Algorithmic Trading & Quantitative Finance with Python & MT5

Understand why algorithmic trading has become an important part of modern financial markets and trading infrastructure. Explore the evolution of technology-driven trading and how automation, data, and quantitative models are changing the industry. Learn about emerging trends such as AI, machine learning, systematic strategies, and automated execution. This lecture provides a broader perspective on the future opportunities and applications of algorithmic trading.

Discover the fundamental concept of algorithmic trading and how mathematical models can be transformed into systematic trading decisions. Learn how probability, statistics, quantitative analysis, and programming come together to analyze market behavior. Understand the basic building blocks of an algorithmic trading system and how trading rules can be converted into executable logic. This lecture establishes the quantitative foundation for the strategies covered throughout the course.

Explore a range of important techniques used in the development and implementation of algorithmic trading strategies. Understand how technical indicators, quantitative models, statistical methods, market data, and execution logic can be combined to build systematic strategies. Learn how different techniques can be applied according to market conditions and trading objectives. This lecture provides a practical framework for evaluating and developing algorithmic trading approaches.

Learn how to effectively use the study material, strategy files, supporting resources, and practical content provided with the course. Understand how to follow the lectures alongside the downloadable materials and practice the concepts independently. The lecture explains a structured approach for studying, implementing, testing, and modifying the strategies covered in the program. This will help you get maximum practical value from the course resources.

Explore the Stepwise Adaptive Runner algorithm and understand the logic behind its systematic trading process. Learn how predefined rules can adapt to changing market conditions while progressing through different trading stages. The lecture demonstrates how the strategy can be translated into algorithmic logic and applied to market data. This provides practical exposure to developing adaptive, rule-based trading systems.

Explore an algorithmic strategy based on averaging positions as the market moves and predefined profit conditions are reached. Understand the underlying trading logic, position management approach, and rules used to control entries and exits. The lecture demonstrates how a discretionary trading concept can be converted into systematic algorithmic rules. It also provides an opportunity to understand the importance of risk management and position-sizing logic when designing such strategies.

Explore the Gradient Convergence Strategy (GCS), a systematic approach designed to identify changes in price direction and momentum. Understand how price gradients and convergence concepts can be converted into objective trading signals. Learn how the strategy processes market movements to identify potential entry and exit conditions. This lecture demonstrates how mathematical concepts can be transformed into a practical algorithmic trading strategy.

Learn how Z-Score can be used to measure the relative position of price compared with its historical behavior. Understand the statistical foundation of mean reversion and how deviations from the mean can generate potential trading signals. The lecture demonstrates how Z-Score thresholds can be incorporated into systematic entry and exit rules. This provides a practical introduction to statistical techniques used in quantitative trading.

Explore a mean-reversion strategy built around the Simple Moving Average (SMA). Learn how the distance between market price and its moving average can be used to identify potential deviations and reversion opportunities. Understand how systematic entry, exit, and signal conditions can be defined using SMA-based rules. This lecture demonstrates how a simple technical concept can be developed into a structured algorithmic trading strategy.

Learn how moving average crossover signals can be converted into a systematic algorithmic trading strategy. Understand how short-term and long-term moving averages interact to identify potential changes in market direction. The lecture covers the basic logic behind entries, exits, and trend identification using crossover conditions. This provides a practical foundation for building and testing trend-following algorithms.

Explore the TrendLock Donchian Engine (TDE), a systematic trend-following strategy based on Donchian channel concepts. Learn how price breakouts and trend conditions can be combined to identify potential trading signals. Understand how the algorithm tracks market direction and applies predefined rules for entries and exits. This lecture demonstrates how channel-based analysis can be transformed into a structured algorithmic trading system.

Learn how the Smart Range Breakout strategy uses Average True Range (ATR) to adapt breakout conditions to changing market volatility. Understand how dynamic volatility measurements can help distinguish meaningful price movements from ordinary market fluctuations. The lecture demonstrates how range boundaries, breakout conditions, and ATR-based filters can be incorporated into an algorithm. This provides a practical example of building volatility-adaptive trading strategies.

