Quantitative Trading Course

Compare traditional trading with algorithmic trading, highlighting speed, efficiency, and emotionless, rule-based decision making. Learn how predefined rules, risk management, and scalability drive disciplined, consistent trading.

Explore how algorithmic trading enables diversification and risk management across assets. It uses fundamental and technical signals to drive data-driven decisions and scale beyond human limits.

Compare algorithmic and traditional trading by cost and overheads, highlighting 24/7 availability enabling global markets and timely market events, while addressing technical complexity, quantitative analysis, programming, and infrastructure management.

Explore market risk in algorithmic trading, including volatility, unforeseen events, and overfitting, and learn risk management practices, out-of-sample testing, parameter constraints, and cost considerations.

Explore how offer and demand shape forex prices by examining selling and buying pressures influenced by interest rates, economic conditions, and market sentiment.

Explore statistical arbitrage strategies in forex trading, including cointegration for currency pairs, market making to profit from bid-ask spreads, and high-frequency trading using fast algorithms.

Identify the components of a forex trading strategy, focusing on entry and exit criteria, supported by technical analysis tools: support and resistance, moving averages, indicators, and chart patterns.

Explore exit criteria and risk management in trading, including stop loss, take profit, trailing stop, target levels, position sizing, and risk-reward ratios, with real-world examples.

Backtesting uses historical market data to evaluate a forex trading strategy's profitability and risk, identify weaknesses, and optimize entry, exit rules, and risk management.

Master backtesting for forex strategies by collecting historical data, selecting timeframes, running simulations with metatrader or python, and analyzing results to refine entry, exit, drawdowns, and risk reward ratio.

Explore the role of backtesting in forex trading, highlighting common pitfalls—data quality, overfitting, slippage, spreads, and market impact—and best practices for realistic, robust results.

Explore the role of backtesting in algorithmic trading, noting pitfalls and best practices. Discover forex execution algorithms, including market and limit orders and advanced types like trailing stops.

Analyze execution quality metrics in forex trading, focusing on slippage ratio and transaction cost analysis, and evaluate field ratio to optimize order execution and broker efficiency.

Explore how Renaissance Technologies and the Medallion Fund use a quantitative approach, differential geometry, and high-frequency trading to uncover non-random patterns and exploit inefficiencies with statistical arbitrage.

Explore Virtu Financial as a global market maker and high-frequency trading firm founded in 2008. Highlight its ultra-low latency technology, proprietary algorithms, and risk and compliance efforts shaping liquidity.

Explore Citadel, founded by Kenneth C Griffin, a global firm renowned for quantitative trading, hedge fund management, market making, and asset management.

Learn to generate, test, and optimize trading strategies with StrategyQuant X using a user-friendly, no-coding platform; install, activate a trial, and run the initial setup.

Learn how to buy SQX (Strategic One) using three licenses—ultimate, professional, and starter—and compare free trial with paid access. Apply a coupon for discounts and understand the 20% commission.

Tailor your trading portfolio by building single or multi symbol, multi timeframe strategies, using a template or improving an existing strategy, with configurable risk, backtesting settings, and data range options.

Explore how to assemble a perfect portfolio for quantitative trading in part 2 using builder blocks, signals, indicators, stop limits, and flexible entry and exit options.

Explore the genetic options window in the evolution module, adjusting generations, population size, and crossover and mutation rates. Learn how islands, migration, initial population, and restart options refine strategy evolution.

Students learn to create their first strategy using Strategy Quant, configuring build settings, indicators such as ATR and Ichimoku, data ranges, and backtest parameters, then refine filters to rank strategies.

explore evaluating the performance of hundreds of trading strategies, compare profitability and risk metrics, review an ichimoku based approach, and plan adding more strategies to the data bank.

Learn to generate more strategies by adjusting full settings, set stop loss and profit targets, and use RSI signals to compare net profit across 518 strategies for the best approach.

Explore key metrics for evaluating trading strategies, including total profit, profit in pips, yearly average profit, and risk-adjusted measures like compound annual growth rate, Sharpe ratio, and return drawdown ratio.

