Algorithmic Trading & Quantitative Finance with Python

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.

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 lecture, we build a complete Volume Rate of Change (VROC) breakout trading system from scratch.

This session converts a simple volume indicator into a structured event-driven algorithm with:

• Volume expansion detection
• Range locking mechanism
• Breakout & breakdown entries
• Risk-defined exits (+2% / −1%)
• VROC reversal exit
• Capital protection logic (5% drawdown stop)
• Automated trade history logging
• Full session performance reporting

The Master Table numeric example (VROC 144%, range 884–886, breakout @ 889, etc.) is implemented exactly as shown in the strategy sheet Algo 46

You will also see the algo interacting with:

• A price + volume exchange simulator
• News-driven price acceleration
• Real-time equity tracking
• Professional trade summary output Algo 46

By the end of this lecture, you will understand how to:

• Use volume as permission (not signal)
• Separate priming from execution
• Design structured breakout systems
• Build institutional-style PnL reporting
• Integrate news-driven event modeling

This lecture strengthens your understanding of event-based trading architecture and systematic breakout logic.

In this lecture, we build a Volume Pressure Index (VPI) based participation engine that dynamically adjusts trading size based on rolling volume dominance.

This model:

• Uses rolling accumulated buy/sell volume
• Computes VPI between −1 and +1
• Classifies dominance into 5 states
• Scales quantity from 0% to 100%
• Ignores direction entirely
• Focuses purely on participation intensity