Using Generative AI for Predecting Market Movement

Meet the instructor, a seasoned finance professional turned data scientist and algo trader, who blends risk analysis, data science, and GPT-driven finance use cases.

Learn to install and configure Anaconda for Python and algorithmic trading, including choosing the right operating system installer, adding Anaconda to your path, and starting with Anaconda3.

Learn how Python represents data types—integers, floats, strings, and booleans—with examples like 1145, -502, and 3.0, and check a variable’s type using the type command.

Learn how to type strings in python with single or double quotes, verify with the type function, print outputs, and concatenate strings with variables, including casting between strings and numbers.

Explore time series visualization with Plotly for financial analysis, plotting interactive charts in three lines of code, and build TradingView–style graphs for algorithmic trading.

Explore scatterplot graphs to visualize time-series market data with Plotly, building interactive charts using x and y axes, markers, and sliders, illustrated by Apple stock data.

Explore candlestick charts with open, high, low, and close data in Plotly, and learn to update layouts, add annotations, and shapes to highlight periods like bull markets.

Discover the basics of trading by exploring market players, trading strategies, and financial products; build your own diversified and competitive portfolio through fundamental and technical analysis, and algorithmic optimization.

Explore generative AI models and the transformer architecture, learn their limitations and solutions, and practice with OpenAI and Gemini APIs, including sentiment analysis and market trend use cases.

Learn the essentials of neural language models, including the transformer architecture with self-attention and encoder-decoder structure, and how pre-training, fine-tuning, prompting, and retrieval augmented generation address limitations in finance.

Explore transformer fundamentals, including tokenization, embedding, and positional encoding, and learn how self-attention, multi-head attention, and the encoder-decoder architecture handle long sequences in natural language processing.

Learn how large language models pre-train on vast unstructured data from the internet and code, then fine-tune for domain tasks. Discover instruction using prompt engineering and retrieval augmented generation.

Explore prompt engineering to guide llms with prompts, using zero-shot and few-shot learning with one, two, or n-shot examples, enabling precise sentiment classification and instruction-based outputs.