Quantitative Bond Trading on Interactive Brokers' Platform

Explore the fundamentals of bonds as fixed-income instruments, distinguish debt from equity, and practice bond math including valuations, duration, cash flows, and par value using Python.

Explore zero coupon bonds, their single cash flow and par value, and how discounting yields present value for bond valuation, including strip bonds as a bond composed of cash flows.

Understand discounting to compute present value by applying a discount rate to future cash flows, including par value, zero coupon bonds, and coupon paying bonds.

Explore how bond yield and yield curve determine present value by discounting cash flows with spot rates, understanding yield to maturity, and the price-yield relationship.

Explore how compounding frequency affects bond pricing and present value by adjusting coupon payments, period counts, and discount rates across annual, semiannual, quarterly, and continuous compounding.

Price a bond in Python by discounting coupon and par cash flows to their present value using coupon rate, discount rate, periods, and par value, with notes on continuous compounding.

Calculate a bond's yield to maturity by optimizing the price equation with the Brand Q optimizer in SciPy, iterating yields from 0 to 20% with up to 1000 iterations.

Explore how to price real-world bonds using market observable parameters, handling fractional periods, next coupon dates, and yield-to-maturity to compute clean and dirty prices while accounting for accrued interest.

Download and run the IBKR agent to connect your TWS or IB gateway to the AI trading terminal. Choose paper or live trading and start trading once connected.

Discover global scanners in Interactive Brokers' terminal to spot top gainers and losers with filters for market cap, volume, and price across US and Indian markets.

Explore bond trading on the Interactive Brokers platform using a paper trading account, adding bonds to a watch list, and placing limit orders with bid/ask details.

Learn to use the TWS Bond Scanner on Interactive Brokers to filter bonds by yield, maturity, and other parameters, customize the result table, export to CSV, and place trades directly.

Learn to create a bond contract object using the IB API, retrieve bond contract details, and implement a reusable us_bond function for accurate contract data.

Explore extracting issuer bonds by name, retrieve contract details such as CUSIP, rating, and description, and tailor output to show only descriptions for targeted quantitative analysis.

Learners implement a multiissuer bond details extractor, storing CUSIP, description, bond type, and issue date in a dictionary, then convert to pandas data frames for Apple, Intel, and GM.

Learn to extract historical data for fixed income bonds with the IB API, configuring ticker ID, bond contract, duration, bar size, and including data timing options.

Implement a bond historical data extractor by selecting bonds, requesting 60-day 5-minute candles via EClient's historical data, and printing bars with open, high, low, close, and volume.

Store bond bars in a bond data structure keyed by request ID, then convert to a pandas data frame and set the date as the index.

Map bond data to a key by using bond description from contract details, replacing cusip or isin in a dictionary of data frames; use 100-offset request IDs for contract details.

Learn to extract historical yield data from Interactive Brokers by using yield bid/ask in the request and daily data over a 100-day window.

Explore five basic bond order types on Interactive Brokers: market order, limit order, market if touched, limit if touched, trailing stop limit; and preview the one cancels all advanced order.

Learn to place a bond limit order on IB API: build a bond contract, obtain the next valid order id, and place the order; blank eTradeOnly and formCode.

Master market orders and trailing stop orders for bonds on the interactive brokers platform, understanding order types, execution realities, and the role of price transparency.

Explore one cancels all (OCA) groups to place multiple bond orders on the IBKR platform. When any order fills, the others in the group are canceled using OCA type 1.

Explore streaming real-time bond data with multithreading, examine limitations of bond price authenticity, and demonstrate using aggregated 250 ms market data, including bid/ask, last, and yield information.

Stream real time bond data with the IB API, capturing tick price, tick size, and yield for 5051 and 52, using a stream snapshot data helper and unique request IDs.

Stream real-time bond data in a background thread and store it in a dictionary keyed by sip, using a name map for tick types like bid price and ask price.

Apply Markowitz modern portfolio theory to optimize a bond portfolio, balancing expected return and volatility to trace the efficient frontier.

Implement modern portfolio theory in Python by extracting historical data for stocks and bonds, computing expected returns and volatility, and constructing the efficient frontier.

Extract close prices, compute daily returns, and derive correlation and covariance matrices to guide portfolio optimization through volatility and expected return.

Implement CAPM to estimate asset returns, compute beta and market return, and assess portfolio return and volatility to build an efficient frontier.

Refresh matrix multiplication and apply it to portfolio volatility using weight and covariance matrices in Python, underscoring how linear algebra enables portfolio optimization.

This lecture shows building five-asset portfolios with discretized weights, computing returns and volatility from a covariance matrix, and selecting the optimum portfolio using Sharpe on the efficient frontier.

Build the efficient frontier by aggregating returns for each risk level and transposing it onto the existing scatterplot to reveal the most optimum portfolio for every risk level.

Optimize weights for a predetermined portfolio by minimizing the Sharpe ratio with bounds and a sum-to-one constraint, using covariance and expected returns to assess portfolio volatility.