QuantFlow - 5.0 October Edition 2026

Course Starts From 31st October 2026
Timings 2 sessions a day (Saturday And Sunday) 
• Morning: 10 a.m. to 12 p.m. IST
• Afternoon: 2 p.m. to 4 p.m. IST 

Program overview

Quantflow 5.0 is a live coding mentorship that takes traders and investors from Python basics to self-autonomous trading agents, using an AI-driven, production-grade workflow.

You learn Python, NumPy and Pandas. Then you use AI tools such as ChatGPT, Claude, Codex and OpenCode to build indicators, strategies, scanners, dashboards and backtests. The program ends with agents that watch the market, reason with large language models and trade within strict risk rules.

Every module is taught live and hands-on. You code along with mentors, ask questions in real time and leave each session with working code. This write-up covers the November 2026 edition. Enroll on learn.marketcalls.in.

What's new in 5.0

Version 5.0 makes the whole trading workflow AI-driven and adds autonomous agents, AI Skills and deeper research tools.

Addition	What it means for you
AI-driven workflow	AI speeds up every step from idea to execution, with production-grade discipline
AI coding best practices	A repeatable loop: describe, generate, review, backtest, deploy
Autonomous trading agents	Agents built with agno that scan, decide and trade within your rules
LLMs and context engineering	How LLMs and agents work, and how to give them the right data, docs and constraints
AI Skills	Write your own skills to automate backtesting, execution and other workflows
MCP, APIs and WebSockets	Connect AI assistants and live market feeds to your trading stack
NautilusTrader	Event-driven backtesting alongside fast vectorBT research
Options backtesting	Single-leg and multi-leg modules, with Greeks from opengreeks
Regime detection	Decision API / JEV-based models that adapt strategies to market conditions
No-code strategies	Build and run strategies across 35+ brokers without writing code

Who it is for

The program suits both beginners and experienced traders.

• New to coding: you start with Python, NumPy and Pandas, with step-by-step guidance and practical examples.
• Experienced: advanced modules on options backtesting, machine learning, regime detection, AI Skills and autonomous agents let you go deeper.

Learning journey

The program runs in five stages across 15 modules:

1. Foundations (modules 1–3): Python, NumPy, Pandas, AI-assisted coding, LLMs and context engineering.
2. Data and research (modules 4–7): data engineering, custom indicators, dashboards and Nifty 500 scanners.
3. Strategy and testing (modules 8–11): trend and momentum strategies, backtesting, options, ML and regime detection.
4. Execution and risk (modules 12–14): OpenAlgo execution, sandbox testing, risk management, alerts and bots.
5. Autonomous AI (module 15): AI Skills and self-autonomous trading agents.

Curriculum: Foundations

Module 1: Python, NumPy and Pandas basics. This module builds the programming base every later module relies on. It covers:

• Python 3.12 core concepts
• NumPy for fast, vectorised maths on price arrays
• Pandas to load, clean and resample OHLCV data
• Your toolkit: VS Code or Cursor, GitHub and Jupyter Notebook

Module 2: AI tools to build indicators and strategies. You learn to use AI as a coding partner:

• ChatGPT for ideas and quick prototypes
• Claude for long-context coding and refactoring
• Codex for agentic coding inside your project
• OpenCode, an open-source terminal coding agent

You also learn the build loop: describe the idea, let AI write the code, review it, backtest, then deploy to sandbox before going live.

Module 3: LLMs, AI agents and context engineering. This module explains:

• How LLMs work: tokens, context windows and tool calling
• How agents plan, call tools and observe in a repeating loop
• Context engineering: giving the model the right instructions, market data, API docs, memory and MCP tools
• litellm, which gives you one API across many LLM providers

Curriculum: Data and research

Module 4: Data, databases, APIs and MCP. You build the data layer for research and execution:

• Managing equity, futures and options data
• Reading and writing to databases with DuckDB and SQL
• Docker containers for repeatable setups
• REST APIs, WebSockets and MCP for live ticks and AI tool access

Module 5: Custom indicators and strategies. You turn ideas into indicators, then into rule-based strategies:

• Standard indicators with TA-Lib and the OpenAlgo library
• Custom indicators beyond RSI and MACD
• Entry, exit and filter rules
• Prompting AI tools to build each one faster

Module 6: Visualization and momentum dashboards.

