Certificate Programme · Dubai / Online

AI-Assisted Algorithmic Trading and Financial Data Analysis

From data collection to live deployment. Learn how to design, backtest, and automate trading strategies — and how AI is changing the way professionals analyse financial markets.

Most traders and finance professionals still rely on manual processes — checking charts, copying data into spreadsheets, executing orders by hand. Algorithmic trading changes that. A well-designed system can scan markets, test hypotheses, and execute strategies consistently and without emotion.

This programme teaches you how to build those systems using Python — from collecting and cleaning financial data, to defining and backtesting strategies, to connecting to broker and exchange APIs and deploying in a cloud environment.

As of 2026, AI is also changing how market professionals work. This programme introduces practical machine learning for financial markets and AI-enhanced analysis techniques — so participants leave with a modern, complete picture of the field.

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Quick facts

Total duration
15 hours live
Format
6 sessions × 2.5 hrs
Delivery
In-person or Zoom
Level
Intermediate · basic Python recommended
Cohort size
Maximum 12 participants
Location
Dubai Knowledge Park
Certificate
Innosoft Gulf · KHDA

Prerequisites

Basic Python familiarity is recommended. Participants should be comfortable running scripts and working with data in a notebook environment. No advanced mathematics or finance degree is required. If you are coming from the AI-Assisted Cryptocurrency Trading programme, this is the natural next step.

What you will be able to do after this programme

✓Collect, clean, and structure financial market data from APIs and web sources using Python.
✓Calculate and visualise technical indicators and perform exploratory analysis on price and volume data.
✓Translate a trading idea into a systematic rule-based strategy with defined entry, exit, and risk management conditions.
✓Backtest a strategy on historical data and evaluate its performance using standard metrics.
✓Connect to a broker or exchange API and place orders programmatically in a paper trading or live environment.
✓Deploy a trading system to a cloud server so it operates continuously without a local machine.
✓Apply machine learning models to financial data for signal generation and strategy improvement.
✓Use AI tools to analyse news and unstructured market data as an additional signal source.

Curriculum — 10 modules · 15 hours of live instruction

Module 1Environment setup
  • Python environment for financial data work
  • Key libraries: pandas, numpy, matplotlib, requests, ccxt
  • Jupyter notebooks and cloud notebook environments
  • Connecting to data sources and APIs
Module 2Financial data collection and alternative data
  • Market data sources: price, volume, OHLCV from exchanges and brokers
  • Alternative data sources: news feeds, earnings call transcripts, social media signals
  • Scraping and collecting data from web sources and APIs
  • Handling missing data, time zones, and data quality
  • Building a clean, structured financial dataset ready for analysis and modelling
Module 3Financial data analysis and AI-assisted pattern detection
  • Exploratory analysis of price and volume data
  • Technical indicators: moving averages, RSI, Bollinger Bands, MACD
  • Returns, volatility, and correlation analysis
  • Using AI tools to surface patterns and anomalies that are difficult to detect manually
  • Visualising market data for insight and presentation
Module 4Broker and exchange API connection
  • Connecting to broker and crypto exchange APIs using Python
  • Authenticating, placing orders, and retrieving account data
  • Rate limits, error handling, and safe API usage
  • Paper trading and sandbox environments for testing
Module 5Strategy definition and AI-assisted hypothesis generation
  • Types of trading strategies: trend-following, mean reversion, momentum
  • Translating a trading idea into a systematic rule set
  • Using AI tools to generate, screen, and refine strategy hypotheses from market data
  • Entry and exit conditions, position sizing, and risk management
  • Common mistakes in strategy design — and how AI can help and hurt
Module 6Strategy backtesting and validation
  • Building a backtesting framework in Python
  • Evaluating strategy performance: returns, drawdown, Sharpe ratio
  • Overfitting, look-ahead bias, survivorship bias, and data leakage
  • Forward testing and walk-forward validation
  • Using AI to evaluate parameter sensitivity and stress-test strategy assumptions
Module 7Cloud environment setup
  • Why cloud deployment matters for trading systems
  • Setting up a cloud server for continuous operation
  • Scheduling, logging, and monitoring your system
  • Managing credentials and API keys securely
Module 8Trading system deployment and AI-assisted monitoring
  • Deploying your strategy to a live cloud environment
  • Real-time data feeds and order execution
  • AI-assisted performance monitoring and anomaly detection in live systems
  • Handling failures gracefully and moving from paper trading to live operation safely
  • Human oversight: what AI should decide and what requires human judgment
Module 9Machine learning for financial markets
  • Where machine learning adds genuine value in trading — and where it does not
  • Supervised learning for price direction and signal classification
  • Feature engineering from market data and alternative data sources
  • Gradient boosting and ensemble methods used by professional quant firms
  • Evaluating ML model performance in a financial context
Module 10AI-assisted trading: LLMs, sentiment, and professional practice
  • How professional trading desks and quant funds are using AI in 2026
  • Financial LLMs (FinBERT) for sentiment analysis from news, earnings calls, and social media
  • Using large language models to process unstructured financial information at scale
  • AI for idea generation and research — humans accountable for risk and execution
  • Practical limitations, explainability, and governance in AI-assisted trading
  • Building a responsible AI-assisted trading workflow from research to live deployment

