FX
CURIO
Learning PathsAdvanced10 hours13 lessons

Quant & Automation

From discretionary rules to code: hypothesis testing, signal design, bot architecture and kill switches.

Who this path is for

Technical traders with some programming ability who want systematic execution.

What you will be able to do

  • State an edge as a testable hypothesis
  • Engineer signals without leaking future data
  • Design a bot with monitoring and kill switches
  • Recognise the ML traps that destroy live results

Curriculum

modules 1

Quant thinking

Evidence over narrative.

  1. 01What Is Quantitative Trading?Quant trading replaces gut feeling with data, math, and code. Here's what actually separates it from discretionary trading.Quant & AutomationBeginner7 min read
  2. 02Statistical Edges and Hypothesis TestingHow to tell a real edge from noise: p-values, sample size, and the statistical traps that make bad strategies look good.Quant & AutomationIntermediate8 min read
  3. 03Mean Reversion vs. Momentum ModelsThe two pillars of quant strategy design pull in opposite directions — and knowing which regime favors which is the real edge.Quant & AutomationIntermediate8 min read
modules 2

Signals and portfolios

From single alpha to a book.

  1. 04Signal Design and Feature EngineeringRaw price data isn't a signal. Turning it into predictive features is where most of a quant's real work happens.Quant & AutomationAdvanced8 min read
  2. 05Portfolio Construction and CorrelationCombining strategies isn't about picking the best one — it's about picking ones that lose money at different times.Quant & AutomationAdvanced9 min read
  3. 06Correlation Risk & Portfolio HeatFive trades at 1% risk each can secretly be one trade at 5% risk if the pairs move together.Risk & Money ManagementIntermediate7 min read
modules 3

Building the machine

Data in, orders out, safely.

  1. 07Trading APIs and Data PipelinesEvery algorithmic strategy runs on top of a data and execution pipeline. Get this plumbing wrong and nothing above it matters.Quant & AutomationIntermediate8 min read
  2. 08Building a Trading Bot ArchitectureA trading bot is a distributed system with money on the line. Its architecture needs to be boring, testable, and hard to break.Quant & AutomationAdvanced9 min read
  3. 09Algorithmic Execution and Order SlicingA great signal can still lose money if it's executed badly. Execution algorithms exist to minimize what a trade costs to place.Quant & AutomationAdvanced8 min read
  4. 10Latency and VPS HostingThe physical distance and network path between your trading terminal and the broker's server can measurably affect execution quality, especially for fast strategies.Execution, Brokers & CostsIntermediate7 min read
modules 4

Operating it

Live systems fail loudly or silently.

  1. 11Monitoring, Risk Controls, and Kill SwitchesAn algorithmic strategy left unmonitored is a liability waiting to happen. Here's how professionals watch their bots.Quant & AutomationAdvanced7 min read
  2. 12Machine Learning Pitfalls in TradingML looks powerful on financial data — and that's exactly the problem. Most ML trading models fail for the same handful of reasons.Quant & AutomationAdvanced9 min read
  3. 13Walk-Forward Analysis and Forward TestingThe bridge between backtest and live trading: proving a strategy on data it has never seen before.Backtesting & JournalingAdvanced7 min read
Practice it

Practise this path on live markets

Theory sticks when you apply it. Run the setups from this path through CURIO's analysis, journal and backtest engine.

  • Backtest the strategy on historical data
  • Journal with excuse tags & streaks
  • Certificates for completed paths from Pro
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