SINGLE - just straight one pass meaning fetching data (local/remote), the data cleaning(only main session, no extended market) indicators and entry-points (in given time window) and backtest with SINGLE parameter values and strategy result. Used for basic strategy research.
MULTI - basic strategy pass taken from step above but with hyperparameters testing, result for each strategy for each parameter combination in vbt portfolio dataset. To evaluate performance of each(best) parameter combination, maybe display as parallel coordinates plot.

I can imagine it can be used to create MVP (not use with notebooks as they become clumsy with lot of data), hyperparameter testing should be refactored, walk forward optimization and cross validation should be added.

Maybe start exploration with /research/strat_ORDER_IMBALANCE/v2_single.ipynb

strategies I have worked on so far

CANDLE_GAPS - candle gaps of various size within high resolution ohlcv (1s) as source for entry signals
LINREG_MULTI - combination of linear regressions on different length and time resolutions
SUPERTREND - supetrend on different resolutions
ORDER_IMBALANCE - order imbalanced columns calculated from trade data as new columns on OHLCV bars
TIME_ENTRIES - just idea for time based entries to leverage random movement of price

Data preparation

Usually for strategies above 1second OHLCV data are prepared by vectorized aggregator based on trade data for given period fetched from Alpaca (simple not funded registration is needed there - both crypto/stocks). Aggregator is in prepare_aggregated_data.ipynb. Alpaca secret and key is loaded from .env it contains just

ACCOUNT1_LIVE_API_KEY=_YOUR_ALPACA_API_KEY
ACCOUNT1_LIVE_SECRET_KEY=YOUR_ALPACA_SECRET_KEY

It also uses some code from my v2realbot platform which is imported as requirement.

I usuall prepare these data in advanced and store it in .parquet format, usually I prepare few months of 1s data as highest resolution and then during the research I just load this parquet and then resample it to lower frequencies for signals, but usually backtest on 1s frequency in order to get highest precision of entry/exit.

Research for v2realbot

Overview

Strategy research and development tracker. Serves as a central hub for strategizing, idea generation, and performance tracking of the stragies

Purpose

This repository is established as an issue tracker to:

Facilitate the proposal and discussion of new trading strategies.
Track the progress and refinement of existing strategies.
Document and share insights, research findings, and performance analyses.

Getting Started

Proposal of New Strategies: To propose a new strategy, create a new issue with a clear and descriptive title. Outline your strategy, including its rationale, intended market conditions, and expected outcomes.
Discussion and Feedback: Use the issues section for ongoing discussions, feedback, and collaborative refinement of strategies.
Strategy Documentation: Each strategy should be documented in detail on the Wiki pages, including its parameters, implementation guidelines, and any relevant backtesting results. (Note: documentation either here or on trading.mujdenik.eu - we will see what's better in practice)
Problem Reporting and Optimization: Report any issues or suggest optimizations for existing strategies through the Issues section, providing relevant data and analysis to support your observations.
Contribution Guidelines: Please refer to CONTRIBUTING.md for detailed guidelines on how to contribute effectively to this repository.

Collaboration Tools

Issues: For proposing, discussing, and tracking strategies.
Wiki: For detailed documentation and resource sharing, please se trading.mujdenik.eu
Research folder: Notebook dedicated do each strategy.

Integration with v2realbot

This repository operates in tandem with the v2realbot repository. Ensure all strategies are compatible and follow the guidelines for integration into the v2realbot Trading Platform.