v0 iOS app
This commit is contained in:
Jiahao Zhang
parent
f8dc1fca25
commit
4b12480559
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@ -1,16 +1,226 @@
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env/
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# Python
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__pycache__/
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.DS_Store
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*.csv
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src/
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eval_results/
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eval_data/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
|
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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# Environment variables
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.installed.cfg
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*.egg
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MANIFEST
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.env
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.env.local
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.env.*.local
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.venv
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||||
env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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.pytest_cache/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.mypy_cache/
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.dmypy.json
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dmypy.json
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.tox/
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.nox/
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# Results folder - ignore all analysis outputs
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# UV Package Manager
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uv.lock
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|
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# Project specific - Results & Evaluation Data
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results/
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eval_results/
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backend/eval_results/
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backend/results/
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data_cache/
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backend/tradingagents/dataflows/data_cache/
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*.log
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logs/
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*.out
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*.pid
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|
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# API & Runtime Files
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*.sock
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.restart_api_*
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api_*.log
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server_*.log
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# Configuration & Secrets
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config.local.*
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*.key
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*.pem
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*.p12
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*.p8
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api_keys.txt
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secrets.json
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# macOS
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.DS_Store
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.AppleDouble
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.LSOverride
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Icon?
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._*
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.DocumentRevisions-V100
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.fseventsd
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.Spotlight-V100
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.TemporaryItems
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.Trashes
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.VolumeIcon.icns
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.com.apple.timemachine.donotpresent
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.AppleDB
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.AppleDesktop
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Network Trash Folder
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Temporary Items
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.apdisk
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||||
|
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# Xcode & iOS
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*.xcuserstate
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*.xcuserdatad
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project.xcworkspace/
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xcuserdata/
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*.xccheckout
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*.moved-aside
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DerivedData/
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*.hmap
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*.ipa
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*.dSYM.zip
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*.dSYM
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*.xcscmblueprint
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*.xcarchive
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timeline.xctimeline
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playground.xcworkspace
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.build/
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|
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# Swift Package Manager
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.build/
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.swiftpm/
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Package.resolved
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# CocoaPods
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Pods/
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*.podspec
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Podfile.lock
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|
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# Carthage
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Carthage/Build/
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# Accio dependency management
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Dependencies/
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.accio/
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|