• Charting with Matplotlib, Seaborn and Plotly
• Equity curves, drawdowns and return distributions
• Interactive Streamlit dashboards in pure Python
• Momentum dashboards showing live leaders, laggards and rankings

Module 7: Scanners and screeners on the Nifty 500.

• Scanning all Nifty 500 stocks in one pass
• Screening on price, volume, trend and indicators
• Signals for breakouts, momentum and pattern matches
• Turning screener hits into alerts or automated trades

Curriculum: Strategy and testing

Module 8: Trend and momentum strategies. You design the strategy families used by systematic traders:

• Trend-following and trend-reversal strategies
• Time-series momentum, where each asset is measured against its own past
• Cross-sectional momentum, where assets are ranked against each other
• Rebalancing: rank, select the top names, allocate, hold and re-rank each cycle

Module 9: Backtesting, optimization and metrics. You validate strategies before risking capital:

• vectorBT for fast research at scale
• NautilusTrader for realistic, event-driven simulation
• Parameter optimization and walk-forward testing
• Key metrics: CAGR, drawdown, Sharpe ratio and win rate

Module 10: Options backtesting. You build your own options backtesting engine:

• Single-leg modules for directional calls and puts
• Multi-leg modules for straddles, strangles, spreads and condors
• Greeks (delta, gamma, theta, vega) with opengreeks
• Options chains, expiries and strike selection

Module 11: Machine learning and regime detection.

• ML foundations: features, labels and train/test splits
• Classical trading-signal models with scikit-learn
• Deep learning with PyTorch
• Decision API / JEV-based regime detection models

Curriculum: Execution, risk and autonomous AI

Module 12: Execution across 35+ brokers. You take strategies live with OpenAlgo:

• One API for orders, market data and positions across 35+ brokers
• No-code strategy building
• Sandbox testing without real capital
• A production-grade, AI-driven trading workflow

Module 13: Position sizing and risk management.

• Fixed, percentage and volatility-based position sizing
• Capital allocation and exposure limits
• Stop loss, target and trailing stop loss (SL, target, TSL)
• Risk management with the OpenAlgo Strategy Management module

Module 14: Alerts, Telegram and WhatsApp bots.

• Price, signal and risk-threshold alerts
• A Telegram bot for signals, P&L and commands
• A WhatsApp bot for trade updates
• Real-time monitoring of active strategies

Module 15: AI Skills and autonomous trading agents. In the capstone, you build agents that run the whole workflow within your guardrails:

• Building agents with agno: tools, memory and reasoning
• Using AI Skills for backtesting and execution, and writing your own

The agent loop works like this: observe live data, reason with an LLM, decide using strategy and regime, then act in sandbox before going live. Risk limits, SL and TSL stay on at all times.

Libraries and tools

You use every library below hands-on in the live sessions:

• Data: numpy, pandas
• Execution and indicators: openalgo, talib
• LLM gateway and database: litellm, duckdb
• AI agents: agno
• Deep learning and charts: pytorch, seaborn, matplotlib, plotly
• Dashboards and ML: streamlit, scikit-learn
• Backtesting: vectorbt, nautilus_trader
• Options Greeks: opengreeks

You'll also get comfortable with VS Code, GitHub, Jupyter Notebook, Docker and databases.

Requirements

Have these ready before the first session so you can code along from day one:

• Python 3.12 or higher
• VS Code or Cursor
• A ChatGPT or Claude subscription (the USD 20/month plan)
• A Windows or Mac laptop with a good internet connection

What you get

• Interactive live coding sessions with expert mentorship
• Lifetime access to the Marketcalls Private Trading Community
• A basic Python certification and the Quantflow 5.0 course certification
• PPTs, Python source code and session recordings, available for 1 or 2 years depending on your plan
• Ready-to-use strategies, AI Skills and agent workflows
• Hands-on experience building and deploying production-grade trading systems

FAQs

Can a beginner join? Yes. Beginners start with Python, NumPy and Pandas, with step-by-step guidance. Experienced traders can go deeper with the advanced modules.

What do I need before joining? Python 3.12+, VS Code or Cursor, and a ChatGPT or Claude subscription. See Requirements above.

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