Strategy Showcase

This folder contains one subfolder per strategy type. The goal is to show how to implement different logic patterns and data requirements with the investing_algorithm_framework, and to provide a starting point for your own research. The strategies are intentionally simple and are not meant to be profitable as-is — they are reference implementations of a pattern, not production-grade alpha.

Each subfolder is self-contained and ships with:

• README.md — what the strategy is, why it exists, fit level, parameters, caveats.
• strategy.py — the TradingStrategy (or pipeline) implementation.
• backtest.py — a runnable script that wires the strategy into an app and runs a backtest over a recent date range.
• requirements.txt — Python dependencies for that specific subfolder.

If a strategy type cannot be implemented today with the framework, the subfolder contains only a README.md explaining why it is currently not possible and what would be needed to make it possible.

Fit legend

Symbol	Meaning
✅	Sweet spot — framework is well-suited and example is end-to-end runnable.
🟢	Workable — implementable with some bookkeeping in user code.
🟡	Partial — pattern is shown but a key primitive is approximated.
🔴	Not currently possible — README only, explains why.

Index

1. Position-taking strategies (signal → orders)

#	Strategy	Fit	Description
01	trend_following	✅	Vector backtest of an EMA crossover.
02	mean_reversion	✅	Buy oversold, sell overbought (Bollinger + RSI).
03	cross_sectional_momentum	✅	Pipeline ranks symbols by 30d return, holds top-N.
04	multi_factor_portfolio	✅	Pipeline blends momentum + low-vol + liquidity factors.
05	pairs_trading	🟢	Z-score on BTC/ETH log-price spread.
06	funding_rate_carry	🟡	Carry pattern shown, but no native perp/funding-rate data provider.
07	event_driven_signal	✅	Volatility-breakout entry on bar-close events.
08	dca_accumulation	✅	Scheduled fixed-size buys (dollar-cost averaging).

2. Portfolio construction strategies

#	Strategy	Fit	Description
09	risk_parity	✅	Inverse-volatility weighting, monthly rebalance.
10	mean_variance_markowitz	✅	Quarterly Markowitz optimisation (scipy).
11	hierarchical_risk_parity	✅	López de Prado HRP with scipy clustering.
12	vol_targeting_overlay	✅	Scales gross exposure to a target portfolio vol.

3. Execution strategies

#	Strategy	Fit	Description
13	twap_vwap_execution	🟡	Bar-level TWAP slicing of a parent order.
14	implementation_shortfall	🔴	Needs intra-bar fills the framework does not currently model.
15	iceberg_orders	🔴	Requires venue-side iceberg primitives not abstracted by the framework.

4. Derivatives strategies

#	Strategy	Fit	Description
16	options_selling_income	🔴	No options instrument / chain primitives in the framework today.
17	volatility_trading	🔴	Requires a vol surface and option pricing pipeline.
18	delta_hedged_options	🔴	Requires Greeks pipeline and options venue support.
19	futures_basis_calendar	🟡	Pattern shown on spot proxy; no native futures-curve data model.

5. Slow market making

#	Strategy	Fit	Description
20	slow_market_making	🟢	Posts limit orders around mid every bar; capped inventory.

6. High-frequency / sub-second strategies (not currently possible)

#	Strategy	Fit	Description
21	active_market_making	🔴	Requires sub-ms event loop and L2 book — see iaf_realtime plan.
22	latency_arbitrage	🔴	Requires colocated low-latency venue connectivity.
23	hft	🔴	Bar-driven event loop is too slow for true HFT.
24	orderbook_microstructure	🔴	No L2 order-book data model.
25	news_nlp_subsecond	�	Event-driven trigger model planned — see #534.
26	ml_inference_high_freq	🔴	Inference loop dominated by per-iteration overhead.

Running an example

cd examples/strategies_showcase/01_trend_following
pip install -r requirements.txt
python backtest.py

Backtests fetch OHLCV from the public BITVAVO endpoint via ccxt (no API keys required for market data). The first run will download data and cache it; subsequent runs reuse the cache.