From 91b2d5a990b14195102f594d3ffc6c525d9b1876 Mon Sep 17 00:00:00 2001 From: Lev Gusiev <89646710+BytecodeBrewer@users.noreply.github.com> Date: Wed, 1 Jul 2026 12:18:22 +0200 Subject: [PATCH] Revert "Implement first storage layer" --- .dockerignore | 25 -- CONTRIBUTING.md | 251 +++++++---- README.md | 60 ++- argus_probe.duckdb | Bin 3944448 -> 0 bytes dockerfile | 12 - docs/forecast_research.md | 50 --- ...-data-sources.md => research-data-sources} | 0 docs/research-databases-and-storage.md | 388 ------------------ docs/roadmap.md | 143 +++---- docs/storage.md | 154 ------- pyproject.toml | 1 - src/argus/clients/exchangerate_client.py | 20 +- src/argus/domain/internal_models.py | 34 -- src/argus/storage/__init__.py | 0 src/argus/storage/database.py | 271 ------------ tests/test_exchangerate_client.py | 37 +- tests/test_internal_models.py | 101 ----- tests/test_storage_database.py | 222 ---------- tests/test_timeseries_service.py | 5 +- tests/test_validation_domain.py | 8 +- tests/test_yfinance_client.py | 2 +- 21 files changed, 286 insertions(+), 1498 deletions(-) delete mode 100644 .dockerignore delete mode 100644 argus_probe.duckdb delete mode 100644 dockerfile delete mode 100644 docs/forecast_research.md rename docs/{research-data-sources.md => research-data-sources} (100%) delete mode 100644 docs/research-databases-and-storage.md delete mode 100644 docs/storage.md delete mode 100644 src/argus/domain/internal_models.py delete mode 100644 src/argus/storage/__init__.py delete mode 100644 src/argus/storage/database.py delete mode 100644 tests/test_internal_models.py delete mode 100644 tests/test_storage_database.py diff --git a/.dockerignore b/.dockerignore deleted file mode 100644 index 8c24b79..0000000 --- a/.dockerignore +++ /dev/null @@ -1,25 +0,0 @@ -# Git -.git -.github -.githooks -.gitignore - -# Local environment and secrets -.venv -.env - -# Python cache and test cache -__pycache__ -.pytest_cache -.ruff_cache -.mypy_cache -.coverage -htmlcov - -# Build artifacts -dist -build -*.egg-info - -# Project docs not needed for the test image -docs \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 3f87759..0b53865 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -4,9 +4,33 @@ Thank you for your interest in contributing to ARGUS. ARGUS is a Python-based market analytics project focused on clean data workflows, reliable code, useful metrics and future AI-assisted monitoring. -The project is still growing, so contributions should be small, focused and easy to review. You do not need to be an expert to contribute, but your changes should be understandable, reliable and related to the current project direction. +This project is still growing, so contributions should help the project become more stable, understandable and useful step by step. -Good starting points are issues labeled `good first issue`. These issues are usually smaller, easier to review and better suited for getting familiar with the project. +> [!IMPORTANT] +> ARGUS values reliability, clear communication and long-term skill building. +> Contributions should improve the project without creating unnecessary complexity. + +--- + +## Project Mindset + +ARGUS is not only about adding features quickly. + +The project is built around: + +- clean Python code +- understandable architecture +- reliable tests +- useful documentation +- careful data handling +- practical analytics +- continuous learning + +Good contributions should make the project easier to use, test, maintain or extend. + +--- + +## What You Can Contribute Helpful contributions include: @@ -14,14 +38,17 @@ Helpful contributions include: - tests - documentation improvements - small refactorings +- validation improvements - analytics metrics +- chart improvements - data-source clients -- UI or chart improvements -- CI/CD and tooling improvements -- architecture or research notes +- CI/CD improvements +- issue clarification +- architecture notes +- examples and usage instructions -> [!IMPORTANT] -> Please keep changes focused and avoid adding unnecessary complexity. +> [!NOTE] +> Large features should usually start with an issue or short discussion before implementation. --- @@ -49,45 +76,46 @@ Bad examples: --- -## Contribution Expectations +## Development Setup -Contributors are expected to keep changes focused, understandable and related to the issue or task. +Clone the repository: -Please: +```bash +git clone https://github.com/BytecodeBrewer/argus.git +cd argus +``` -- explain your changes clearly -- be open to review feedback -- improve your contribution step by step after feedback -- avoid unrelated rewrites -- respect the existing architecture unless there is a clear reason to change it -- do not add scripts that automatically run `git add`, `git commit`, `git push` or create pull requests unless this was discussed first +Create a virtual environment: -A contribution may be declined or delayed if it: +```bash +python -m venv .venv +``` -- does not fit the current roadmap -- adds too much complexity too early -- breaks existing functionality -- lacks necessary checks or documentation -- duplicates existing work -- bypasses the repository workflow +Activate it. ---- +On Windows PowerShell: -## Branch Workflow +```powershell +.venv\Scripts\Activate.ps1 +``` -For issue-based work, create your branch from the related GitHub issue when possible. +On macOS/Linux: -GitHub may suggest a branch name based on the issue title. You can shorten it if the generated name is too long. +```bash +source .venv/bin/activate +``` -Good branch names are focused and describe the task: +Install the project with development dependencies: -```text -43-research-forecasting-approach -33-add-yfinance-client -40-improve-test-coverage +```bash +pip install -e ".