Government Bond Structuring Expert
Budget: $30 – $250 CAD
I am looking for a quantitative finance freelancer to help me implement, backtest, and document fixed-income trading strategies as part of a university-level research project in Fixed Income Securities.
This is a serious quantitative project, close to what you’d see in asset management, hedge funds, or rates strategy teams. Strong finance intuition matters as much as coding skills.
Part A – Macro & Rates View (Light support)
Assist in structuring a macro analysis framework (growth, inflation, employment).
Help translate macro views into:
Directional call on 10Y government bond yield
Directional call on FX
No forecasting magic needed — logic, clarity, and economic justification matter.
Part B – Yield Curve Trading Strategies
You will help code, test, and analyze yield-curve strategies using US Treasury data (2001–2025).
Strategy 1: Nelson-Siegel Factor Trading
Fit Nelson-Siegel curve monthly
Compare actual vs modeled yield curve
Build DV01-neutral long/short bond portfolios
Monthly rebalancing
Strategy 2: Yield Curve Spread Trading
Implement curve spreads (2x5, 2x10, or 5x10)
Entry/exit rules based on:
Rolling mean
Standard deviation thresholds
Backtest performance
Performance Metrics (for both):
Average returns
Volatility
Sharpe ratio
Skewness
Risk discussion (curve steepening, flattening, twists)
Part C – Credit Relative Value Strategy
Work with TRACE corporate bond data
Merge with Compustat fundamentals (via WRDS)
Build a monthly cross-sectional regression model:
Yield vs leverage, ROA, size, duration, convexity
Use regression residuals as mispricing signal
Construct long-short portfolios
Monthly rebalancing & performance evaluation
Technical Requirements
Python or R (Python preferred)
Strong knowledge of:
Fixed income instruments
DV01, duration, convexity
Yield curves
Regression & portfolio construction
Clean, well-commented code
Ability to explain financial intuition, not just code
Deliverables
Working scripts (Python / R)
Clear explanation of strategy logic
Summary tables of performance metrics
Figures/plots for yield curves & strategy results
Code ready to be attached as appendix
Project Context
Academic research project (Master-level / advanced undergraduate)
High standards: clarity, rigor, and financial logic
No “black-box” solutions — everything must be explainable
Timeline
Flexible but ideally completed within 2–3 weeks
Milestones possible (Part B → Part C)
Budget
Open / negotiable
Will prioritize quality and expertise over lowest price
Ideal Freelancer Profile
Background in quant finance, financial engineering, or economics
Experience with rates or credit markets
Has done backtesting projects before
Can communicate clearly and think like a portfolio manager
This is a serious quantitative project, close to what you’d see in asset management, hedge funds, or rates strategy teams. Strong finance intuition matters as much as coding skills.
Part A – Macro & Rates View (Light support)
Assist in structuring a macro analysis framework (growth, inflation, employment).
Help translate macro views into:
Directional call on 10Y government bond yield
Directional call on FX
No forecasting magic needed — logic, clarity, and economic justification matter.
Part B – Yield Curve Trading Strategies
You will help code, test, and analyze yield-curve strategies using US Treasury data (2001–2025).
Strategy 1: Nelson-Siegel Factor Trading
Fit Nelson-Siegel curve monthly
Compare actual vs modeled yield curve
Build DV01-neutral long/short bond portfolios
Monthly rebalancing
Strategy 2: Yield Curve Spread Trading
Implement curve spreads (2x5, 2x10, or 5x10)
Entry/exit rules based on:
Rolling mean
Standard deviation thresholds
Backtest performance
Performance Metrics (for both):
Average returns
Volatility
Sharpe ratio
Skewness
Risk discussion (curve steepening, flattening, twists)
Part C – Credit Relative Value Strategy
Work with TRACE corporate bond data
Merge with Compustat fundamentals (via WRDS)
Build a monthly cross-sectional regression model:
Yield vs leverage, ROA, size, duration, convexity
Use regression residuals as mispricing signal
Construct long-short portfolios
Monthly rebalancing & performance evaluation
Technical Requirements
Python or R (Python preferred)
Strong knowledge of:
Fixed income instruments
DV01, duration, convexity
Yield curves
Regression & portfolio construction
Clean, well-commented code
Ability to explain financial intuition, not just code
Deliverables
Working scripts (Python / R)
Clear explanation of strategy logic
Summary tables of performance metrics
Figures/plots for yield curves & strategy results
Code ready to be attached as appendix
Project Context
Academic research project (Master-level / advanced undergraduate)
High standards: clarity, rigor, and financial logic
No “black-box” solutions — everything must be explainable
Timeline
Flexible but ideally completed within 2–3 weeks
Milestones possible (Part B → Part C)
Budget
Open / negotiable
Will prioritize quality and expertise over lowest price
Ideal Freelancer Profile
Background in quant finance, financial engineering, or economics
Experience with rates or credit markets
Has done backtesting projects before
Can communicate clearly and think like a portfolio manager