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# fastlane
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fastlane/report.xml
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fastlane/Preview.html
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fastlane/screenshots/**/*.png
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fastlane/test_output
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fastlane/readme.md
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# Code Injection
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iOSInjectionProject/
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# IDE & Editors
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.idea/
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.vscode/
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*.swp
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*.swo
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*~
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.sublime-workspace
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.sublime-project
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*.code-workspace
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|
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# JetBrains
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.idea/
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*.iws
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*.iml
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*.ipr
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out/
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# Vim
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*.swp
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*.swo
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.netrwhist
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|
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# Emacs
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*~
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\#*\#
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/.emacs.desktop
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/.emacs.desktop.lock
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*.elc
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auto-save-list
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tramp
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.\#*
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# Backup files
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*.bak
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*.backup
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*.old
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*.orig
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*.rej
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*.tmp
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# Temporary files
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.tmp/
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temp/
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tmp/
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*.temp
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|
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# Database
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*.db
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*.sqlite
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*.sqlite3
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# Jupyter Notebook
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.ipynb_checkpoints
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|
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# pyenv
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.python-version
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# pipenv
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Pipfile.lock
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# PEP 582
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__pypackages__/
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# Celery
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celerybeat-schedule
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celerybeat.pid
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# SageMath
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*.sage.py
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# Spyder
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.spyderproject
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.spyproject
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# Rope
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.ropeproject
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# mkdocs
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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|
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# Pyre
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.pyre/
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|
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73
README.md
73
README.md
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@ -1,6 +1,41 @@
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<p align="center">
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<img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
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</p>
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# 🚀 Trading Agents
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<div align="center">
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<img src="assets/TauricResearch.png" alt="Tauric Logo" width="400"/>
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[](https://opensource.org/licenses/MIT)
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[](https://www.python.org/downloads/)
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</div>
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## 📁 Project Structure
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The project is organized into clear, separate components:
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```
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TradingAgents/
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├── backend/ # Python backend server and trading logic
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│ ├── api.py # FastAPI server
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│ ├── tradingagents/ # Core trading agents and logic
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│ ├── cli/ # Command-line interface
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│ └── requirements.txt # Python dependencies
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├── ios/ # iOS mobile application
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│ └── TradingDummy/ # Swift/SwiftUI app project
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├── docs/ # All documentation
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│ ├── PRD.md # Product requirements
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│ ├── DOCUMENTATION.md # Technical documentation
