TOMORROW / KNOWLEDGE & METHODS
Tomorrow LibraryQuantitative knowledge & methods

Code

Code you can inspect.

Start with the runnable workbench, then explore earlier source implementations in their original context.

Runnable workbench

Data loading

Validated OHLCV loading and a Prosperity midpoint adapter.

CSV input; explicit timestamp parsing; duplicate and OHLC checks.

New educational reference implementation

Runnable workbench

Feature development

Trailing returns, volatility and standardised price displacement.

Rolling features use observations available at each decision time.

New educational reference implementation

Runnable workbench

Forecasting baselines

Ridge regression with training-only scaling and chronological label separation.

A transparent baseline for experiments, with a zero forecast for comparison.

New educational reference implementation

Runnable workbench

Portfolio management

Equal weights, inverse-volatility weights and exposure constraints.

Long-only weights, cash allowance and per-asset caps.

New educational reference implementation

Runnable workbench

Risk management

Drawdowns, historical VaR / expected shortfall and return summaries.

Explicit annualisation and a signed-loss convention.

New educational reference implementation

Runnable workbench

Backtesting

Single-asset account simulation with next-open orders, fees and slippage.

Cash, shares, equity and an auditable trade ledger.

New educational reference implementation

Historical source

Vector backtesting examples

Historical vector backtester; the file attributes Yves J. Hilpisch / The Python Quants.

WTech / TECHNICAL/SMAVectorBacktester.py

See the original file and its attribution

Historical source

Risk and portfolio utilities

A larger collection of risk and allocation routines; some loaders reference external data files.

WTech / TECHNICAL/edge_risk_kit.py

See the original file and its attribution

Historical source

Prototype account backtester

Early account simulation with script-level data downloads. Review accounting and execution assumptions before reuse.

TMRW / BACKTEST/Engine.py

See the original file and its attribution

Historical source

Feature utility collection

Earlier returns, moving-average and related utilities. Historical code rather than the tested workbench.

Knowledgebase / 1. Quantitative Finance/Library.py

See the original file and its attribution

Historical source

Strategy notebooks

Explore the original notebook and its assumptions in the source repository.

WTech / STRATEGIES/Strategy.ipynb

See the original file and its attribution

Historical source

Portfolio notes

An earlier portfolio-management research note.

Knowledgebase / 1. Quantitative Finance/PortfolioManagement/Endavu.md

See the original file and its attribution

Find your next reference.