Explore the SuperTrend indicator as a foundation for developing a systematic trend-following algorithm. Learn how price and volatility information can be combined to identify potential changes in market direction. Understand how SuperTrend signals can be converted into objective entry and exit rules. The lecture demonstrates how a widely used technical indicator can be structured into a repeatable algorithmic trading framework.

Learn how the Moving Average Convergence Divergence (MACD) framework can be used to develop a systematic momentum strategy. Understand how MACD lines, signal lines, and momentum changes can be incorporated into algorithmic trading rules. The lecture demonstrates how momentum signals can be converted into structured entry and exit conditions. This provides practical insight into designing trend and momentum-based algorithmic strategies.

Explore the Heikin-Ashi Trend Catcher strategy and learn how modified price candles can be used to identify market trends more systematically. Understand how Heikin-Ashi price calculations help filter short-term market noise and highlight directional movements. The lecture demonstrates how candle-based conditions can be converted into objective algorithmic entry and exit rules. This provides a practical example of developing a trend-following strategy using alternative price representations.

Learn how the Average Directional Index (ADX) can be used to measure the strength of a market trend. Understand how ADX and directional movement components can be incorporated into systematic trading rules. The lecture demonstrates how trend-strength conditions can be combined with entry and exit logic to build an algorithmic strategy. This approach helps illustrate the difference between identifying market direction and measuring the strength of that direction.

Explore the application of the GARCH model for forecasting market volatility in an algorithmic trading framework. Learn how historical return behavior can be used to estimate changing volatility conditions and how those estimates can influence position sizing. The lecture connects econometric volatility modeling with practical risk and capital allocation decisions. This provides an introduction to using advanced quantitative models for dynamic position management.

Explore a VWAP-based mean-reversion strategy that uses Volume Weighted Average Price as a reference for estimating market fair value. Understand how deviations from VWAP can be analyzed to identify potential reversion opportunities. The lecture demonstrates how price, volume, and statistical distance from VWAP can be incorporated into systematic trading rules. This provides a practical example of combining market microstructure concepts with algorithmic mean-reversion techniques.

VWAP Reversion works only until the market starts trending.
This upgraded algo uses ADX to protect your VWAP trades from runaway losses.

In this video, you’ll learn:

How VWAP entries are generated using 20-TP moving window
Why VWAP alone fails during trending markets
How ADX detects counter-trend strength
When ADX triggers an emergency exit
How to build candles from ticks (5 ticks = 1 candle)
How Unrealised & Realised PNL is calculated
Full Python code (tick → candle → signal → execution → PNL)

In this video, we break down a complete RSI Reversion Algorithmic Trading Model — from concept to execution.

You’ll learn:

What RSI truly measures (momentum, speed, pressure)
Why RSI < 30 and RSI > 70 zones create high-probability reversal setups
How to convert RSI logic into an automated trading script
Entry, exit, stop-loss, and take-profit architecture
Mean-reversion confirmation using momentum neutral zones
Full PnL reporting: wins, losses, equity curve, drawdown control

we break down a complete Pivot Point Breakout Trading Algorithm — a clean, indicator-light system built entirely on price-derived levels.

Based on the classic levels P, R1, R2, S1, S2, this model identifies where the market is likely to reverse or break out with strength.

Once the warm-up window collects enough prices, the algorithm:

1. Calculates Pivot, R1, R2, S1, S2

2. Tracks price tick-by-tick

3. Detects real-time breakouts above R1/R2 or breakdowns below S1/S2

4. Executes Long/Short entries based on pure price crossing logic

5. Runs automated Take-Profit & Stop-Loss

6. Recalculates pivots after every completed trade

7. Maintains full unrealized and realized PnL reporting

In most trend-following systems, traders rely on one or two moving averages to judge direction. But markets rarely trend in a clean line. Momentum forms a structure — and understanding that structure gives far better signals.