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│ └── FASTAPI_SETUP.md # API setup guide
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├── assets/ # Images and media assets
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├── README.md # This file
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└── LICENSE # MIT license
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```
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### Quick Navigation
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- 🐍 [Backend Setup](backend/README.md) - Python server and trading agents
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- 📱 [iOS App Setup](ios/README.md) - Mobile application
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- 📚 [Documentation](docs/README.md) - All project documentation
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## Overview
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<div align="center" style="line-height: 1;">
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<a href="https://arxiv.org/abs/2412.20138" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv"/></a>
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@ -93,7 +128,7 @@ Our framework decomposes complex trading tasks into specialized roles. This ensu
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## Installation and CLI
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### Installation
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### Backend Installation
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Clone TradingAgents:
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```bash
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@ -107,8 +142,9 @@ conda create -n tradingagents python=3.13
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conda activate tradingagents
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```
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Install dependencies:
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Install backend dependencies:
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```bash
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cd backend
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pip install -r requirements.txt
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```
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@ -126,12 +162,37 @@ export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
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### CLI Usage
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You can also try out the CLI directly by running:
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From the backend directory, you can try out the CLI directly by running:
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```bash
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python -m cli.main
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```
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You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
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||||
### FastAPI Server
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||||
Alternatively, you can run the FastAPI server for REST API access:
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```bash
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python run_api.py
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||||
```
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The server will start at `http://localhost:8000`. You can then use the iOS app or make API calls directly.
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||||
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||||
### iOS App
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||||
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||||
To use the iOS companion app:
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||||
|
||||
1. Navigate to the iOS directory:
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||||
```bash
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||||
cd ios/TradingDummy
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||||
```
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||||
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||||
2. Open the project in Xcode:
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||||
```bash
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||||
open TradingDummy.xcodeproj
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||||
```
|
||||
|
||||
3. Make sure the backend server is running, then build and run the app (⌘+R)
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||||
|
||||
<p align="center">
|
||||
<img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
|
||||
</p>
|
||||
|
|
|
|||
|
|
@ -0,0 +1,73 @@
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|||
# TradingDummy iOS App - Project Structure
|
||||
|
||||
## Overview
|
||||
|
||||
The iOS app is now organized following MVVM architecture with clear separation of concerns.
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||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
TradingDummy/
|
||||
├── Configuration/
|
||||
│ └── AppConfig.swift # App-wide configuration (API URLs, timeouts)
|
||||
├── Models/
|
||||
│ └── AnalysisModels.swift # Data models (Request/Response)
|
||||
├── Services/
|
||||
│ └── TradingAgentsService.swift # API service layer
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||||
├── ViewModels/
|
||||
│ └── TradingAnalysisViewModel.swift # Business logic
|
||||
├── Views/
|
||||
│ ├── TradingAnalysisView.swift # Main view
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||||
│ ├── AnalysisResultView.swift # Result display components
|
||||
│ └── SupportingViews.swift # Loading, Error, Welcome views
|
||||
├── TradingDummyApp.swift # App entry point
|
||||
└── ContentView.swift # Original demo view (can be removed)
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
### Models
|
||||
- `AnalysisRequest`: Request payload for API
|
||||
- `AnalysisResponse`: Response data structure with all analysis reports
|
||||
|
||||
### Services
|
||||
- `TradingAgentsService`: Singleton service for API communication
|
||||
- `APIError`: Custom error types for better error handling
|
||||
|
||||
### ViewModels
|
||||
- `TradingAnalysisViewModel`: Manages state and business logic
|
||||
- Uses `@Published` properties for SwiftUI binding
|
||||
- Handles async API calls with proper error handling
|
||||
|
||||
### Views
|
||||
- `TradingAnalysisView`: Main screen with ticker input
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||||
- `AnalysisResultView`: Displays analysis results with expandable cards
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||||
- Supporting views for different states (loading, error, welcome)
|
||||
|
||||
## Configuration
|
||||
|
||||
### Development
|
||||
- API URL: `http://localhost:8000`
|
||||
- Timeout: 60 seconds
|
||||
|
||||
### Production
|
||||
- Update `AppConfig.swift` with your production URL
|
||||
- Build with Release configuration