[dev]" ``` -If you create the branch manually, use: +--- + +## Branch Workflow + +Create a new branch for your work: ```bash git checkout -b @@ -96,30 +124,24 @@ git checkout -b Example: ```bash -git checkout -b 43-research-forecasting-approach +git checkout -b 12-add-volatility-metric ``` +Use focused branch names that describe the work. + --- ## Commit Expectations Commits should be small, understandable and related to the current task. -ARGUS uses a conventional commit style with an issue reference: - -```text -type(#issue): short description -``` - Good commit messages: ```text -docs(#43): research first forecasting approach -feat(#33): add yfinance historical data client -test(#40): add tests for conversion service -fix(#33): handle empty historical data response -refactor(#34): split metric helpers -ci(#10): update commit message workflow +Add rolling volatility metric +Fix currency validation edge case +Update README setup instructions +Add tests for trend metrics ``` Avoid unclear messages: @@ -133,25 +155,21 @@ final ``` > [!TIP] -> A good commit tells future readers what changed and which issue it belongs to. +> A good commit tells future readers what changed and why it belongs to the task. --- -## Checks +## Testing -Before opening a pull request, run the project checks: +Before opening a pull request, run the test suite: ```bash pytest -ruff check . -ruff format --check . ``` -These checks verify that tests pass, code style is valid and formatting is consistent. - -A pull request should not be marked as ready for review if checks are failing without explanation. +A pull request should not be opened as ready for review if tests are failing without explanation. -If a check fails and you are unsure why, mention it clearly in the pull request. +If a test fails and you do not know why, mention it clearly in the pull request. > [!IMPORTANT] > CI checks must pass before a pull request can be merged. @@ -160,23 +178,65 @@ If a check fails and you are unsure why, mention it clearly in the pull request. ## Pull Request Expectations -Pull requests should target `develop` unless the maintainer explicitly says otherwise. +A good pull request should include: -Do not open feature, research or documentation pull requests directly against `main`. -The `main` branch is reserved for stable/release-ready changes. +- a clear title +- a short explanation of what changed +- a link to the related issue if available +- notes about tests +- screenshots for UI changes if useful +- a short explanation of any trade-offs -Please use the pull request template and fill it out clearly. +Pull requests should be focused and reviewable. -The template helps reviewers understand: +Before opening a pull request, run: -- what changed -- which issue is related -- whether tests were run -- whether documentation or screenshots are needed -- if there are any notes or trade-offs +```bash +pytest +ruff check . +ruff format --check . +``` + +--- + +## Reliability Expectations + +Contributors are expected to work reliably. -Do not bypass the pull request template or replace it with an unrelated auto-generated description. -It makes reviewing harder and may delay the merge. +This means: + +- do not submit random or unfinished code without context +- do not ignore failing tests +- do not introduce secrets, API keys or local machine paths +- do not rewrite unrelated parts of the project without discussion +- communicate if you are unsure +- keep changes understandable for future contributors +- respect the existing architecture unless there is a clear reason to change it + +Reliability does not mean knowing everything already. + +It means being honest, careful and consistent. + +--- + +## Learning Mindset + +ARGUS welcomes contributors who want to improve their technical skills. + +You do not need to be an expert to contribute. + +Helpful behavior includes: + +- asking clear questions +- explaining your reasoning +- being open to review feedback +- improving your code after feedback +- learning from tests, errors and architecture discussions +- documenting what you learned when it helps others + +> [!NOTE] +> This project values skill growth. +> A thoughtful small contribution is better than a large unclear one. --- @@ -205,14 +265,25 @@ For analytics code: ## Secrets and API Keys -Never commit secrets, API keys, tokens, passwords, `.env` files or local config files with private data. +Never commit secrets. + +Do not commit: -Use a local `.env` file for secrets: +- API keys +- tokens +- passwords +- `.env` files +- local config files with private data + +Use a local `.env` file for secrets. ```env -EXCHANGE_RATE_API_KEY=your_api_key_here +api_key=your_api_key_here ``` +> [!WARNING] +> If you accidentally commit a secret, revoke it immediately and inform the maintainer. + --- ## Documentation @@ -228,4 +299,44 @@ Useful documentation includes: - data-source assumptions - troubleshooting notes +Repository-level files such as `README.md`, `CONTRIBUTING.md`, `CODE_OF_CONDUCT.md` and `LICENSE` belong in the repository root. + Technical notes, research and deeper explanations belong in `docs/`. + +--- + +## Communication + +Please communicate respectfully and constructively. + +When giving feedback: + +- focus on the code or idea, not the person +- explain the reason behind suggestions +- be specific +- stay open to alternatives + +When receiving feedback: + +- assume good intent +- ask questions if something is unclear +- improve the contribution step by step + +All contributors are expected to follow the project’s Code of Conduct. + +--- + +## Maintainer Notes + +The maintainer may ask for changes before merging a pull request. + +A contribution may be declined if it: + +- does not fit the current roadmap +- adds too much complexity too early +- breaks existing functionality +- lacks necessary tests +- duplicates existing work +- does not follow the project’s quality expectations + +This helps keep ARGUS stable, learnable and maintainable. \ No newline at end of file diff --git a/README.md b/README.md index 8723389..607f0d0 100644 --- a/README.md +++ b/README.md @@ -120,18 +120,11 @@ README.md - Tkinter - pytest -### Current data sources +### Current data source - ExchangeRate API for live currency conversion - yfinance for historical market-data retrieval and analytics -### Storage - -- DuckDB — local analytical storage for normalized historical market data - ->[!Note] -> See docs/storage.md for details. - --- ## Planned / Future Tech Stack @@ -145,32 +138,46 @@ Planned or likely future technologies include: - Frankfurter API for historical FX data - possible additional market-data APIs later +### Data processing + +- pandas +- NumPy +- possibly Polars later for larger datasets + ### Storage - PostgreSQL +- DuckDB +- Parquet +- optional cloud storage ### Visualization and UI +- matplotlib +- Plotly - NiceGUI -- Django ### DevOps and deployment +- GitHub Actions +- Docker - Docker Compose -- Travis CI +- cloud deployment later ### Cloud and data engineering -- Azure +- Azure, GCP or AWS depending on project direction - scheduled ingestion -- agentic Workflows -- Blob Storage -- scaled analysis +- data quality checks +- reporting pipelines ### AI and agentic workflows - LLM-assisted summaries - RAG over stored reports or notes +- agentic data checks +- anomaly monitoring +- human-in-the-loop signal review > [!CAUTION] > AI and agentic features are future-stage ideas. @@ -192,7 +199,6 @@ Recommended for development: - VS Code - a virtual environment - pytest -- Docker, if you want to run tests in an isolated container environment > [!NOTE] > Runtime dependencies are managed through `pyproject.toml`. @@ -248,7 +254,7 @@ pip install -e ".