https://github.com/aaronjoshi93/Market-Forecasts/releases

Market Forecasts Weekly Archive: Automated Charts, Analysis, Signals & Insights

Table of contents

• What this repo is for
• How it fits into the weekly workflow
• How to get started
• How the forecasting pipeline works
• Data sources and reliability
• Chart generation and documentation
• The file layout
• Release access and updates
• Testing and quality
• Customization and advanced topics
• Governance, contribution, and community
• Frequently asked questions
• Acknowledgments and credits

What this repo is for Market Forecasts Weekly Archive collects a weekly forecast and the related charts, notes, and markdown documentation. It automates the creation of charts and stores an analy­sis guide that explains the decision logic behind each forecast. The goal is to provide a transparent, reproducible weekly snapshot of market expectations. The archive is designed for traders, researchers, and anyone who wants a consistent weekly reference point for market direction and key levels.

How this project fits into the weekly workflow

• Data gathering: Each week, a data pull runs from multiple sources to capture price action, volume, volatility, and order-flow signals where available.
• Feature extraction: The system computes indicators, support and resistance levels, trend lines, and high-probability price targets.
• Chart generation: Visualizations are produced automatically to accompany the narrative.
• Documentation: A markdown file documents the analysis, the reasoning, and the conclusions.
• Packaging: The weekly package is prepared for release and stored in the archive, with an accessible chart bundle and notes.

How to get started

• Prerequisites: A modern Python environment is assumed unless you choose an alternative path. You will need Python 3.11 or newer and a clean venv or conda environment.
• Getting the code: Clone the repository to your local machine.
  • git clone https://github.com/aaronjoshi93/Market-Forecasts.git
• Installing dependencies: Create a virtual environment and install the requirements.
  • python3 -m venv venv
  • source venv/bin/activate
  • pip install -r requirements.txt
• Running the pipeline locally: Start the forecast cycle to generate a new week’s data, charts, and documentation.
  • python -m forecast_pipeline.run --week latest
• Verifying outputs: Check the outputs directory for the latest charts, markdown notes, and a summary.
• Access to releases: Visit the Releases page to grab the latest packaged assets. From https://github.com/aaronjoshi93/Market-Forecasts/releases download the latest release assets and execute the installer or unpack the archive according to the platform. This page hosts the weekly packaged materials and the corresponding files you need to run the archive locally or on a server.
• Access to releases (alternate): For the latest assets, check the Releases section at the same URL for updated builds and new documents. The releases page is the primary source for the packaged weekly archive, including charts and Markdown documentation.

Overview of the weekly forecast archive The Market Forecasts Weekly Archive is built around three pillars: chart visuals, predictive levels, and narrative research. Each week produces a self-contained package that includes:

• A set of charts that reflect price action and predicted key levels.
• A markdown file that documents the analysis, the method, and the conclusions.
• A short, structured executive summary that highlights the forecast direction and the main risk factors.
• Supporting data files and metadata to maintain reproducibility.

The weekly package is designed to be easy to share. You can attach the markdown notes to a notebook, include the charts in a blog post, or present the visuals in a dashboard. The documentation is designed to be readable by people who want context and by tools that want a reproducible record.

Why this approach works

• Clarity: The weekly package clarifies the forecast path with charts and notes.
• Reproducibility: The pipeline records the steps and parameters used to generate the forecast.
• Transparency: The analysis section exposes the reasoning for forecasts and key levels.
• Accessibility: The markdown notes make it easy to annotate and repurpose the content.

Key features and capabilities

• Automated chart generation: The pipeline creates multiple chart types to illustrate price action, momentum, and levels.
• Key level predictions: The forecast includes probable support and resistance levels, with rationale.
• Markdown documentation: The notes explain the methodology, data considerations, and interpretation of results.
• Research and analysis: The documentation includes context, references, and evidence to support conclusions.
• Weekly cadence: The archive follows a predictable weekly schedule for compilation and release.
• Extensible architecture: The components are designed to accommodate new indicators and data sources.