|
||||
|
||||
## Usage
|
||||
|
||||
1. Open `TradingDummy.xcodeproj` in Xcode
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||||
2. Build and run (⌘+R)
|
||||
3. The app uses Xcode 16's file system synchronized groups - all Swift files are automatically included
|
||||
|
||||
## Testing
|
||||
|
||||
1. Ensure backend is running: `cd backend && uv run python3 run_api.py`
|
||||
2. Run the app in simulator
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||||
3. Enter a ticker symbol and tap "Analyze"
|
||||
|
||||
## Notes
|
||||
|
||||
- All files in subdirectories are automatically included by Xcode 16
|
||||
- No need to manually add files to the project
|
||||
- The modular structure makes it easy to add new features or modify existing ones
|
||||
|
|
@ -0,0 +1,639 @@
|
|||
// !$*UTF8*$!
|
||||
{
|
||||
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|
||||
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|
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|
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|
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DEVELOPMENT_TEAM = 7T2V4G2PCV;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
IPHONEOS_DEPLOYMENT_TARGET = 18.1;
|
||||
MACOSX_DEPLOYMENT_TARGET = 15.1;
|
||||
MARKETING_VERSION = 1.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = zjh08177.TradingDummyTests;
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SDKROOT = auto;
|
||||
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator macosx xros xrsimulator";
|
||||
SWIFT_EMIT_LOC_STRINGS = NO;
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2,7";
|
||||
TEST_HOST = "$(BUILT_PRODUCTS_DIR)/TradingDummy.app/$(BUNDLE_EXECUTABLE_FOLDER_PATH)/TradingDummy";
|
||||
XROS_DEPLOYMENT_TARGET = 2.1;
|
||||
};
|
||||
name = Debug;
|
||||
};
|
||||
2B8FC8792E1077B6000CC9A0 /* Release */ = {
|
||||
isa = XCBuildConfiguration;
|
||||
buildSettings = {
|
||||
BUNDLE_LOADER = "$(TEST_HOST)";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 1;
|
||||
DEVELOPMENT_TEAM = 7T2V4G2PCV;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
IPHONEOS_DEPLOYMENT_TARGET = 18.1;
|
||||
MACOSX_DEPLOYMENT_TARGET = 15.1;
|
||||
MARKETING_VERSION = 1.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = zjh08177.TradingDummyTests;
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SDKROOT = auto;
|
||||
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator macosx xros xrsimulator";
|
||||
SWIFT_EMIT_LOC_STRINGS = NO;
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2,7";
|
||||
TEST_HOST = "$(BUILT_PRODUCTS_DIR)/TradingDummy.app/$(BUNDLE_EXECUTABLE_FOLDER_PATH)/TradingDummy";
|
||||
XROS_DEPLOYMENT_TARGET = 2.1;
|
||||
};
|
||||
name = Release;
|
||||
};
|
||||
2B8FC87B2E1077B6000CC9A0 /* Debug */ = {
|
||||
isa = XCBuildConfiguration;
|
||||
buildSettings = {
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 1;
|
||||
DEVELOPMENT_TEAM = 7T2V4G2PCV;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
IPHONEOS_DEPLOYMENT_TARGET = 18.1;
|
||||
MACOSX_DEPLOYMENT_TARGET = 15.1;
|
||||
MARKETING_VERSION = 1.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = zjh08177.TradingDummyUITests;
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SDKROOT = auto;
|
||||
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator macosx xros xrsimulator";
|
||||
SWIFT_EMIT_LOC_STRINGS = NO;
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2,7";
|
||||
TEST_TARGET_NAME = TradingDummy;
|
||||
XROS_DEPLOYMENT_TARGET = 2.1;
|
||||
};
|
||||
name = Debug;
|
||||
};
|
||||
2B8FC87C2E1077B6000CC9A0 /* Release */ = {
|
||||
isa = XCBuildConfiguration;
|
||||
buildSettings = {
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 1;
|
||||
DEVELOPMENT_TEAM = 7T2V4G2PCV;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
IPHONEOS_DEPLOYMENT_TARGET = 18.1;
|
||||
MACOSX_DEPLOYMENT_TARGET = 15.1;
|
||||
MARKETING_VERSION = 1.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = zjh08177.TradingDummyUITests;
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SDKROOT = auto;
|
||||
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator macosx xros xrsimulator";
|
||||
SWIFT_EMIT_LOC_STRINGS = NO;
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2,7";
|
||||
TEST_TARGET_NAME = TradingDummy;
|
||||
XROS_DEPLOYMENT_TARGET = 2.1;
|
||||
};
|
||||
name = Release;
|
||||
};
|
||||
/* End XCBuildConfiguration section */
|
||||
|
||||
/* Begin XCConfigurationList section */
|
||||
2B8FC84A2E1077B4000CC9A0 /* Build configuration list for PBXProject "TradingDummy" */ = {
|
||||
isa = XCConfigurationList;
|
||||
buildConfigurations = (
|
||||
2B8FC8722E1077B6000CC9A0 /* Debug */,
|
||||
2B8FC8732E1077B6000CC9A0 /* Release */,
|
||||
);
|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Release;
|
||||
};
|
||||
2B8FC8742E1077B6000CC9A0 /* Build configuration list for PBXNativeTarget "TradingDummy" */ = {
|
||||
isa = XCConfigurationList;
|
||||
buildConfigurations = (
|
||||
2B8FC8752E1077B6000CC9A0 /* Debug */,
|
||||
2B8FC8762E1077B6000CC9A0 /* Release */,
|
||||
);
|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Release;
|
||||
};
|
||||
2B8FC8772E1077B6000CC9A0 /* Build configuration list for PBXNativeTarget "TradingDummyTests" */ = {
|
||||
isa = XCConfigurationList;
|
||||
buildConfigurations = (
|
||||
2B8FC8782E1077B6000CC9A0 /* Debug */,
|
||||
2B8FC8792E1077B6000CC9A0 /* Release */,
|
||||
);
|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Release;
|
||||
};
|
||||
2B8FC87A2E1077B6000CC9A0 /* Build configuration list for PBXNativeTarget "TradingDummyUITests" */ = {
|
||||
isa = XCConfigurationList;
|
||||
buildConfigurations = (
|
||||
2B8FC87B2E1077B6000CC9A0 /* Debug */,
|
||||
2B8FC87C2E1077B6000CC9A0 /* Release */,
|
||||
);
|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Release;
|
||||
};
|
||||
/* End XCConfigurationList section */
|
||||
|
||||
/* Begin XCRemoteSwiftPackageReference section */
|
||||
2B8FC87D2E1077CC000CC9A0 /* XCRemoteSwiftPackageReference "ReSwift" */ = {
|
||||
isa = XCRemoteSwiftPackageReference;
|
||||
repositoryURL = "https://github.com/ReSwift/ReSwift.git";
|
||||
requirement = {
|
||||
kind = upToNextMajorVersion;
|
||||
minimumVersion = 6.1.1;
|
||||
};
|
||||
};
|
||||
/* End XCRemoteSwiftPackageReference section */
|
||||
|
||||
/* Begin XCSwiftPackageProductDependency section */
|
||||
2B8FC87E2E1077CC000CC9A0 /* ReSwift */ = {
|
||||
isa = XCSwiftPackageProductDependency;
|
||||
package = 2B8FC87D2E1077CC000CC9A0 /* XCRemoteSwiftPackageReference "ReSwift" */;
|
||||
productName = ReSwift;
|
||||
};
|
||||
/* End XCSwiftPackageProductDependency section */
|
||||
};
|
||||
rootObject = 2B8FC8472E1077B4000CC9A0 /* Project object */;
|
||||
}
|
||||
7
TradingDummy/TradingDummy.xcodeproj/project.xcworkspace/contents.xcworkspacedata
generated
Normal file
7
TradingDummy/TradingDummy.xcodeproj/project.xcworkspace/contents.xcworkspacedata
generated
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<Workspace
|
||||
version = "1.0">
|
||||
<FileRef
|
||||
location = "self:">
|
||||
</FileRef>
|
||||
</Workspace>
|
||||
Binary file not shown.