[dev]" ## API Key Setup -ARGUS uses the ExchangeRate API for live currency conversion. Historical analytics currently use yfinance and do not require an additional API key. +ARGUS currently uses the ExchangeRate API for live currency conversion. ### 1. Create an API key @@ -278,7 +284,7 @@ The `.env` file must stay local and should never be committed. --- -## Running ARGUS Locally +## Running ARGUS Start the current Tkinter GUI: @@ -288,22 +294,6 @@ python -m argus.main This starts the local ARGUS prototype with calculator, currency conversion and basic analytics views. -## Running Argus in Docker - -ARGUS includes a minimal Docker setup for running the test suite in an isolated container environment. - -Build the Docker image: - -```bash -docker build -t argus . -``` - -Run ARGUS in a container: - -```bash -docker run --rm argus -``` - ### Legacy CLI / Debug Interface The legacy CLI is still available for quick local checks and debugging: @@ -320,7 +310,7 @@ python src/legacy/debug_main.py ## Running Tests -Run the test suite locally: +Run the test suite: ```bash pytest @@ -356,4 +346,4 @@ Current focus: - add stronger market metrics - expand pandas-based analytics workflows - improve dashboard usefulness without adding unnecessary 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zK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF z5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk z1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs z0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZ zfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&U zAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C7 z2oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N z0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+ z!2h>Xhc*DhP!K?W6OuY4Q=wRZQQF-)Ot*j [!NOTE] -> The fields below describe the future database-oriented structure. -> Technical fields such as `id`, `instrument_id`, `source_id`, `created_at` and `updated_at` are expected to appear in the database layer. -> Internal Python models may reference related objects directly, for example `source` and `instrument`, before database IDs exist. - -### data_sources - -Stores where data came from. - -Recommended first database fields: - -```text -id -name -provider_kind -requires_api_key -created_at -updated_at -``` - -Example internal/source records: - -| name | provider_kind | requires_api_key | -| ---------------- | ------------- | ---------------: | -| ExchangeRate API | fx_rates | true | -| yfinance | market_prices | false | -| FRED | macro_data | true | - -### instruments - -Stores what ARGUS can analyze. - -Examples: - -* EUR/USD -* AAPL -* SPY -* S&P 500 -* BTC-USD - -Recommended first database fields: - -```text -id -symbol -name -asset_class -currency -exchange -base_currency -quote_currency -created_at -updated_at -``` - -Example instrument records: - -| symbol | name | asset_class | currency | exchange | base_currency | quote_currency | -| ------- | ---------------- | ----------- | -------- | --------- | ------------- | -------------- | -| EUR/USD | Euro / US Dollar | fx | null | null | EUR | USD | -| AAPL | Apple Inc. | stock | USD | NASDAQ | null | null | -| SPY | SPDR S&P 500 ETF | etf | USD | NYSE Arca | null | null | - -### price_bars - -Stores historical market data in an OHLCV-ready structure. - -Recommended first database fields: - -```text -id -instrument_id -source_id -timestamp -timeframe -open -high -low -close -adjusted_close -volume -created_at -updated_at -``` - -FX-style exchange-rate data can be represented as a price bar by storing the rate in `close`. - -The other OHLCV fields can stay empty until ARGUS uses data sources that provide them. - -Example price bar records shown with joined source and instrument information for readability: - -| source | instrument | timestamp | timeframe | open | high | low | close | adjusted_close | volume | -| -------- | ---------- | ---------- | --------- | -----: | -----: | -----: | -----: | -------------: | -------: | -| yfinance | EUR/USD | 2024-01-02 | 1d | null | null | null | 1.095 | null | null | -| yfinance | AAPL | 2024-01-02 | 1d | 187.15 | 188.44 | 183.89 | 185.64 | 184.25 | 50200000 | - ---- - -## Recommended First Implementation Step - -The first storage implementation should not be tied to one specific data provider. - -ARGUS currently works with an existing ExchangeRate API client and evaluates broader market data through yfinance. -Frankfurter may be added later as a stronger FX-oriented historical data source. - -The storage layer should therefore focus on a normalized internal market-data format instead of depending on one API response structure. - -Recommended first step: - -```text -active data client -→ normalize into instruments and price_bars -→ store in DuckDB -→ query with SQL -→ use results for analytics and charts -``` - ---- - -## Future Direction - -Later sprints can expand the storage layer step by step. - -Possible later additions: - -| Future Area | Possible Additions | -| --- | --- | -| Better source mapping | source-specific symbols, provider metadata | -| Watchlists | user-selected instruments | -| Reports | generated report metadata and history | -| Macro data | FRED indicators and observations | -| Paper trading | simulated orders, positions and portfolio history | -| Server architecture | PostgreSQL | -| SQL tooling | SQLGate with PostgreSQL | -| Cloud direction | managed PostgreSQL or cloud storage | - -SQLGate should be kept for a later PostgreSQL phase. - -It becomes useful when ARGUS moves toward: - -* server-based storage -* stronger database management -* richer metadata -* more stable application state -* user-facing features -* report history -* cloud-ready architecture - -Additional metadata such as documentation links, terms links or provider governance fields can also become useful later. - -For the first DuckDB phase, these details should stay in research documentation instead of the database schema. - ---- - -## Final Recommendation - -ARGUS should start with DuckDB as the first local analytics storage layer. - -DuckDB fits the current phase best because ARGUS needs local analytical SQL workflows, not a full server database yet. - -The first implementation should