployment
Small automated tests can catch sizing, stop, time, and state errors before they reach an account.
Read articleEngineering7 min read
Intermediate
Engineering discipline
Logging and observability for Expert Advisors
A bot that cannot explain its state is difficult to trust. Design logs that help you distinguish no signal from no execution.
Read articleEngineering8 min read
Advanced
Architecture
State machines for trading bots
Explicit states make order handling, restart recovery, and protection logic easier to reason about than scattered flags.
Read articleTrading foundations6 min read
Beginner
Execution
Order types and when to use them
Choose order types based on the strategy's priority: participation, price control, breakout activation, or risk protection.
Read articleData & research8 min read
Advanced
Market microstructure
Liquidity and market impact
A strategy can be profitable on paper but untradeable at its intended size. Learn how liquidity changes the result.
Read articleStrategy7 min read
Intermediate
Execution research
Spread dynamics and session selection
Use observed spread and activity to decide when a strategy should trade, rather than relying on a fixed clock alone.
Read articleEngineering6 min read
Intermediate
Operations
Latency and why it matters
Latency matters differently for a scalper, a session system, and a slower swing EA. Measure the path before paying for speed.
Read articleRisk management7 min read
Beginner
Trade management
Stops, targets, trailing exits, and time exits
Exit logic controls the trade distribution, account exposure, and operational behaviour. Design it as carefully as the entry.
Read articleRisk management8 min read
Intermediate
Protection
Daily risk controls for Expert Advisors
Build account-level brakes that remain active when the strategy is wrong, the market is abnormal, or the operator is away.
Read articleEngineering7 min read
Intermediate
Operations
Build a monitoring dashboard for an EA
A compact dashboard should answer whether the terminal is alive, what risk is open, and why the system is or is not trading.
Read articleData & research8 min read
Intermediate
Machine learning
Machine learning for trading: a realistic starting point
ML can help with classification, ranking, or regime description, but it does not remove the need for clean data and honest validation.
Read articleData & research8 min read
Advanced
Machine learning
Feature engineering without leakage
Create useful features from trading data while keeping the future out of the input and the live pipeline reproducible.
Read articleData & research8 min read
Advanced
Machine learning
Walk-forward validation for machine-learning models
Use rolling training and validation windows to test whether an ML pipeline adapts without learning from the future.
Read articleStrategy7 min read
Advanced
Machine learning
Classification vs. regression in trading
Choose a model target that maps clearly to a decision, and measure whether it improves the trade process after costs.
Read articleData & research6 min read
Intermediate
Machine learning
When not to use machine learning
Sometimes a clear rule, better data, or better execution solves the problem more reliably than a model.
Read articleEngineering8 min read
Intermediate
Deployment
From research to live deployment
A staged checklist for moving from a promising backtest to a monitored system without skipping the uncomfortable evidence.
Read articleStrategy7 min read
Intermediate
Process
How to review a strategy after launch
Build a post-launch review that separates statistical variation, execution drift, and genuine strategy failure.
Read articleMT5 setup7 min read
Beginner
Troubleshooting
Debugging an EA that does not trade
A step-by-step operational diagnosis for silent Expert Advisors, from connection and symbol checks to signal and protection state.
Read articleMT5 setup7 min read
Beginner
MT5 setup
Broker symbol specifications that change your EA
Tick value, point size, volume steps, trading sessions, and stop levels can differ across brokers even when the symbol name looks familiar.
Read articleMT5 setup7 min read
Intermediate
Operations
Timezones, daylight saving, and trading automation
Make session filters, daily loss resets, and research timestamps consistent across brokers, data feeds, and local displays.
Read articleEngineering6 min read
Beginner
Process
A safe process for changing strategy parameters
Treat every input change as a new experiment so a live system does not drift away from the evidence that justified it.
Read articleBrowse by topic
Find your next question.
Risk management
Position sizing, drawdown controls, and surviving losing streaks.
Strategy research
How to develop, validate, and challenge an algorithmic edge.
MT5 setup
Installing Expert Advisors and keeping the execution environment healthy.
Trading foundations
The core concepts every algo trader should understand.
Prop-firm rules
Drawdown limits, automation policies, and evaluation constraints.
Data & backtesting
Data quality, testing design, walk-forward analysis, and stress tests.
Execution
Spreads, slippage, fills, sessions, and real-world trade mechanics.
Process & journaling
Repeatable review habits that separate evidence from emotion.
Operations
VPS uptime, monitoring, logs, alerts, and operational discipline.
Portfolio design
Combining systems, managing correlation, and allocating risk across a portfolio.
Python & tooling
Research notebooks, reproducible experiments, code quality, and useful tools.
Machine learning
Features, labels, leakage, validation, and realistic uses of ML in trading.
Market microstructure
Liquidity, order types, order books, impact, and the mechanics behind fills.
Data engineering
Collecting, cleaning, aligning, and versioning the data behind a research process.
Software engineering
Architecture, testing, state management, debugging, and maintainable trading code.
Performance analysis
Expectancy, attribution, robustness, drawdown, and post-trade diagnosis.
Research hygiene
Avoiding false discoveries with clear hypotheses, baselines, and honest validation.
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