|
|
@ -0,0 +1,14 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>SchemeUserState</key>
|
||||
<dict>
|
||||
<key>TradingDummy.xcscheme_^#shared#^_</key>
|
||||
<dict>
|
||||
<key>orderHint</key>
|
||||
<integer>0</integer>
|
||||
</dict>
|
||||
</dict>
|
||||
</dict>
|
||||
</plist>
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
{
|
||||
"colors" : [
|
||||
{
|
||||
"idiom" : "universal"
|
||||
}
|
||||
],
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
{
|
||||
"images" : [
|
||||
{
|
||||
"idiom" : "universal",
|
||||
"platform" : "ios",
|
||||
"size" : "1024x1024"
|
||||
},
|
||||
{
|
||||
"appearances" : [
|
||||
{
|
||||
"appearance" : "luminosity",
|
||||
"value" : "dark"
|
||||
}
|
||||
],
|
||||
"idiom" : "universal",
|
||||
"platform" : "ios",
|
||||
"size" : "1024x1024"
|
||||
},
|
||||
{
|
||||
"appearances" : [
|
||||
{
|
||||
"appearance" : "luminosity",
|
||||
"value" : "tinted"
|
||||
}
|
||||
],
|
||||
"idiom" : "universal",
|
||||
"platform" : "ios",
|
||||
"size" : "1024x1024"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "1x",
|
||||
"size" : "16x16"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "2x",
|
||||
"size" : "16x16"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "1x",
|
||||
"size" : "32x32"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "2x",
|
||||
"size" : "32x32"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "1x",
|
||||
"size" : "128x128"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "2x",
|
||||
"size" : "128x128"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "1x",
|
||||
"size" : "256x256"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "2x",
|
||||
"size" : "256x256"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "1x",
|
||||
"size" : "512x512"
|
||||
},
|
||||
{
|
||||
"idiom" : "mac",
|
||||
"scale" : "2x",
|
||||
"size" : "512x512"
|
||||
}
|
||||
],
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
import Foundation
|
||||
|
||||
enum AppConfig {
|
||||
// API Configuration
|
||||
static let apiBaseURL: String = {
|
||||
// Check for environment variable first (useful for CI/CD)
|
||||
if let envURL = ProcessInfo.processInfo.environment["TRADINGAGENTS_API_URL"] {
|
||||
return envURL
|
||||
}
|
||||
|
||||
// Default URLs for different environments
|
||||
#if DEBUG
|
||||
#if targetEnvironment(simulator)
|
||||
// For iOS Simulator
|
||||
return "http://localhost:8000"
|
||||
#else
|
||||
// For real device - UPDATE THIS WITH YOUR MAC'S IP
|
||||
return "http://192.168.4.223:8000"
|
||||
#endif
|
||||
#else
|
||||
// For production, update this to your deployed server URL
|
||||
return "https://api.tradingagents.com"
|
||||
#endif
|
||||
}()
|
||||
|
||||
// Network Configuration
|
||||
static let requestTimeout: TimeInterval = 600.0
|
||||
static let maxRetries = 3
|
||||
|
||||
// UI Configuration
|
||||
static let defaultTicker = "AAPL"
|
||||
static let animationDuration = 0.3
|
||||
}
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>NSLocalNetworkUsageDescription</key>
|
||||
<string>This app needs to connect to your local trading analysis server to provide market insights and trading analysis.</string>
|
||||
<key>NSAppTransportSecurity</key>
|
||||
<dict>
|
||||
<key>NSAllowsArbitraryLoads</key>
|
||||
<false/>
|
||||
<key>NSExceptionDomains</key>
|
||||
<dict>
|
||||
<key>localhost</key>
|
||||
<dict>
|
||||
<key>NSExceptionAllowsInsecureHTTPLoads</key>
|
||||
<true/>
|
||||
<key>NSExceptionMinimumTLSVersion</key>
|
||||
<string>TLSv1.0</string>
|
||||
</dict>
|
||||
<key>192.168.4.223</key>
|
||||
<dict>
|
||||
<key>NSExceptionAllowsInsecureHTTPLoads</key>
|
||||
<true/>
|
||||
<key>NSExceptionMinimumTLSVersion</key>
|
||||
<string>TLSv1.0</string>
|
||||
</dict>
|
||||
</dict>
|
||||
</dict>
|
||||
</dict>
|
||||
</plist>
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
import Foundation
|
||||
|
||||
// MARK: - Request Model
|
||||
struct AnalysisRequest: Codable {
|
||||
let ticker: String
|
||||
}
|
||||
|
||||
// MARK: - Response Model
|
||||
struct AnalysisResponse: Codable {
|
||||
let ticker: String
|
||||
let analysisDate: String
|
||||
let marketReport: String?