So I built a Moving Average Ribbon Breakout Algo, using:
✔ FAST MA (5) – short-term strength
✔ MID MA (10) – medium trend filter
✔ SLOW MA (20) – long-term direction

When the ribbon aligns in one direction, it confirms strong momentum:
FAST > MID > SLOW → Bullish alignment
FAST < MID < SLOW → Bearish alignment

In this video, you will learn why the Hull Moving Average (HMA) is one of the fastest and most effective indicators for algorithmic trading, and how it forms the core of a complete trend-following system.

• Traditional moving averages lag behind real price movements:
• SMA is extremely slow.
• EMA reacts faster but still turns late during reversals.
• WMA attempts to speed up response but often introduces noise.
• This lag results in late entries, delayed exits, and a higher number of losing trades.

• HMA overcomes all these limitations:
• Provides up to 80% less lag compared to traditional moving averages.
• Generates a smooth, stable line without the jitter seen in fast EMAs.
• Reacts almost instantly to trend changes.
• Offers clearer signals with significantly fewer whipsaws.

• How HMA achieves this performance:
• Uses WMA of the half-length to capture the most recent trend movement.
• Subtracts WMA of the full length to remove older lagging effects.
• Applies a final WMA using the square-root length to smooth the output.
• Produces a clean, low-lag trend line that reacts immediately yet remains stable.

Explore an adaptive algorithmic trading strategy that uses ATR to identify changing volatility regimes and automatically adjust its trading approach. Learn how SuperTrend and SMA can be combined to respond differently to high- and low-volatility market conditions. The strategy demonstrates how regime detection can dynamically switch between trend-following and alternative signal logic. This lecture provides a practical example of building adaptive algorithms that respond to changing market environments.

In this video, we break down the Keltner Volatility Breakout System and demonstrate the impact of changing the K multiplier.

The value of K controls how wide the Keltner Channel becomes, which changes the breakout behavior and trade frequency.

K Multipliers Demonstrated:

K = 1 (Tight Channel) • Narrow range • More breakouts • More trades • More noise, faster reversals

K = 2 (Balanced / Standard) • Ideal default setting • Balanced number of signals • Good trend quality • Smooth volatility filtering

K = 3 (Wide Channel) • Very wide volatility bands • Rare breakout signals • Only large volatility expansions trigger trades • Best for strong trend-following behavior

A fully-simulated, real-time trading environment where News + Price + Mean Reversion come together to create a realistic algo-trading lab.

This system is built using 4 connected engines:

1️⃣ News Engine – generates real-time sentiment events (best/good/neutral/bad/worst) every 10–20 seconds.
2️⃣ Exchange Simulator – converts news into smooth price reactions using geometric ramps (whole-number prices only).
3️⃣ Price Feed – produces micro-drift to mimic natural market behaviour.
4️⃣ RSI Mean Reversion Algo – trades Long/Short using RSI(14), with take-profit, stop-loss, equity protection & full PnL reporting.

? What makes it unique?

• Real-time sentiment → price pipeline

• Smooth & realistic price ramping

• RSI-based mean reversion entries/exits

• Complete trade history + win rate + equity curve

• Risk systems: SL / TP / Capital Stop

• Works like a real intraday mini-market!

News Flow Price Reactor ALGO + ATR Volatility Position Sizing Engine (0–100 Units)
A complete real-time trading simulation where News + Price + Volatility fuse into one intelligent decision system.

