# coding-kitties/investing-algorithm-framework

Framework for quantitative trading. Complete framework for development, backtesting, and deploying automated trading algorithms and trading bots.

Repository: https://github.com/coding-kitties/investing-algorithm-framework
Canonical: https://ross.abutalabs.com/products/investing-algorithm-framework
Homepage: https://coding-kitties.github.io/investing-algorithm-framework/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: trading-bot, cryptocurrency, algorithmic-trading, trade, python, trading, trading-strategies, backtesting, backtesting-trading-strategies, trading-bots, quantitative, quantitative-analysis, quantitative-finance, quantitative-trading
Last push: 2026-08-20T13:48:56+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 98, release rhythm 73, longevity 100
- inputs: {"age_days": 2450, "days_push": 13, "days_rel": 101, "gap_med": 1.0, "n_releases_24m": 163}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1714, forks 250 (observed 2026-08-28T04:05:26.097449+00:00)

## What it is
A Python framework for developing, backtesting, and deploying quantitative trading algorithms and trading bots. It supports vector and event-driven backtesting, strategy comparison dashboards, and production deployment to local or cloud environments.

## Use cases
- backtest crypto trading strategies on historical data
- build and deploy an automated trading bot
- compare multiple trading strategies in a dashboard
- run Monte Carlo simulations to validate strategy robustness
- compute performance metrics like Sharpe ratio and max drawdown
- deploy a live trading algorithm to AWS Lambda or Azure Functions
- rank and filter a universe of symbols with cross-sectional pipelines

## When to choose
- you want a full quant workflow (strategy, backtest, compare, deploy) in one Python framework
- you trade crypto or other markets and need both vector and event-driven backtesting
- you need built-in performance analytics and statistical robustness testing
- you want to move from research to live trading with the same codebase

## When to avoid
- you need a lightweight backtesting library without deployment features
- you require broker integrations or asset classes not supported by the framework
- you prefer point-and-click trading platforms over writing Python code
- you need guaranteed low-latency high-frequency trading execution

## Facets
- artifact type: framework
- maturity: active
- function: trading, benchmarking, analytics, workflow-automation
- domain: fintech
- platform: python, cross-platform, cloud
- tags: algorithmic-trading, backtesting, trading-bot, quantitative-trading, crypto-trading, strategy-development, event-driven-backtesting, vector-backtesting, portfolio-management, live-trading, cryptocurrency, quantitative-finance, automation

## Member repositories
- coding-kitties/investing-algorithm-framework (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:26.097449+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:34:38.750291+00:00, confidence not recorded.
  - readme: https://github.com/coding-kitties/investing-algorithm-framework (fetched 2026-08-28T04:05:26.097449+00:00, sha 1f71cd9cba6e)
  - homepage: https://coding-kitties.github.io/investing-algorithm-framework/ (fetched 2026-08-29T11:10:33.121035+00:00, sha 89f628f8d15f)
- Data as of 2026-08-30T08:39:29.467469+00:00.