|
||||
let sentimentReport: String?
|
||||
let newsReport: String?
|
||||
let fundamentalsReport: String?
|
||||
let investmentPlan: String?
|
||||
let traderInvestmentPlan: String?
|
||||
let finalTradeDecision: String?
|
||||
let processedSignal: String?
|
||||
let error: String?
|
||||
|
||||
enum CodingKeys: String, CodingKey {
|
||||
case ticker
|
||||
case analysisDate = "analysis_date"
|
||||
case marketReport = "market_report"
|
||||
case sentimentReport = "sentiment_report"
|
||||
case newsReport = "news_report"
|
||||
case fundamentalsReport = "fundamentals_report"
|
||||
case investmentPlan = "investment_plan"
|
||||
case traderInvestmentPlan = "trader_investment_plan"
|
||||
case finalTradeDecision = "final_trade_decision"
|
||||
case processedSignal = "processed_signal"
|
||||
case error
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,357 @@
|
|||
import Foundation
|
||||
import Combine
|
||||
import os.log
|
||||
|
||||
// MARK: - SSE Event Models
|
||||
public struct SSEEvent: Codable {
|
||||
let type: String
|
||||
let message: String?
|
||||
let agent: String?
|
||||
let section: String?
|
||||
let content: String?
|
||||
let status: String?
|
||||
}
|
||||
|
||||
// MARK: - Progress Models
|
||||
public struct AnalysisProgress {
|
||||
public let currentAgent: String
|
||||
public let message: String
|
||||
public let reports: [String: String]
|
||||
public let isComplete: Bool
|
||||
public let error: String?
|
||||
|
||||
public init(currentAgent: String, message: String, reports: [String: String], isComplete: Bool, error: String?) {
|
||||
self.currentAgent = currentAgent
|
||||
self.message = message
|
||||
self.reports = reports
|
||||
self.isComplete = isComplete
|
||||
self.error = error
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - TradingAgentsService
|
||||
public class TradingAgentsService: ObservableObject {
|
||||
internal let logger = Logger(subsystem: "com.tradingagents.app", category: "TradingAgentsService")
|
||||
|
||||
private let baseURL: String = {
|
||||
// Check for environment variable first
|
||||
if let envURL = ProcessInfo.processInfo.environment["TRADINGAGENTS_API_URL"] {
|
||||
return envURL
|
||||
}
|
||||
|
||||
// Default URLs for different environments
|
||||
#if DEBUG
|
||||
#if targetEnvironment(simulator)
|
||||
// For iOS Simulator
|
||||
return "http://localhost:8000"
|
||||
#else
|
||||
// For real device - UPDATE THIS WITH YOUR MAC'S IP
|
||||
return "http://192.168.4.223:8000"
|
||||
#endif
|
||||
#else
|
||||
// For production, update this to your deployed server URL
|
||||
return "https://api.tradingagents.com"
|
||||
#endif
|
||||
}()
|
||||
|
||||
private var eventSource: URLSessionDataTask?