Who this programme is for

Traders and investors
Professionals who want to move from manual analysis to systematic, data-driven strategies they can test and automate.
Finance professionals
Analysts, portfolio managers, and risk professionals who work with financial data and want to automate workflows and build quantitative tools.
Data analysts moving into finance
Analysts with Python skills who want to apply them specifically to financial markets, trading systems, and market data.
Software developers in fintech
Developers who want to build trading tools, connect to exchange APIs, or work in a financial technology role.
Aspiring quantitative analysts
Professionals who want to build a practical foundation in quantitative methods, backtesting, and systematic strategy design.
Corporate quantitative teams
Teams at banks, asset managers, brokerages, or trading firms looking to upskill on Python, automation, and AI-enhanced analysis.

Your instructor

Ahmed El Koutbia
Ahmed El Koutbia
Founder and Lead Instructor — Innosoft Gulf
Ahmed began his career at the Chicago Board Options Exchange (CBOE) and has since built and deployed production trading systems and AI-driven market analysis tools. He brings together deep financial markets experience and practical software engineering — covering everything from data collection and strategy backtesting to live API connectivity and cloud deployment. He has delivered corporate AI, big data, and technology training to professionals from organisations across the region including Aramco, DP World, Visa, the UAE Central Bank, Motorola Solutions, New York University, and the American University of Sharjah.
Saudi Aramco DP World Visa UAE Central Bank Motorola Solutions New York University American University of Sharjah

Your learning path

AI-Assisted Crypto Trading
→
AI-Assisted Algorithmic Trading
→
Advanced Quantitative Strategies

Alumni of the AI-Assisted Cryptocurrency Trading programme receive priority enrolment. Each programme builds directly on the previous.

Upcoming cohorts

Option A · Next cohort
Weekend cohort
Session 1 — Saturday, 20 June 2026
Session 2 — Sunday, 21 June 2026
Session 3 — Saturday, 27 June 2026
Session 4 — Sunday, 28 June 2026
Session 5 — Saturday, 4 July 2026
Session 6 — Sunday, 5 July 2026
6:00 PM – 8:30 PM GST · In-person or Zoom
Option B
Weekday evening cohort
Dates to be announced — join the waitlist when you apply.
Mon / Wed / Fri · 6:30 PM – 9:00 PM GST · In-person or Zoom
Option C · Groups of 4+
Corporate intensive
Custom dates available
5 sessions × 3 hours · 10:00 AM – 1:00 PM GST
Best for quantitative teams, finance departments, and trading desks

Programme fee

USD 1,250 per participant approximately AED 4,590 · VAT inclusive
What’s included
  • 15 hours of live instruction across 6 sessions
  • Hands-on implementation in every session — real data, real APIs, real systems
  • Cloud environment guidance for strategy deployment
  • Innosoft Gulf certificate of completion
  • KHDA attestation available on request
  • Priority enrolment for the next programme in the learning path

Seats limited to 12 per cohort · Corporate group bookings of 4 or more receive a custom schedule and preferential pricing — contact info@innosoftgulf.com or WhatsApp +971 52 351 7403

Frequently asked questions

Do I need to know Python already?
Basic Python familiarity is recommended — you should be comfortable running scripts and working with data in a notebook. You do not need to be an advanced programmer. The course focuses on applying Python to financial problems, not teaching Python from scratch.
Do I need a live trading account?
No. The course uses sandbox and paper trading environments throughout. You will connect to real APIs but execute trades in test mode. No real capital is required during the programme.
Is this relevant for crypto as well as traditional markets?
Yes. The techniques covered apply to both traditional financial markets and crypto markets. The API connection module specifically covers crypto exchange APIs (using ccxt) alongside traditional broker APIs.
Can my organisation send a group?
Yes. The Corporate Intensive option is available for quantitative teams and finance departments on custom dates. Contact us at info@innosoftgulf.com or WhatsApp +971 52 351 7403.

This programme is intended for educational and market analysis purposes only. It does not constitute financial advice and does not guarantee trading results. Algorithmic trading and financial markets carry significant risk of loss.

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