store historical market data in an OHLCV-ready structure. - -The recommended first data model is: - -```text -data_sources -instruments -price_bars -``` - -Notebook exploration should be the main developer workflow before SQL logic is moved into application code. - -The DuckDB CLI can be used for quick inspection. - -PostgreSQL and SQLGate should be introduced later when ARGUS moves toward a more product-like or cloud-based architecture. diff --git a/docs/roadmap.md b/docs/roadmap.md index c637327..85706af 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -27,121 +27,92 @@ Scope: Outcome: Sprint 1 established the local ARGUS foundation with package structure, GUI prototype, analytics prototype, tests, documentation, CI, Dependabot and governance files. -### Sprint 2 — Reporting & Market Analytics Foundation +### Sprint 2 — Market Analytics & Data Source Expansion **Status:** In progress -Move ARGUS from a simple FX-focused prototype toward a first usable market analytics and reporting tool. +Move from simple FX conversion toward broader market analytics. -**Scope:** - -- Add stronger market analytics metrics: +Scope: +- Add stronger market metrics: - cumulative return - strongest / weakest day - rolling volatility - - basic performance analytics - - basic risk analytics -- Add or improve real market data support: - + - performance analytics + - risk analytics +- Extend the current dashboard without adding unnecessary chart noise +- Add or evaluate new data clients: + - Frankfurter for historical FX data - yfinance for broader market data - - existing FX conversion remains available where useful +- Replace or reduce dependency on the current ExchangeRate API where needed - Improve pandas-based analysis workflows -- Introduce local storage for historical market data -- Add report generation and export -- Add a first simple prediction feature -- Introduce NiceGUI as the next GUI direction -- Extend the current dashboard with real market analytics -- Add tests for metric calculations, data transformations and storage behavior -- Improve CI/CD with first deployment or release automation steps - -**Outcome:** - -ARGUS becomes a basic market analytics and reporting tool. -Users can fetch market data, store it locally, calculate metrics, generate a first report and view results through a first modern dashboard. +- Add tests for metric calculations and data transformations +- Document metric definitions, assumptions and chart behavior ---- +Outcome: +ARGUS becomes a basic market analytics tool, not only a converter. -### Sprint 3 — Advanced Local Analytics & Product Quality +### Sprint 3 — Storage, Web-Ready UI & Data Architecture **Status:** Planned -Expand the local ARGUS application into a stronger analytics product with better data handling, UI structure, predictions and quality checks. - -**Scope:** - -- Extend the local storage layer -- Add a first local ETL workflow -- Improve the NiceGUI dashboard structure and usability -- Explore how NiceGUI can later interact with a more modern frontend stack such as Django, React or Node.js-based services -- Keep Tkinter as legacy/prototype unless it is no longer useful -- Add more metrics, instruments and prediction features -- Improve report templates and report structure -- Introduce first LLM-based summaries for generated reports -- Add first performance tests -- Introduce Snyk or another dependency/security scanning workflow -- Improve code quality, test coverage and maintainability - -**Outcome:** +Prepare ARGUS for persistent data workflows and a stronger product interface. -ARGUS becomes a more scalable local analytics application. -It can process more instruments, produce better reports, provide first automated summaries and offer more reliable insight into market data. - ---- +Scope: -### Sprint 4 — Extended Analysis & Cloud-Ready Foundation +- Add local storage layer: + - PostgreSQL, DuckDB, SQLite or Parquet depending on use case +- Store historical market data +- Separate ingestion, transformation, analytics and presentation layers more clearly +- Start NiceGUI as the main web-ready UI direction +- Keep Tkinter as legacy/prototype unless still useful +- Keep CLI as internal/debug interface only +- Add clearer architecture documentation +- Prepare the project for larger data workflows and external contributors -**Status:** Planned - -Prepare ARGUS for deeper analysis, cloud interaction and future portfolio-assistant workflows while keeping the local product usable and transparent. +Outcome: +ARGUS has a clearer data architecture and starts moving from local prototype toward a scalable analytics application. -**Scope:** +### Sprint 4 — Cloud, Pipelines & Portfolio-Grade Data Engineering -- Add Docker Compose for a more complete local development setup -- Introduce a first Azure connection, focused on simple storage or artifact exchange -- Improve the LLM workflow -- Introduce a first RAG-ready structure for reports, notes, documentation and stored analysis artifacts -- Add data quality checks -- Improve caching and efficient storage of market data -- Add more export options for users -- Add more metrics and better metadata visualization -- Improve transparency around data sources, generated reports and analysis assumptions -- Prepare clear interfaces for future cloud and assistant workflows +**Status:** Future -**Outcome:** +Turn ARGUS into a stronger end-to-end data engineering project. -ARGUS becomes ready to interact with a future cloud layer. -The application can produce clearer, more transparent market analysis and prepares the foundation for retrieval-based workflows, stronger automation and future ARGUS Core integration. +Scope: ---- +- Docker / Docker Compose +- Scheduled data ingestion +- Cloud storage or cloud database +- CI/CD improvements +- Data quality checks +- Basic pipeline orchestration +- Reporting layer +- Architecture diagram +- Deployment documentation -### Sprint 5 — Cloud Interaction & Agentic Monitoring Foundation +Target workflow: -**Status:** Planned +```text +API → Ingestion → Storage → Transformation → Analysis → Visualization → CI/CD +``` -Start the first cloud-connected ARGUS workflows and introduce the foundation for monitoring, agentic checks and strategy-support features. +### Sprint 5 — AI-Assisted Research & Agentic Monitoring -**Scope:** +**Status:** Future vision -- Add first cloud workflows that extend local analysis -- Connect local ARGUS workflows with the first cloud-side services -- Extend RAG over stored market notes, reports, documentation and analysis artifacts -- Add