|
||||
private var streamingDelegate: SSEStreamDelegate?
|
||||
private let session: URLSession
|
||||
|
||||
@Published var isAnalyzing = false
|
||||
@Published var progress = AnalysisProgress(
|
||||
currentAgent: "",
|
||||
message: "",
|
||||
reports: [:],
|
||||
isComplete: false,
|
||||
error: nil
|
||||
)
|
||||
|
||||
public init() {
|
||||
self.session = URLSession.shared
|
||||
logger.info("🚀 TradingAgentsService initialized with baseURL: \(self.baseURL)")
|
||||
}
|
||||
|
||||
public func streamAnalysis(for ticker: String) -> AnyPublisher<AnalysisProgress, Never> {
|
||||
let subject = PassthroughSubject<AnalysisProgress, Never>()
|
||||
|
||||
logger.info("📡 Starting stream analysis for ticker: \(ticker)")
|
||||
|
||||
let urlString = "\(baseURL)/analyze/stream?ticker=\(ticker)"
|
||||
logger.info("🌐 Request URL: \(urlString)")
|
||||
|
||||
guard let url = URL(string: urlString) else {
|
||||
logger.error("❌ Invalid URL: \(urlString)")
|
||||
subject.send(AnalysisProgress(
|
||||
currentAgent: "",
|
||||
message: "",
|
||||
reports: [:],
|
||||
isComplete: true,
|
||||
error: "Invalid URL: \(urlString)"
|
||||
))
|
||||
return subject.eraseToAnyPublisher()
|
||||
}
|
||||
|
||||
var request = URLRequest(url: url)
|
||||
request.setValue("text/event-stream", forHTTPHeaderField: "Accept")
|
||||
request.setValue("no-cache", forHTTPHeaderField: "Cache-Control")
|
||||
request.setValue("keep-alive", forHTTPHeaderField: "Connection")
|
||||
request.timeoutInterval = 600.0 // 10 minutes
|
||||
|
||||
logger.info("📋 Request headers: \(request.allHTTPHeaderFields ?? [:])")
|
||||
|
||||
// Use direct streaming approach
|
||||
self.streamWithCustomSession(request: request, subject: subject)
|
||||
|
||||
return subject.eraseToAnyPublisher()
|
||||
}
|
||||
|
||||
private func streamWithCustomSession(request: URLRequest, subject: PassthroughSubject<AnalysisProgress, Never>) {
|
||||
logger.info("🔄 Starting SSE streaming with delegate")
|
||||
|
||||
// Create delegate for real-time streaming
|
||||
let delegate = SSEStreamDelegate(subject: subject, service: self)
|
||||
|
||||
// Create session with delegate for streaming
|
||||
let config = URLSessionConfiguration.default
|
||||
config.timeoutIntervalForRequest = 600.0
|
||||
config.timeoutIntervalForResource = 600.0
|
||||
let delegateSession = URLSession(configuration: config, delegate: delegate, delegateQueue: nil)
|
||||
|
||||
let task = delegateSession.dataTask(with: request)
|
||||
delegate.task = task
|
||||
|
||||
// Store delegate and task
|
||||
self.streamingDelegate = delegate
|
||||
self.eventSource = task
|
||||
|
||||
task.resume()
|
||||
logger.info("🚀 SSE Stream task started with delegate")
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
internal func formatAgentName(_ agent: String) -> String {
|
||||
switch agent.lowercased() {
|
||||
case "market": return "Market Analyst"
|
||||
case "social": return "Social Media Analyst"
|
||||
case "news": return "News Analyst"
|
||||
case "fundamentals": return "Fundamentals Analyst"
|
||||
case "bull_researcher": return "Bull Researcher"
|
||||
case "bear_researcher": return "Bear Researcher"
|
||||
case "trader": return "Trading Team"
|
||||
default: return agent.capitalized
|
||||
}
|
||||
}
|
||||
|
||||
internal func formatSectionName(_ section: String) -> String {
|
||||
switch section {
|
||||
case "market_report": return "Market Analysis"
|
||||
case "sentiment_report": return "Sentiment Analysis"
|
||||
case "news_report": return "News Analysis"
|
||||
case "fundamentals_report": return "Fundamentals Analysis"
|
||||
case "investment_plan": return "Investment Plan"
|
||||
case "trader_investment_plan": return "Trading Plan"
|
||||
case "final_trade_decision": return "Final Decision"
|
||||
default: return section.replacingOccurrences(of: "_", with: " ").capitalized
|
||||
}
|
||||
}
|
||||
|
||||
public func stopAnalysis() {
|
||||
eventSource?.cancel()
|
||||
eventSource = nil
|
||||
streamingDelegate = nil
|
||||
isAnalyzing = false
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - SSE Stream Delegate for Real-time Streaming
|
||||
private class SSEStreamDelegate: NSObject, URLSessionDataDelegate {
|
||||
private let subject: PassthroughSubject<AnalysisProgress, Never>
|
||||
private weak var service: TradingAgentsService?
