| Name | tradeflow JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | A package to simulate autocorrelated time series of signs |
| upload_time | 2025-07-24 07:29:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.8 |
| license | None |
| keywords |
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|
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No requirements were recorded.
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<h1 align="center">
<img src="https://raw.githubusercontent.com/MartinGangand/tradeflow/main/doc/_static/tradeflow_logo.svg" width="650" alt="Tradeflow Logo" />
</h1>
<p align="center">
<a href="https://pypi.org/project/tradeflow/"><img alt="PyPI Latest Release" src="https://img.shields.io/pypi/v/tradeflow" /></a>
<a href="https://pypi.org/project/tradeflow/"><img alt="Python Versions" src="https://img.shields.io/pypi/pyversions/tradeflow.svg" /></a>
<a href="https://github.com/MartinGangand/tradeflow/actions/workflows/ci.yml?query=branch%3Amain"><img alt="CI" src="https://github.com/MartinGangand/tradeflow/actions/workflows/ci.yml/badge.svg?branch=main" /></a>
<a href="https://codecov.io/github/MartinGangand/tradeflow"><img alt="Coverage" src="https://codecov.io/github/MartinGangand/tradeflow/graph/badge.svg?token=T5Z95K8KRM" /></a>
</p>
As stated in the book _Trades, Quotes and Prices: Financial Markets Under the Microscope_ by Bouchaud et al. [[1, Chapter 10]](#1):
> *"The signs of arriving market orders have long-range autocorrelations."*
**tradeflow** is a Python package for fitting and simulating autocorrelated time series of signs.
## Features
* **Fit autoregressive (AR) models** to time series of signs:
- Automatic model order selection using the Partial Autocorrelation Function (PACF)
- Parameter estimation methods: Yule-Walker equations, Maximum Likelihood Estimation, and Burg's method
* **Simulate autocorrelated sign sequences** from fitted models
* **Summarize simulations** by comparing original and simulated time series:
- Plot the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF)
- Compute the proportion of buy signs ($+1$)
- Compute descriptive statistics on consecutive sign runs (mean, standard deviation, percentiles)
* **Perform statistical tests**:
- Augmented Dickey-Fuller (ADF) test for time series stationarity
- Breusch-Godfrey test for residual autocorrelation
## Usage
Fit an autoregressive model to a time series of signs (e.g., `[1, 1, -1, -1, 1, -1, 1, 1, 1, 1, ...]`):
```python
from tradeflow import AR
ar_model = AR(signs=signs, max_order=50, order_selection_method='pacf')
ar_model.fit(method="yule_walker", check_stationarity=True, check_residuals=True)
```
<br>
Simulate an autocorrelated time series of signs from the fitted AR model:
```python
ar_model.simulate(size=10_000)
# [1, -1, 1, 1, 1, 1, -1, -1, 1, 1, ...]
```
<br>
Compare the ACF and PACF of the original and simulated time series:
```python
ar_model.simulation_summary(plot=True)
```
<img src="https://raw.githubusercontent.com/MartinGangand/tradeflow/main/doc/_static/simulation_summary.png" width="950" alt="Simulation summary" />
## Installation
tradeflow can be installed with pip:
```bash
pip install tradeflow
```
## Documentation
Read the full documentation [here](https://martingangand.github.io/tradeflow/).
## Background
This package is inspired by the book _Trades, Quotes and Prices: Financial Markets Under the Microscope_ by Bouchaud et al. [[1, Chapters 10 and 13]](#1).
The book discusses the highly persistent nature of the sequence of binary variables $\epsilon_t$ that describe the direction of market orders.
That is, buy orders ($\epsilon_t = +1$) tend to follow other buy orders, and sell orders ($\epsilon_t = -1$) tend to follow other sell orders, often for very long periods.
We assume that the time series of signs $\epsilon_t$ is well modelled by a **discrete autoregressive process** of order $p > 0$. In this framework, the best predictor of the next market order sign (just before it occurs) is a linear combination of the past signs:
```math
\hat{\epsilon}_t = \sum_{k=1}^{p} \mathbb{K}(k) \epsilon_{t-k}
```
Here, $\mathbb{K}(k)$ can be inferred from the sign autocorrelation function using the Yule-Walker equations.
$p$ determines how many past signs are used ($\forall \ell > p, \mathbb{K}(\ell) \approx 0$).
As a result, the probability that the next sign is $\epsilon_t$ is:
```math
\mathbb{P}_{t-1}(\epsilon_t) = \frac{1 + \epsilon_t \hat{\epsilon}_t}{2}
```
## References
<a id="1">[1]</a>
Bouchaud J-P, Bonart J, Donier J, Gould M. _Trades, Quotes and Prices: Financial Markets Under the Microscope_. Cambridge University Press; 2018.
