A structured path for traders who want to understand rules-based systems, Python research, honest backtesting, ML validation, execution risk, and live deployment.
Start pathLearning structure
Turn automation from hype into a controlled research process: define rules, test without leakage, size risk, validate models, and only move toward live systems after paper checks.
01
Start with algorithmic trading basics, market mechanics, broker APIs, Python, time-series data, and a first rules-based strategy.
02
Build honest backtests, read performance metrics, manage sizing, account for transaction costs, and use statistics without fooling yourself.
03
Learn ML features and labels, time-series validation, advanced quant strategies, live deployment, monitoring, and research ethics.
Live curriculum
Every module turns one risk concept into lessons, fieldwork, and a quiz checkpoint.
Module 1
FreeStart with definitions: what algorithmic trading is, why institutions and serious traders use it, and how an automated system works end to end.
Module 2
FreeBefore writing a strategy, understand the playing field: how orders meet in the order book, the role of brokers and APIs, and what each order type implies.
Module 3
FreePython is the de facto language for quantitative research. Build practical foundations with environments, NumPy, Pandas, and price data.
Module 4
FreeA strategy is only as good as its data. Learn price-data structure, timeframe resampling, and the quiet data traps that break backtests.
Module 5
FreeConnect the pieces from hypothesis to rules, signals, and positions through a simple moving-average crossover strategy.
Module 6
FreeA bad backtest is more dangerous than no backtest. Learn honest testing practices and the biases that must be avoided.
Module 7
FreeReturns alone are misleading. Build the metric vocabulary needed to balance reward and risk and compare strategies fairly.
Module 8
FreeIndicators transform price data into features. Learn the main indicator families, how to program them, and common implementation mistakes.
Module 9
FreeSizing determines survival. Even a strategy with edge can fail when position size is wrong, so learn practical sizing logic.
Module 10
FreeCosts quietly kill strategies. Model transaction costs realistically and understand capacity, turnover, and market impact.
Module 11
FreeQuant trading is applied statistics. Build intuition for separating real patterns from randomness.
Module 12
FreeMachine learning is a tool, not magic. Frame market prediction as supervised learning and build the right conceptual pipeline.
Module 13
FreeWrong validation is the fastest way to fool yourself with ML. Learn validation techniques specific to financial time series.
Module 14
FreeApply statistical foundations to practical quant strategies including mean reversion, pairs trading, cross-sectional momentum, and factor investing.
Module 15
MembersSurvey modern AI techniques in trading, where they can help, and where they are often over-promoted.
Module 16
FreeA strong strategy can fail through weak operations. Learn how to run strategies live with staged rollout, monitoring, and fail-safes.
Module 17
FreeClose the path by combining disciplined research, ethical awareness, regulatory context, and honest expectations for quant trading.
Module 18
FreeA comprehensive assessment drawing from foundations, backtesting, metrics, risk, statistics, machine learning, strategy, and production.