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Improve your backtest
Backtesting with realistic microstructure cost
A price-only backtest quietly assumes you can trade at the mid-price for free. Real fills pay the half-spread, move the book, and miss when liquidity thins out. This notebook builds a microstructure transaction-cost model — half-spread plus market impact — from Aperiodic's L1 price, impact, and slippage metrics, checks it against measured slippage, and shows how different cost assumptions reshape the same simple momentum backtest.
Sample data
Funding-aware perpetual backtests
A perpetual-futures position does not only earn the price move — it also pays or receives funding at discrete settlement prints. A price-only backtest ignores those cash flows and can misattribute carry to trading alpha, most of all for a persistently long strategy in a rising, positive-funding market. This notebook isolates realised funding and shows exactly where it binds: it is a first-order cost for a directional (long-biased) position and nets out for a direction-neutral one.
Sample data
Find new alpha
Alpha Discovery
We define Directional Alpha as a signal that exhibits a consistent positive or negative relationship with forward returns.
Sample data
Predicting Short-Term Crypto Returns with Market Microstructure
Sample data
Intro to Aperiodic Flow Metrics
This notebook introduces order-flow analytics with the `aperiodic` Python package. We focus on a single large-cap instrument — Binance BTC perpetuals (`perpetual-BTC-USDT:USDT`) — over the exact six-month window from September 1, 2025 through February 28, 2026, using 1-hour observations.
Execute smarter
Timing execution around liquidity
Execution cost is not constant through the day. Spreads widen, depth thins, and order flow turns adverse together around stressed periods. For a moderate-frequency order, when you execute can matter more than shaving microseconds off the route. This notebook measures when liquidity is deep, spreads are narrow, and flow is least toxic, then ranks the hours by modelled execution cost and cross-checks that ranking against measured slippage.
Sample data
Intro to Aperiodic L1 Price Metrics
This notebook explores top-of-book (L1) pricing with Aperiodic's `l1_price` metric for Binance BTC perpetuals from September 1, 2025 through February 28, 2026 at 5-minute granularity.
Learn the metrics
Intro to Aperiodic Derivatives Metrics
This notebook introduces a simple derivatives regime dashboard using Aperiodic's `funding`, `open_interest`, and `basis` metrics for Binance BTC perpetuals over September 1, 2025 → February 28, 2026 at 5-minute frequency.
Sample data
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