Raw data
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"description": "<h1 align=\"center\">\n<img src=\"https://raw.githubusercontent.com/MartinGangand/tradeflow/main/doc/_static/tradeflow_logo.svg\" width=\"650\" alt=\"Tradeflow Logo\" />\n</h1>\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/tradeflow/\"><img alt=\"PyPI Latest Release\" src=\"https://img.shields.io/pypi/v/tradeflow\" /></a>\n <a href=\"https://pypi.org/project/tradeflow/\"><img alt=\"Python Versions\" src=\"https://img.shields.io/pypi/pyversions/tradeflow.svg\" /></a>\n <a href=\"https://github.com/MartinGangand/tradeflow/actions/workflows/ci.yml?query=branch%3Amain\"><img alt=\"CI\" src=\"https://github.com/MartinGangand/tradeflow/actions/workflows/ci.yml/badge.svg?branch=main\" /></a>\n <a href=\"https://codecov.io/github/MartinGangand/tradeflow\"><img alt=\"Coverage\" src=\"https://codecov.io/github/MartinGangand/tradeflow/graph/badge.svg?token=T5Z95K8KRM\" /></a>\n</p>\n\nAs stated in the book _Trades, Quotes and Prices: Financial Markets Under the Microscope_ by Bouchaud et al. [[1, Chapter 10]](#1):\n\n> *\"The signs of arriving market orders have long-range autocorrelations.\"*\n\n**tradeflow** is a Python package for fitting and simulating autocorrelated time series of signs.\n\n## Features\n* **Fit autoregressive (AR) models** to time series of signs:\n - Automatic model order selection using the Partial Autocorrelation Function (PACF)\n - Parameter estimation methods: Yule-Walker equations, Maximum Likelihood Estimation, and Burg's method\n* **Simulate autocorrelated sign sequences** from fitted models\n* **Summarize simulations** by comparing original and simulated time series:\n - Plot the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF)\n - Compute the proportion of buy signs ($+1$)\n - Compute descriptive statistics on consecutive sign runs (mean, standard deviation, percentiles)\n* **Perform statistical tests**:\n - Augmented Dickey-Fuller (ADF) test for time series stationarity\n - Breusch-Godfrey test for residual autocorrelation\n\n## Usage\nFit an autoregressive model to a time series of signs (e.g., `[1, 1, -1, -1, 1, -1, 1, 1, 1, 1, ...]`):\n\n```python\nfrom tradeflow import AR\n\nar_model = AR(signs=signs, max_order=50, order_selection_method='pacf')\nar_model.fit(method=\"yule_walker\", check_stationarity=True, check_residuals=True)\n```\n<br>\n\nSimulate an autocorrelated time series of signs from the fitted AR model:\n\n```python\nar_model.simulate(size=10_000)\n# [1, -1, 1, 1, 1, 1, -1, -1, 1, 1, ...]\n```\n<br>\n\nCompare the ACF and PACF of the original and simulated time series:\n\n```python\nar_model.simulation_summary(plot=True)\n```\n\n<img src=\"https://raw.githubusercontent.com/MartinGangand/tradeflow/main/doc/_static/simulation_summary.png\" width=\"950\" alt=\"Simulation summary\" />\n\n## Installation\ntradeflow can be installed with pip:\n\n```bash\npip install tradeflow\n```\n\n## Documentation\nRead the full documentation [here](https://martingangand.github.io/tradeflow/).\n\n## Background\nThis package is inspired by the book _Trades, Quotes and Prices: Financial Markets Under the Microscope_ by Bouchaud et al. [[1, Chapters 10 and 13]](#1).\n\nThe book discusses the highly persistent nature of the sequence of binary variables $\\epsilon_t$ that describe the direction of market orders.\nThat is, buy orders ($\\epsilon_t = +1$) tend to follow other buy orders, and sell orders ($\\epsilon_t = -1$) tend to follow other sell orders, often for very long periods.\n\nWe assume that the time series of signs $\\epsilon_t$ is well modelled by a **discrete autoregressive process** of order $p > 0$. In this framework, the best predictor of the next market order sign (just before it occurs) is a linear combination of the past signs:\n\n```math\n\\hat{\\epsilon}_t = \\sum_{k=1}^{p} \\mathbb{K}(k) \\epsilon_{t-k}\n```\n\nHere, $\\mathbb{K}(k)$ can be inferred from the sign autocorrelation function using the Yule-Walker equations.\n$p$ determines how many past signs are used ($\\forall \\ell > p, \\mathbb{K}(\\ell) \\approx 0$).\n\nAs a result, the probability that the next sign is $\\epsilon_t$ is:\n\n```math\n\\mathbb{P}_{t-1}(\\epsilon_t) = \\frac{1 + \\epsilon_t \\hat{\\epsilon}_t}{2}\n```\n\n## References\n<a id=\"1\">[1]</a> \nBouchaud J-P, Bonart J, Donier J, Gould M. _Trades, Quotes and Prices: Financial Markets Under the Microscope_. Cambridge University Press; 2018.\n",
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