agentic checks for: +Add AI support only after the data, storage, service and reporting layers are stable. - - data quality - - anomalies - - recurring market scans - - report consistency -- Add first human-in-the-loop review workflows for signals or strategy ideas -- Add automated monitoring workflows -- Prepare the first foundations for: +Scope: - - paper trading - - backtesting - - controlled strategy evaluation - - future portfolio-assistant workflows +- LLM-assisted report summaries +- Explanation of unusual movements +- RAG over stored market notes, reports or documentation +- Agentic checks for data quality, anomalies and recurring market scans +- Human-in-the-loop signal review +- Automated monitoring workflows -**Outcome:** +Outcome: -ARGUS and the first cloud-side services begin to interact. -ARGUS becomes useful not only as an analytics and reporting tool, but also as the first foundation for monitoring, strategy evaluation and controlled market-research workflows. +ARGUS starts behaving like its name: a system that continuously watches market data, evaluates it and helps generate useful signals. diff --git a/docs/storage.md b/docs/storage.md deleted file mode 100644 index 27ce106..0000000 --- a/docs/storage.md +++ /dev/null @@ -1,154 +0,0 @@ -# ARGUS Storage Layer - -ARGUS uses DuckDB as the local storage layer for normalized market data. - -The storage layer stores ARGUS-internal market data structures and provides reusable historical data for analytics, charts, dashboards and reports. - -The storage design follows the direction described in [`docs/research-databases-and-storage.md`](research-databases-and-storage.md). - -## Storage Workflow - -ARGUS uses a storage-first workflow for historical market data. - -```text -User / GUI / Analytics request - ↓ -Market data service - ↓ -Check DuckDB storage - ↓ -If data exists: - read stored data - return it for analytics, charts or reports - -If data is missing: - fetch data from a client/API - normalize the response into ARGUS-internal data - return the normalized data - save the normalized data in DuckDB -``` - -DuckDB is used to avoid unnecessary repeated API calls and to make historical market data reusable across analytics, dashboard and reporting workflows. - -Fresh API data can be used immediately after normalization and is also persisted so future requests can use the local storage layer first. - -## Schema Overview - -The first storage schema is based on three related entities: - -```text -data_sources -instruments -price_bars -``` - -### `data_sources` - -Stores where market data came from. - -Examples: - -```text -yfinance -ExchangeRate API -Frankfurter -FRED -``` - -Each source describes a provider or API that can deliver market, FX or macro data. - -### `instruments` - -Stores what ARGUS can analyze. - -Examples: - -```text -EUR/USD -AAPL -SPY -BTC-USD -``` - -An instrument represents the internal ARGUS identity of an asset, currency pair, ETF, index or other market object. - -Provider-specific symbols should be normalized before storage. For example: - -```text -yfinance provider symbol: EURUSD=X -ARGUS instrument symbol: EUR/USD -``` - -### `price_bars` - -Stores historical time-series values in an OHLCV-ready structure. - -A price bar belongs to: - -```text -one data source -one instrument -one timestamp -one timeframe -``` - -FX rates are stored as `close` values. - -For simple FX data, the remaining OHLCV fields can stay empty. For broader market data, the same structure can store open, high, low, close, adjusted close and volume values. - -The combination of source, instrument, timestamp and timeframe identifies a unique stored price bar. - -## Internal Models and Storage - -ARGUS uses internal domain models before data is stored: - -```text -DataSource -Instrument -PriceBar -``` - -These models describe the meaning of the data inside ARGUS. - -The storage layer translates these internal models into DuckDB tables: - -```text -DataSource -> data_sources -Instrument -> instruments -PriceBar -> price_bars -``` - -In Python, a `PriceBar` references a `DataSource` and an `Instrument`. - -In DuckDB, this relationship is stored through IDs: - -```text -price_bars.source_id -> data_sources.id -price_bars.instrument_id -> instruments.id -``` - -This keeps the database normalized while still allowing ARGUS to work with meaningful internal models in Python. - -## Reading Stored Data - -Stored price bars can be read by: - -```text -source -instrument -start date -end date -``` - -The storage layer joins `price_bars`, `data_sources` and `instruments` so that stored IDs become readable market data again. - -Read operations return tabular data that can be used by: - -```text -analytics -charts -dashboards -reports -``` - -This allows ARGUS to process stored historical data without depending on raw API response structures. diff --git a/pyproject.toml b/pyproject.toml index a8ac7b1..e2dc784 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,6 @@ dependencies = [ "numpy", "matplotlib", "yfinance", - "duckdb", ] [project.optional-dependencies] diff --git a/src/argus/clients/exchangerate_client.py b/src/argus/clients/exchangerate_client.py index d899718..8ea5e89 100644 --- a/src/argus/clients/exchangerate_client.py +++ b/src/argus/clients/exchangerate_client.py @@ -24,16 +24,6 @@ def get_rates(curr1: str, curr2: str): resp.raise_for_status() payload = resp.json() - if payload["result"] == "success": - data["result"] = "success" - data["conversion_rate"] = payload["conversion_rate"] - return data - else: - data["result"] = "error" - data["error_type"] = payload.get("error_type") - check_error(data["error_type"]) - return None - except req.exceptions.Timeout: print("API hat zu lange gebraucht.") return None @@ -51,6 +41,16 @@ def get_rates(curr1: str, curr2: str): print("Unerwartete API-Antwortstruktur.") return None + if payload.get("result") == "success": + data["result"] = "success" + data["conversion_rate"] = payload.get("conversion_rate") + return data + else: + data["result"] = "error" + data["error_type"] = payload.get("error_type") + check_error(data["error_type"]) + return None + def check_error(err_type: str) -> None: """ diff --git a/src/argus/domain/internal_models.py b/src/argus/domain/internal_models.py deleted file mode 100644 index 3b7630e..0000000 --- a/src/argus/domain/internal_models.py +++ /dev/null @@ -1,34 +0,0 @@ -from dataclasses import dataclass -from datetime import date - - -@dataclass -class DataSource: - name: str - provider_kind: str - requires_api_key: bool = False - - -@dataclass -class Instrument: - symbol: str - name: str - asset_class: str - currency: str | None = None - exchange: str | None = None - base_currency: str | None = None - quote_currency: str | None = None - - -@dataclass -class PriceBar: - source: DataSource - instrument: Instrument - timestamp: date - timeframe: str - close: float - open: float | None = None - high: float | None = None - low: float | None = None - adjusted_close: float | None = None - volume: float | None = None diff --git a/src/argus/storage/__init__.py b/src/argus/storage/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/argus/storage/database.py b/src/argus/storage/database.py deleted file mode 100644 index ea7128c..0000000 --- a/src/argus/storage/database.py +++ /dev/null @@ -1,271 +0,0 @@ -import duckdb -from datetime import date -import pandas as pd -from argus.domain.internal_models import DataSource, PriceBar, Instrument - - -def initialize_database(database_path: str) -> None: - """ - Initialize the DuckDB database schema. - - Creates the required sequences and tables for data sources, - instruments, and price bars. - - Args: - database_path (str): Path to the DuckDB database file. - - Returns: - None - """ - queries = [ - "CREATE SEQUENCE IF NOT EXISTS data_sources_id_seq;", - "CREATE SEQUENCE IF NOT EXISTS instruments_id_seq;", - "CREATE SEQUENCE IF NOT EXISTS price_bars_id_seq;", - """ - CREATE TABLE IF NOT EXISTS data_sources ( - id INTEGER PRIMARY KEY DEFAULT nextval('data_sources_id_seq'), - name TEXT NOT NULL UNIQUE, - provider_kind TEXT NOT NULL, - requires_api_key BOOLEAN NOT NULL - ); - """, - """ - CREATE TABLE IF NOT EXISTS instruments ( - id INTEGER PRIMARY KEY DEFAULT nextval('instruments_id_seq'), - symbol TEXT NOT NULL UNIQUE, - name TEXT NOT NULL, - asset_class TEXT NOT NULL, - currency TEXT, - exchange TEXT, - base_currency TEXT, - quote_currency TEXT - ); - """, - """ - CREATE TABLE IF NOT EXISTS price_bars ( - id INTEGER PRIMARY KEY DEFAULT nextval('price_bars_id_seq'), - source_id INTEGER NOT NULL, - instrument_id INTEGER NOT NULL, - timestamp DATE NOT NULL, - timeframe TEXT NOT NULL, - close DOUBLE NOT NULL, - open DOUBLE, - high DOUBLE, - low DOUBLE, - adjusted_close DOUBLE, - volume DOUBLE, - FOREIGN KEY (source_id) REFERENCES data_sources (id), - FOREIGN KEY (instrument_id) REFERENCES instruments (id), - UNIQUE (source_id, instrument_id, timestamp, timeframe) - ); - """, - ] - - connection = duckdb.connect(database_path) - try: - for query in queries: - connection.execute(query) - finally: - connection.close() - - -def get_or_create_source(connection, source: DataSource) -> int: - """ - Get an existing data source ID or create a new data source. - - Searches for a data source by name. If it already exists, its ID is - returned. Otherwise, the data source is inserted and the new ID is - returned. - - Args: - connection: Active DuckDB connection. - source (DataSource): Data source model containing provider metadata. - - Returns: - int: Database ID of the existing or newly created data source. - - Raises: - ValueError: If the data source could not be inserted or found. - """ - insert_query = """ - INSERT INTO data_sources (name, provider_kind, requires_api_key) - VALUES (?,?,?) - ON CONFLICT DO NOTHING; - """ - search_query = """ - SELECT id FROM data_sources - WHERE name=? - """ - - result = connection.execute( - query=search_query, - parameters=[source.name], - ).fetchone() - if result is not None: - return result[0] - - connection.execute( - query=insert_query, - parameters=[source.name, source.provider_kind, source.requires_api_key], - ) - - result = connection.execute( - query=search_query, - parameters=[source.name], - ).fetchone() - - if result is None: - raise ValueError("Data source could not be inserted.") - - return result[0] - - -def get_or_create_instrument(connection, instrument: Instrument) -> int: - """ - Get an existing instrument ID or create a new instrument. - - Searches for an instrument by symbol. If it already exists, its ID is - returned. Otherwise, the instrument is inserted and the new ID is - returned. - - Args: - connection: Active DuckDB connection. - instrument (Instrument): Instrument model containing symbol and - asset metadata. - - Returns: - int: Database ID of the existing or newly created instrument. - - Raises: - ValueError: If the instrument could not be inserted or found. - """ - insert_query = """ - INSERT INTO instruments ( - symbol, - name, - asset_class, - currency, - exchange, - base_currency, - quote_currency) - VALUES (?,?,?,?,?,?,?) - ON CONFLICT DO NOTHING; - """ - search_query = """ - SELECT id FROM instruments - WHERE symbol=? - """ - - result = connection.execute( - query=search_query, - parameters=[instrument.symbol], - ).fetchone() - if result is not None: - return result[0] - - connection.execute( - query=insert_query, - parameters=[ - instrument.symbol, - instrument.name, - instrument.asset_class, - instrument.currency, - instrument.exchange, - instrument.base_currency, - instrument.quote_currency, - ], - ) - result = connection.execute( - query=search_query, - parameters=[instrument.symbol], - ).fetchone() - - if result is None: - raise ValueError("Instrument could not be inserted.") - - return result[0] - - -def insert_price_bar(db: str, price_bar: PriceBar) -> None: - insert_query = """ - INSERT INTO price_bars ( - source_id, - instrument_id, - timestamp, - timeframe, - close, - open, - high, - low, - adjusted_close, - volume - ) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - ON CONFLICT DO NOTHING; - """ - connection = duckdb.connect(db) - try: - source_id = get_or_create_source(connection, price_bar.source) - instrument_id = get_or_create_instrument(connection, price_bar.instrument) - connection.execute( - query=insert_query, - parameters=[ - source_id, - instrument_id, - price_bar.timestamp, - price_bar.timeframe, - price_bar.close, - price_bar.open, - price_bar.high, - price_bar.low, - price_bar.adjusted_close, - price_bar.volume, - ], - ) - finally: - connection.close() - - -def read_price_bars( - db: str, - source: DataSource, - instrument: Instrument, - start_date: date, - end_date: date, -) -> pd.DataFrame: - - search_query = """ - SELECT - data_sources.name AS source_name, - instruments.symbol AS instrument_symbol, - price_bars.timestamp, - price_bars.timeframe, - price_bars.open, - price_bars.high, - price_bars.low, - price_bars.close, - price_bars.adjusted_close, - price_bars.volume - FROM price_bars - JOIN data_sources ON price_bars.source_id = data_sources.id - JOIN instruments ON price_bars.instrument_id = instruments.id - WHERE data_sources.name = ? - AND instruments.symbol = ? - AND price_bars.timestamp BETWEEN ? AND ? - ORDER BY price_bars.timestamp; - """ - - connection = duckdb.connect(db) - try: - result = connection.execute( - query=search_query, - parameters=[ - source.name, - instrument.symbol, - start_date, - end_date, - ], - ).df() - finally: - connection.close() - return result diff --git a/tests/test_exchangerate_client.py b/tests/test_exchangerate_client.py index 132a8a8..faf0864 100644 --- a/tests/test_exchangerate_client.py +++ b/tests/test_exchangerate_client.py @@ -3,7 +3,7 @@ from argus.clients.exchangerate_client import get_rates, check_error -def test_check_currency_timeout(monkeypatch, capsys): +def test_check_currency_timeout(monkeypatch): def test_get_resp(url, timeout): raise req.exceptions.Timeout() @@ -12,11 +12,8 @@ def test_get_resp(url, timeout): data = get_rates("EUR", "USD") assert data is None - captured = capsys.readouterr() - assert "API hat zu lange gebraucht." in captured.out - -def test_check_currency_connection_error(monkeypatch, capsys): +def test_check_currency_connection_error(monkeypatch): def test_get_resp(url, timeout): raise req.exceptions.ConnectionError() @@ -25,11 +22,8 @@ def test_get_resp(url, timeout): data = get_rates("EUR", "USD") assert data is None - captured = capsys.readouterr() - assert "Keine Verbindung zur API." in captured.out - -def test_check_currency_request_exception(monkeypatch, capsys): +def test_check_currency_request_exception(monkeypatch): def test_get_resp(url, timeout): raise req.exceptions.RequestException("Testfehler") @@ -38,11 +32,8 @@ def test_get_resp(url, timeout): data = get_rates("EUR", "USD") assert data is None - captured = capsys.readouterr() - assert "Request fehlgeschlagen:" in captured.out - -def test_check_currency_value_error(monkeypatch, capsys): +def test_check_currency_value_error(monkeypatch): test_resp = Mock() test_resp.raise_for_status.return_value = None test_resp.json.side_effect = ValueError("Ungültige JSON-Antwort") @@ -55,16 +46,14 @@ def test_get_resp(url, timeout): data = get_rates("EUR", "USD") assert data is None - captured = capsys.readouterr() - assert "Fehler beim Verarbeiten der API-Antwort." in captured.out - -def test_check_currency_key_error(monkeypatch, capsys): +def test_check_currency_key_error(monkeypatch): test_resp = Mock() test_resp.raise_for_status.return_value = None test_resp.json.return_value = { - "result": "success", # not passing "success" bypases the "conversion_rate" checking + "result": "", "error_type": "", + # "conversion_rate" fehlt absichtlich } def test_get_resp(url, timeout): @@ -75,9 +64,6 @@ def test_get_resp(url, timeout): data = get_rates("EUR", "USD") assert data is None - captured = capsys.readouterr() - assert "Unerwartete API-Antwortstruktur." in captured.out - def test_check_currency_valid(monkeypatch): test_resp = Mock() @@ -97,7 +83,7 @@ def test_get_resp(url, timeout): assert data == {"result": "success", "error_type": "", "conversion_rate": 1.2} -def test_check_currency_invalid(monkeypatch, capsys): +def test_check_currency_invalid(monkeypatch): test_resp = Mock() test_resp.raise_for_status.return_value = None test_resp.json.return_value = { @@ -114,9 +100,6 @@ def test_get_resp(url, timeout): data = get_rates("EUR", "USD") assert data is None - captured = capsys.readouterr() - assert "Invalid request! Please try again later." in captured.out - def test_check_error(capsys): check_error("unsupported-code") @@ -140,7 +123,3 @@ def test_check_error(capsys): captured.out == "Request limit reached! Please try again later or upgrade to exchangerate-api.com.\n" ) - - check_error("Some unknown Error") - captured = capsys.readouterr() - assert captured.out == "" diff --git a/tests/test_internal_models.py b/tests/test_internal_models.py deleted file mode 100644 index 97df4c6..0000000 --- a/tests/test_internal_models.py +++ /dev/null @@ -1,101 +0,0 @@ -from argus.domain.internal_models import DataSource, Instrument, PriceBar -from datetime import date - - -def test_data_source_can_be_created() -> None: - source = DataSource( - name="yfinance", - provider_kind="fx_rates", - ) - - assert source.name == "yfinance" - assert source.provider_kind == "fx_rates" - assert source.requires_api_key is False - - -def test_instrument_can_be_created() -> None: - instrument = Instrument( - symbol="EUR/USD", - name="Euro / US Dollar", - asset_class="fx", - base_currency="EUR", - quote_currency="USD", - ) - - assert instrument.symbol == "EUR/USD" - assert instrument.name == "Euro / US Dollar" - assert instrument.asset_class == "fx" - assert instrument.base_currency == "EUR" - assert instrument.quote_currency == "USD" - assert instrument.currency is None - assert instrument.exchange is None - - -def test_rate_bar_can_be_created() -> None: - source = DataSource( - name="yfinance", - provider_kind="fx_rates", - ) - - instrument_rate = Instrument( - symbol="EUR/USD", - name="Euro / US Dollar", - asset_class="fx", - base_currency="EUR", - quote_currency="USD", - ) - - price_bar = PriceBar( - source=source, - instrument=instrument_rate, - timestamp=date(2026, 1, 1), - timeframe="1d", - close=1.89, - ) - - assert price_bar.source == source - assert price_bar.instrument == instrument_rate - assert price_bar.timestamp == date(2026, 1, 1) - assert price_bar.timeframe == "1d" - assert price_bar.close == 1.89 - assert price_bar.open is None - assert price_bar.high is None - assert price_bar.low is None - assert price_bar.adjusted_close is None - assert price_bar.volume is None - - -def test_stock_ohlcv_bar_can_be_created() -> None: - source = DataSource( - name="yfinance", - provider_kind="market_prices", - ) - - instrument = Instrument( - symbol="AAPL", - name="Apple Inc.", - asset_class="stock", - currency="USD", - exchange="NASDAQ", - ) - - price_bar = PriceBar( - source=source, - instrument=instrument, - timestamp=date(2026, 1, 1), - timeframe="1d", - open=187.15, - high=188.44, - low=183.89, - close=185.64, - adjusted_close=184.25, - volume=50_200_000, - ) - - assert price_bar.instrument.symbol == "AAPL" - assert price_bar.open == 187.15 - assert price_bar.high == 188.44 - assert price_bar.low == 183.89 - assert price_bar.close == 185.64 - assert price_bar.adjusted_close == 184.25 - assert price_bar.volume == 50_200_000 diff --git a/tests/test_storage_database.py b/tests/test_storage_database.py deleted file mode 100644 index d513008..0000000 --- a/tests/test_storage_database.py +++ /dev/null @@ -1,222 +0,0 @@ -from datetime import date - -import