? What this ALGO Does
✔ Real-time news-driven price movement simulation (best/good/neutral/bad/worst)
✔ Price reacts via geometric ramps (smooth % moves)
✔ Continuous micro drift for natural price motion
✔ ATR(14) calculated from closing-only data
✔ Dynamic ATR Score → Volatility normalized between 0–1
✔ Core output: Smart quantity sizing from 1 to 100 units
✔ Low ATR → High units | High ATR → Low units

? Why it’s powerful
? Handles market mood + volatility in one pipeline
? ATR_min & ATR_max are learned dynamically from history
? Perfect for algorithmic experimentation, reinforcement learning, risk models
? Fully automated real-time system (3 modules running together)

This video showcases a complete algorithmic trading environment built from the ground up, combining a fully dynamic Real-Time Exchange Simulator, a News Generator Engine, and an intelligent RSI + ATR Trading Algorithm. The exchange updates prices every 3 seconds, adds natural market behavior through micro-drift (±0.1%), and reacts to incoming news by applying smooth geometric price ramps that reflect sentiment strength—Best (+3%, 20 steps), Good (+2%, 15 steps), Neutral (0%), Bad (–2%, 20 steps) and Worst (–3%, 20 steps). The news system automatically produces fresh events every 10–20 seconds, selecting one of five categories (Best, Good, Neutral, Bad, Worst) to mimic real market headlines and volatility shocks. On top of this dynamic price feed, the RSI + ATR Trading Engine makes smart decisions: it enters LONG when RSI < 30, enters SHORT when RSI > 70, and closes positions on a mean-reversion signal when RSI returns to 50. Volatility is measured using ATR, and position size automatically adjusts between 1 and 100 units—larger trades in low volatility, smaller trades in high volatility—making the system behave like a real professional trading desk. This integrated simulation delivers a true end-to-end demonstration of sentiment-driven price movement, volatility-based risk control, and rule-based algorithmic decision-making.

In this video, we explore the true meaning of Turtle Trading — the philosophy that transformed ordinary people into consistently profitable traders.

The original Turtle experiment proved something powerful: successful trading isn’t a talent — it’s a skill built by following rules.

By removing emotions like fear, greed, and hesitation, Turtle Trading replaces human weakness with: 

Clear rules 

Zero guesswork

Objective signals

Consistent execution

This method is not about indicators — it’s about a mindset:

Follow price

Avoid predictions

Manage risk

Stay consistent

Trust the system

Basic TWAP + Randomized TWAP Algorithm (Full Code + Real Execution Simulation)

Institutions and fund houses never buy in one big order.
They use TWAP — a trick to ensure others don’t understand that buying is happening in the background.

To replicate this, I built two execution models:

Basic TWAP → equal slices at fixed time
Randomized TWAP → unpredictable slices so other algos cannot detect the buying pattern

These models help simulate how fund houses hide their large orders inside the market flow to avoid slippage and impact.

What’s inside?

• Time-based slicing

• Random interval execution

• Slippage simulation

• Full execution reports

• Ready for exchange simulators

In this video, we break down two powerful execution algorithms used by institutions:

Adaptive TWAP

• Adjusts slice size based on spread & volatility

• When spread widens or volatility spikes → slice reduces

• Helps avoid slippage during unstable markets

Aggressive TWAP

• Keeps slice size fixed

• Changes execution speed instead of quantity

• Fires faster when spread is tight and market is stable

What You Will Learn

• How TWAP helps funds hide their buying

• Why Adaptive TWAP slows down in volatile markets

• Why Aggressive TWAP speeds up when spreads tighten

• How real execution engines behave behind the scenes

• How to simulate TWAP using Python with price drift and news-driven volatility

In this video, we build a surveillance-grade TWAP detection system that identifies:

• Basic TWAP

• Random TWAP

• Aggressive TWAP

• Adaptive TWAP

—all without reading algorithm metadata.
The detector relies only on interval patterns, slice behaviour, volatility response and spread correlation — perfectly matching the TWAP behaviour matrix from Algo 29 and Algo 29a PDFs (interval stability, unit stability, volatility scaling, and spread-reaction).

What You’ll Learn
How exchanges detect hidden execution algorithms

Why funds randomize slice sizes and intervals
How Basic, Random, Aggressive & Adaptive TWAP actually behave
How to build a detector using Python (your TWAP scripts)
Real-time classification using rolling window features

In this video, you will learn the complete logic behind the Volatility Adaptive Moving Average (VAMA) — one of the most advanced and truly adaptive MAs used in algorithmic trading.