How the forecasting pipeline works

• Data ingestion: The system pulls price history and related market data from multiple sources. Data is normalized to ensure consistency across charts and indicators.
• Indicator computation: A set of technical indicators is calculated, including moving averages, oscillators, volatility measures, and correlation checks. The indicators provide signals that feed into the forecast.
• Level detection: The pipeline identifies potential support and resistance levels using methods such as swing highs/lows, volume profiles, and volatility bands.
• Pattern recognition: Simple pattern checks are run to detect common formations that may influence the forecast direction.
• Chart rendering: Charts are rendered with overlays for key levels, trend lines, and indicators. The visuals are designed to be clear and informative at a glance.
• Narrative assembly: The markdown notes combine the quantitative results with qualitative analysis. The narrative describes why the forecast looks a certain way and what might cause a change.
• Packaging: The week’s outputs are packaged into a release, with a consistent folder structure and metadata. The package is added to the Releases page and prepared for distribution.

Data sources and reliability

• Price history: The system uses historical price data from reputable, open sources. It normalizes time frames and aligns data points to ensure comparability across assets.
• Market indicators: The pipeline includes a core set of technical indicators that are commonly used in market analysis.
• Alternative data: When available, the system integrates alternative data signals such as sentiment proxies or on-chain metrics to provide additional context.
• Documentation of methods: The analysis includes a description of the data sources, processing steps, and any known limitations. The documentation is designed to be transparent and reproducible.
• Quality checks: The pipeline includes automatic checks to identify missing data, outliers, and inconsistencies. It logs issues and escalates them for review.

Chart generation and visuals

• Chart types: Trend charts, candles or OHLC plots, moving average overlays, and volatility bands are used to illustrate the forecast.
• Annotations: Key levels are annotated directly on the charts. Arrows, labels, and shading help highlight critical areas.
• Color schemes: The visuals use accessible color palettes that work well in both light and dark modes.
• Export formats: Charts are exported as high-resolution PNGs and optionally as vector graphics for scaling in presentations or reports.
• Consistency: The same layout and style are used across all weekly charts to ensure quick recognition and comparison week to week.

Documentation research and analysis

• Narrative structure: Each markdown document follows a consistent structure: executive summary, data and methods, results, key levels, risks, and references.
• Research notes: The notes reproduce the rationale behind the forecast, including relevant charts and evidence.
• Citations: The documentation includes inline references to data sources and indicators used. This improves credibility and traceability.
• Accessibility: The markdown is written for readability, with clear headings and concise language.

File layout and project structure

• data/: Raw and processed data used for charts and analysis.
• charts/: Generated chart images and assets.
• docs/: Markdown documentation for each weekly forecast.
• scripts/: Utility scripts for data acquisition, processing, and report generation.
• templates/: Markdown and chart templates used to standardize weekly outputs.
• releases/: Generated weekly packages and release notes.
• tests/: Tests and validation scripts to ensure data integrity and chart accuracy.
• config/: Configuration files for pipelines, indicators, and thresholds.
• README.md: This document you are reading.

Release access and updates

• The primary source for the official weekly package is the Releases page in the repository. This page hosts the weekly packaged materials, including charts and markdown notes. For the latest assets, visit the Releases page at https://github.com/aaronjoshi93/Market-Forecasts/releases. From that page, download the packaged release and execute the installer or unpack the archive to use the weekly archive locally or on a server.
• If you cannot access the link above, you can still locate the weekly materials by checking the Releases section in the repository. The Releases section contains the latest weekly builds, including charts, notes, and supporting data. You can browse the assets and download the ones that match your platform and use case.
• After downloading, you will typically run a setup script or extract the package. The exact steps depend on the release format. The documentation in the package explains how to install and use the week’s outputs. The key objective is to provide a ready-to-use set of charts and notes that align with the week’s forecast.

Release notes and versioning

• Each weekly release includes a version label, such as v1.x.y or v2.x.y, to help track changes over time.
• The notes describe what changed in the week, what was updated in the forecasting logic, and any corrections to charts or levels.
• The release notes also include a short summary of the Week Ahead forecast, including the expected direction and notable risk factors.