duckdb - -from argus.domain.internal_models import DataSource, Instrument, PriceBar -from argus.storage.database import ( - initialize_database, - insert_price_bar, - read_price_bars, -) - - -def test_initialize_database_creates_required_tables(tmp_path): - db = tmp_path / "test.duckdb" - - initialize_database(db) - connection = duckdb.connect(db) - tables = connection.execute("SHOW TABLES;").fetchall() - connection.close() - table_names = {row[0] for row in tables} - - assert "data_sources" in table_names - assert "instruments" in table_names - assert "price_bars" in table_names - - -def test_data_is_inserted(tmp_path): - source = DataSource( - name="Yahoo", provider_kind="yfinance_api", requires_api_key=False - ) - - instrument = Instrument( - symbol="EUR/USD", - name="EUR - USD Rate", - asset_class="fx", - base_currency="EUR", - quote_currency="USD", - ) - - pricebar = PriceBar( - source=source, - instrument=instrument, - timestamp=date(2026, 1, 1), - timeframe="1d", - close=1.89, - ) - - db = tmp_path / "test.duckdb" - initialize_database(db) - insert_price_bar(db, pricebar) - connection = duckdb.connect(db) - - instrument_count = connection.execute( - "SELECT COUNT(*) FROM instruments;" - ).fetchone() - - source_count = connection.execute("SELECT COUNT(*) FROM data_sources;").fetchone() - - price_bar_count = connection.execute("SELECT COUNT(*) FROM price_bars;").fetchone() - - assert instrument_count is not None - assert source_count is not None - assert price_bar_count is not None - assert instrument_count[0] == 1 - assert source_count[0] == 1 - assert price_bar_count[0] == 1 - - -def test_fx_has_correct_format(tmp_path): - source = DataSource( - name="Yahoo", provider_kind="yfinance_api", requires_api_key=False - ) - - instrument = Instrument( - symbol="EUR/USD", - name="EUR - USD Rate", - asset_class="fx", - base_currency="EUR", - quote_currency="USD", - ) - - pricebar = PriceBar( - source=source, - instrument=instrument, - timestamp=date(2026, 1, 1), - timeframe="1d", - close=1.89, - ) - - db = tmp_path / "test.duckdb" - initialize_database(db) - insert_price_bar(db, pricebar) - connection = duckdb.connect(db) - - price_bar_fx = connection.execute("SELECT * FROM price_bars;").fetchone() - connection.close() - - assert price_bar_fx is not None - assert price_bar_fx[0] == 1 - assert price_bar_fx[1] == 1 - assert price_bar_fx[2] == 1 - assert price_bar_fx[3] == date(2026, 1, 1) - assert price_bar_fx[4] == "1d" - assert price_bar_fx[5] == 1.89 - assert price_bar_fx[6] is None - assert price_bar_fx[7] is None - assert price_bar_fx[8] is None - assert price_bar_fx[9] is None - assert price_bar_fx[10] is None - - -def test_duplicates_are_ignored(tmp_path): - source = DataSource( - name="Yahoo", provider_kind="yfinance_api", requires_api_key=False - ) - - instrument = Instrument( - symbol="EUR/USD", - name="EUR - USD Rate", - asset_class="fx", - base_currency="EUR", - quote_currency="USD", - ) - - pricebar = PriceBar( - source=source, - instrument=instrument, - timestamp=date(2026, 1, 1), - timeframe="1d", - close=1.89, - ) - - db = tmp_path / "test.duckdb" - initialize_database(db) - insert_price_bar(db, pricebar) - insert_price_bar(db, pricebar) - connection = duckdb.connect(db) - count = connection.execute("SELECT COUNT(*) FROM price_bars;").fetchone() - - assert count is not None - assert count[0] == 1 - - -def test_read_price_bars_returns_matching_data(tmp_path): - source = DataSource( - name="Yahoo", - provider_kind="yfinance_api", - requires_api_key=False, - ) - - instrument = Instrument( - symbol="EUR/USD", - name="EUR - USD Rate", - asset_class="fx", - base_currency="EUR", - quote_currency="USD", - ) - - pricebar = PriceBar( - source=source, - instrument=instrument, - timestamp=date(2026, 1, 1), - timeframe="1d", - close=1.89, - ) - - db = tmp_path / "test.duckdb" - initialize_database(db) - insert_price_bar(db, pricebar) - - result = read_price_bars( - db=db, - source=source, - instrument=instrument, - start_date=date(2026, 1, 1), - end_date=date(2026, 1, 31), - ) - - assert result.empty is False - assert len(result) == 1 - assert result.iloc[0]["source_name"] == "Yahoo" - assert result.iloc[0]["instrument_symbol"] == "EUR/USD" - assert result.iloc[0]["timeframe"] == "1d" - assert result.iloc[0]["close"] == 1.89 - - -def test_read_price_bars_returns_empty_dataframe_for_missing_range(tmp_path): - source = DataSource( - name="Yahoo", - provider_kind="yfinance_api", - requires_api_key=False, - ) - - instrument = Instrument( - symbol="EUR/USD", - name="EUR - USD Rate", - asset_class="fx", - base_currency="EUR", - quote_currency="USD", - ) - - pricebar = PriceBar( - source=source, - instrument=instrument, - timestamp=date(2026, 1, 1), - timeframe="1d", - close=1.89, - ) - - db = tmp_path / "test.duckdb" - initialize_database(db) - insert_price_bar(db, pricebar) - - result = read_price_bars( - db=db, - source=source, - instrument=instrument, - start_date=date(2027, 1, 1), - end_date=date(2027, 1, 31), - ) - - assert result.empty is True diff --git a/tests/test_timeseries_service.py b/tests/test_timeseries_service.py index cd5c97a..7dd3c9f 100644 --- a/tests/test_timeseries_service.py +++ b/tests/test_timeseries_service.py @@ -23,9 +23,8 @@ def test_get_a_full_timeseries(): "max_rate": [1.1055831909179688], } result = prepare_trend_analysis(test_curr, test_start, test_end, test_interval) - - assert result is not None - + if result is None: + return False result_df, result_dict = result result_df["date"] = result_df["date"].astype("str") result_dict["min_date"] = [str(result_dict["min_date"][0])] diff --git a/tests/test_validation_domain.py b/tests/test_validation_domain.py index 0166741..a5bd41f 100644 --- a/tests/test_validation_domain.py +++ b/tests/test_validation_domain.py @@ -7,13 +7,9 @@ def test_op_is_valid(): + data = is_valid_op("+") - assert is_valid_op("+") is True - assert is_valid_op("-") is True - assert is_valid_op("*") is True - assert is_valid_op("/") is True - assert is_valid_op("%") is True - assert is_valid_op("**") is True + assert data is True def test_op_is_not_valid(): diff --git a/tests/test_yfinance_client.py b/tests/test_yfinance_client.py index 6201b19..faf15fc 100644 --- a/tests/test_yfinance_client.py +++ b/tests/test_yfinance_client.py @@ -72,7 +72,7 @@ def test_error_raise(monkeypatch): def fake_yfinance_download( tickers=test_curr, start=test_start, end=test_end, interval=test_interval ): - raise Exception("fake yfinance error") + return Exception("fake yfinance error") monkeypatch.setattr("yfinance.download", fake_yfinance_download)