VAMA automatically adjusts its speed depending on volatility:

• High volatility → MA becomes faster

• Low volatility → MA slows down
  

• We break down:
  What makes VAMA different from SMA, EMA, WMA

  How α (alpha) adapts — with clamp range [0.02–0.20]
  Full 20-step VAMA calculation table
  Trend rules: Price > VAMA → Long, Price < VAMA → Short
  LIVE algo code that trades using only price (no volume, no momentum)
  Real-time entries, exits, PnL and volatility-driven adaptations from your script

In this video, we break down one of the most practical and institution-grade execution algorithms — the Intelligent Volume Participation (IVP) Algo, also known as the Percentage of Volume (POV) Algo.

You’ll learn:

What is POV (Percentage of Volume)

How the algo trades at a fixed participation rate

How “Gap” is calculated using real market volume

When the algo enters Catch-Up Mode vs Slow-Down Mode

How slice size is decided dynamically

Full tick-by-tick example with Market Volume, POV Target, Gap, and Execution updates

Why This Algo Is Important?

This algo mimics how institutions execute huge orders without creating market impact.

It automatically adjusts to market volume and ensures you stay close to your target participation (e.g., 10%).

Behind the target? → Trade more (Catch-up Mode)

Ahead of the target? → Trade less (Slow-down Mode)

What You Get in This Video

Simple explanation of POV logic

Real example table with 12 ticks

Practical explanation of target, gap, execution

Clean, beginner-friendly algo trading breakdown

Unlock the power of Adaptive Regression Spread Trading using Real-Time OLS (Machine Learning Method)!

In this video, you’ll learn:

How to calculate β (slope) and α (intercept) step-by-step

How to predict S2 using the regression equation

How to compute spreads and interpret trading signals

Case-wise trading decisions (BUY/SELL logic)

How OLS becomes the backbone of mean-reversion pair trading

Most trading strategies rely on indicators.

This one relies on probability theory.

In this video, I explain Algo – the Pure Bayesian Trend Predictor, a mathematically grounded trading model that determines whether the market is UP, DOWN, or FLAT using Bayes’ theorem.

No indicators

No volume filters

No assumptions

Only price + probability

What you’ll learn:

• How Bayesian inference applies to trading

• Why probabilities are superior to binary signals

• How trend belief updates with every new price

• How this model works on any timeframe

• Why this approach is ideal for live algo trading

This is the same logic used in quant desks, HFT research, and probabilistic AI systems.

This is not a retail strategy — it’s a quant mindset shift.

Most traders chase price breakouts.

Institutions wait for volume confirmation.

In this video, I explain a Volume Spike Confirmation Model used to validate whether a price move is backed by real participation or just noise.

You’ll learn:

• What a true volume spike actually means

• Difference between flexible vs strict spike detection

• How multipliers filter false breakouts

• Why institutions avoid low-volume moves

• How to combine volume spikes with price structure

VWAP is not just another indicator.

It’s a benchmark used by institutions to judge fair price.

In this video, I explain a VWAP Trend Filter with Average Volume Momentum Engine, a clean trading framework that enters trades only when trend direction and volume strength agree.

What you’ll learn:

• Why VWAP defines institutional trend bias

• How average volume confirms real participation

• Exact long & short entry conditions

• Why exits are condition-based (not TP/SL)

• How this model avoids low-quality trades

In this video, I explain OBV from first principles, not just as an indicator but as a volume-driven market control model.

What you’ll learn in this video
✅ What OBV actually measures (accumulation vs distribution)
✅ OBV formula explained row-by-row
✅ Step-by-step OBV calculation using price & volume
✅ OBV slope logic (1-period & N-period interpretation)
✅ Bullish divergence explained with higher OBV lows
✅ Bearish divergence explained with lower OBV highs
✅ How OBV confirms breakouts, reversals & fake moves

Core Concept
Price tells where the market moved.
OBV tells who was in control.