Using the weekly archive in practice

• Traders can use the weekly archive as a reference point for the week ahead, comparing the forecast to current price action.
• Analysts can study the methodology and the rationale for key levels to improve their own approaches.
• Educators can use the markdown notes as teaching material for how to structure market analyses and how to present complex data clearly.
• Students can learn how charts, data, and narrative analysis come together to produce a weekly forecast.

Customization and advanced topics

• Indicator customization: You can modify the set of indicators used in the forecast to fit different markets or time frames.
• Data source variations: You can add or replace data sources with new APIs or datasets. The pipeline is designed to be adaptable.
• Chart styles: You can adjust colors, fonts, chart types, and overlays to match your brand or preference.
• Output formats: The release can be produced in different formats, including PDF reports or HTML pages, in addition to Markdown notes.
• Automation and scheduling: We provide configuration to run the weekly forecast automatically on a schedule. You can set up a cron job or a workflow in your environment to trigger the pipeline.

What to review in the Markdown notes

• Executive summary: A concise statement of the week’s forecast direction and key drivers.
• Data and methods: A description of the data used and the methods employed to derive the forecast.
• Results: A summary of the indicators and their signals, including any caveats.
• Key levels: The main support and resistance levels with rationale and potential reactions.
• Risk factors: A list of factors that could cause the forecast to shift.
• References: Citations and sources for data, charts, and methods.
• Appendices: Any supplementary charts, tables, or details that support the analysis.
• Reproducibility: A note on how to reproduce the results from raw data to charts.

Governance and contribution

• Project governance: The repository follows a collaborative approach. Decisions are made through discussions and consensus.
• How to contribute: If you want to contribute, start by reading the contribution guidelines in the contributing document. You can propose enhancements to data sources, indicators, chart styles, or documentation structure.
• Code quality: Contributions should pass tests and maintain the project’s style and consistency.
• Community standards: The project welcomes diverse perspectives. Engage with respect and provide constructive feedback.

Testing and quality assurance

• Data validation: Automated checks verify the integrity of data, including consistency across sources and accuracy of calculations.
• Chart validation: Generated charts are checked for readability and correctness of overlays.
• Documentation validation: Markdown notes are checked for formatting and consistency with the data and methods.
• Regression tests: Regression tests ensure that changes do not break existing behavior or reduce the quality of outputs.
• Continuous integration: A CI system runs tests automatically when changes are submitted to the repository.

Roadmap and future work

• Expand data coverage: Include additional markets, instruments, and geographies to broaden the forecast scope.
• Improve narrative depth: Add more in-depth analysis, scenario planning, and sensitivity checks.
• Enhance visuals: Improve the visual language of charts to maximize clarity and accessibility.
• Automate reporting: Create automated, publish-ready reports that can be emailed or embedded in dashboards.
• User-specific outputs: Allow users to customize the forecast view for their preferred assets and time horizons.

How to contribute effectively

• Start with small changes: Propose a minor improvement to the chart layout or documentation to get familiar with the process.
• Write tests: Add tests that cover the new feature or the data source change.
• Document your changes: Update the markdown notes to reflect any changes in method or interpretation.
• Engage with the community: Discuss ideas in issues and pull requests to align with project goals.
• Follow formatting guidelines: Keep the markdown consistent, with clear headings, bullet lists, and references.

Security and safety

• Dependency management: The project uses a controlled set of dependencies. It is important to keep them up to date.
• Data handling: Handle data responsibly and clearly document any limitations or assumptions.
• Access to releases: Keep the releases page secure and monitor for unauthorized modifications. The official releases page is the trusted distribution point for the weekly archive.

Frequently asked questions

• What assets are included in the weekly release? The weekly release includes charts, markdown notes, and supporting data necessary to reproduce the forecast for the week.
• How do I reproduce a forecast locally? Install the dependencies, fetch the data, run the forecast pipeline, generate charts, and generate the markdown notes. The process is documented in the repository.
• Can I modify the forecast method? Yes. The architecture is designed to be extensible. You can adjust the indicators, levels, and narrative structure. Provide a clear description of the changes when you contribute.
• Where can I find the latest release? The Releases page is the central place for the most recent weekly archive and its assets. For the latest assets, check the Releases page here: https://github.com/aaronjoshi93/Market-Forecasts/releases.

