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Derivatives trading: theory to algo execution
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Module 1
Mathematical & Python Foundations for Quant Trading
Partial Derivatives and Financial Sensitivities
Compute partial derivatives and interpret them as sensitivity measures in a financial context (e.g., option Greeks).
Expected Value of Continuous Random Variables
Explain how a definite integral is used to calculate the expected value of a continuous random variable.
Brownian Motion: Properties and Applications in Asset Pricing
Describe Brownian motion, its key properties, and its connection to modeling asset price movements.
4
Applying Itô's Lemma to Derivative Pricing
Apply Itô's Lemma to derive how a derivative's price changes given the dynamics of the underlying asset.
5
Vectorized Financial Data Analysis with NumPy and Pandas
Use NumPy arrays and Pandas DataFrames to perform vectorized computations and time-series manipulations on financial data.
6
Rolling Window Statistics for Time-Series Data
Calculate rolling window statistics such as moving averages and historical volatility on time-series data.
7
API Data Extraction for Market Analysis
Fetch historical market data using APIs and prepare it for analysis.
Derivative Payoff Diagrams with Matplotlib/Plotly
Create payoff diagrams and P&L curves for derivative positions using Matplotlib or Plotly.
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