Learn to analyze trading strategies using a data bank, filter by net profit and performance metrics, and compare in-sample and out-of-sample results with equity charts.

Learn how system robustness tests assess trading strategies under varied conditions. Validate profitability and consistency by applying higher precision backtests across data granularity using cross checks and Monte Carlo simulations.

Explore the Monte Carlo test to assess system robustness of trading strategies by simulating trade order randomization and skipped trades, using in-sample validation to examine net profit and drawdown.

Retesting trading strategies on different markets evaluates robustness across diverse conditions, using high-precision and Monte Carlo tests, and comparing profit factors and net profit across symbols like eurusd and usdjpy.

Apply what-if simulations to evaluate trading strategies under alternative conditions, such as trading on selective days and excluding 5% most profitable and 5% most unprofitable trades, with a fixed lot.

Learn how the Monte Carlo retest method assesses trading strategy robustness through multiple simulations that vary spreads, historical data, or parameters, with 50 iterations and 80% confidence checks.

Learn how system optimization fine-tunes trading parameters to boost profitability while controlling risk, using an optimizer on MetaTrader 5 data to compare strategy performance by net profit.

Learn sequential optimization to refine trading strategies by optimizing parameters one at a time, ensuring robustness and reducing overfitting through stability tests and multi-layer backtesting.

Cross-check sequential optimization validates and refines parameters from the initial optimization in Strategic Quant, ensuring robustness, avoiding overfitting, and guiding stable, repeatable performance.

Master walk forward optimization to validate strategy robustness by repeatedly optimizing on in-sample data and testing on out-of-sample segments, simulating changing market conditions and revealing optimal frequency.

Explore how the walk-forward matrix tests trading strategies with overlapping in-sample and out-of-sample windows, optimizes runs and out-of-sample percentage, and evaluates robustness across market conditions.

Learn how system parameter permutation tests a strategy across parameter values using walk forward optimization and cross-checks to assess robustness and compare median versus original results.

explore how a forex trading portfolio diversifies across currency pairs and strategies to manage risk, optimize returns, and rebalance through asset selection, risk assessment, and position sizing.

Learn to create your first portfolio by merging five strategies, allocate initial capital to the first strategy, and evaluate in-sample versus out-of-sample performance and equity charts.

Explore portfolio correlation, learn how near-zero correlation strengthens diversification, and see how day or month calculations, open positions, and diverse currency pairs manage risk.

Learn to create a second portfolio in Strategy Quant using Portfolio Master with five strategies, brute force search with 70/30 in-sample out-of-sample data, and strict filtering for best fitness.

Create a third portfolio by merging multiple strategies into one, then compare merge and split options; the merged portfolio is tradable in MT4/MT5 but not back-tested in SQL.

Export and deploy trading strategies from SQX to MetaTrader 5 by saving portfolio and strategy source files, pasting them into the Mql5 experts folder, and compiling for a live account.

Navigate MetaTrader 5 by opening charts for forex pairs, configuring market watch, and using the navigator, toolbox, and experts to view positions and indicators.

Carry out our first backtest in MetaTrader five on the euro/usd pair using 2017–2022 data, with visual mode and custom indicators.

Backtest the portfolio using the hourly (H1) timeframe to compare trades and profits between strategies, highlighting how broker data differences explain the profit gap between $2,173 and $9,000.

Learn how a virtual private server powers reliable, fast, and accessible trading environments with a VPN, exploring free and paid providers like AWS, EC2, and RDP setup.

Activate a trading strategy on a real account via a vpn-connected VPS, installing MetaTrader 5 and logging into the broker. Create a stochastic advisor, enable automated trading, and monitor trades.

Learn how to set up multiple accounts on a vpn by creating new users, configuring passwords, and running as a different user to switch between accounts.

Maintain trading systems by continuous data analysis and economic calendar monitoring, risk assessment with stop loss and take profit levels, adaptation, diversified portfolios, and record keeping for performance tracking.