Acknowledgments and credits

• The project benefits from open data sources and community contributions that help improve charts and analyses.
• Special thanks to contributors who help maintain the pipeline, improve the documentation, and refine the forecasting logic.

Appendix: Quick reference for the weekly package

• Location: The weekly package resides in the releases directory on the repository and is indexed on the Releases page.
• Contents: A curated set of charts, a markdown forecast report, and any supporting data files needed to reproduce the analysis.
• How to use: Unpack or install the package according to the release instructions. Open the markdown file to read the analysis and view the charts in the charts directory.
• Verification: Compare the charts to the narrative and ensure the key levels align with the data and indicators.

Appendix: Styling and formatting notes

• Headings and structure: The markdown surfaces a clean, consistent structure. Each section uses clear headings, short paragraphs, and bullet lists where helpful.
• Chart captions: Each chart includes a caption that explains what is shown and why it matters.
• Tables: Tables use simple formatting with left-aligned content and concise headers.
• References: All references appear in a dedicated references section within the markdown notes.

Appendix: Example workflow for a weekly forecast

• Monday morning: Pull fresh data and update the pipeline with any new data sources.
• Monday afternoon: Compute indicators and detect new key levels.
• Tuesday morning: Generate charts and draft the narrative.
• Tuesday afternoon: Review the markdown notes for clarity and accuracy.
• Wednesday: Package the weekly archive and create the release.
• Thursday: Publish the release and push the update to the Releases page.
• Friday: Monitor for feedback and prepare for the next cycle.

Appendix: How the archive can be used in different contexts

• Personal trading: Use the weekly forecast as a reference point for personal trading decisions. It can help with planning entry and exit points.
• Education: Students can study the forecasting process. They can learn how data, indicators, and key levels come together to form a forecast.
• Research: Researchers can compare this weekly approach with other forecasting methods. The archive provides a reproducible baseline.
• Dashboards: The charts can be embedded in dashboards for real-time monitoring and weekly summaries.

Appendix: Glossary

• Forecast: A probabilistic expectation of future price movement based on data and indicators.
• Key levels: Price points that historically attract support or resistance.
• Indicators: Mathematical calculations used to evaluate price action and market momentum.
• Reproducibility: The ability to reproduce results from raw data to final outputs using the same steps and parameters.
• Narrative: The written explanation of why the forecast looks the way it does, including context and assumptions.

Appendix: Licensing and usage rights

• The project uses an MIT-compatible license. You can reuse parts of the code and documentation with proper attribution.
• The release packages are provided as-is. Use them according to the terms of their license and the repository guidelines.

Appendix: Contact and community

• For questions or discussions about improvements, open issues in the repository or participate in the community discussions.
• Contributions are welcome across data sources, indicators, chart quality, and documentation clarity.

Releases and access reminder

• The primary path to obtain the weekly archive is the Releases page. For the latest assets, visit https://github.com/aaronjoshi93/Market-Forecasts/releases. From this page, download the latest release assets and execute the installer or unpack the archive to access the weekly forecast package.
• If you cannot access the link above for any reason, you can review the Releases section within the repository to locate the latest weekly build and its assets. The Releases area hosts the full package and notes so you can reproduce or review the forecast.

Closing notes

• The Market Forecasts Weekly Archive is built to be useful, reproducible, and easy to share. It combines data, charts, and clear narration so anyone can understand the weekly forecast and the reasoning behind it. The repository is designed to support growth and experimentation, with a straightforward path to extend indicators, add assets, and improve documentation. The weekly cadence helps maintain a steady stream of insights that can be compared across weeks and markets.

If you need adjustments to any section, or you want more sections added to tailor the README to a particular audience or use case, tell me the target audience and the level of detail you want. I can expand or pare down accordingly.