|
||||
private var buffer = ""
|
||||
private var currentReports: [String: String] = [:]
|
||||
var task: URLSessionDataTask?
|
||||
|
||||
init(subject: PassthroughSubject<AnalysisProgress, Never>, service: TradingAgentsService) {
|
||||
self.subject = subject
|
||||
self.service = service
|
||||
super.init()
|
||||
}
|
||||
|
||||
func urlSession(_ session: URLSession, dataTask: URLSessionDataTask, didReceive response: URLResponse, completionHandler: @escaping (URLSession.ResponseDisposition) -> Void) {
|
||||
guard let httpResponse = response as? HTTPURLResponse else {
|
||||
completionHandler(.cancel)
|
||||
return
|
||||
}
|
||||
|
||||
service?.logger.info("📶 SSE HTTP Response Status: \(httpResponse.statusCode)")
|
||||
|
||||
if httpResponse.statusCode == 200 {
|
||||
completionHandler(.allow)
|
||||
} else {
|
||||
service?.logger.error("❌ SSE HTTP Error: \(httpResponse.statusCode)")
|
||||
DispatchQueue.main.async {
|
||||
self.subject.send(AnalysisProgress(
|
||||
currentAgent: "",
|
||||
message: "",
|
||||
reports: self.currentReports,
|
||||
isComplete: true,
|
||||
error: "HTTP \(httpResponse.statusCode)"
|
||||
))
|
||||
}
|
||||
completionHandler(.cancel)
|
||||
}
|
||||
}
|
||||
|
||||
func urlSession(_ session: URLSession, dataTask: URLSessionDataTask, didReceive data: Data) {
|
||||
guard let service = service else { return }
|
||||
|
||||
let newData = String(data: data, encoding: .utf8) ?? ""
|
||||
service.logger.info("📦 Received \(data.count) bytes: \(String(newData.prefix(100)))...")
|
||||
|
||||
// Add new data to buffer
|
||||
buffer += newData
|
||||
|
||||
// Process complete lines
|
||||
let lines = buffer.components(separatedBy: .newlines)
|
||||
buffer = lines.last ?? "" // Keep incomplete line in buffer
|
||||
|
||||
// Process all complete lines except the last (incomplete) one
|
||||
for line in lines.dropLast() {
|
||||
if line.hasPrefix("data: ") {
|
||||
let jsonString = String(line.dropFirst(6))
|
||||
service.logger.info("🔍 Processing JSON: \(String(jsonString.prefix(50)))...")
|
||||
|
||||
if let jsonData = jsonString.data(using: .utf8),
|
||||
let event = try? JSONDecoder().decode(SSEEvent.self, from: jsonData) {
|
||||
|
||||
service.logger.info("✅ Decoded event - Type: \(event.type)")
|
||||
|
||||
DispatchQueue.main.async {
|
||||
self.processSSEEvent(event, service: service)
|
||||
}
|
||||
} else {
|
||||
service.logger.warning("⚠️ Failed to decode JSON: \(jsonString)")
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
func urlSession(_ session: URLSession, dataTask: URLSessionDataTask, didCompleteWithError error: Error?) {
|
||||
if let error = error {
|
||||
service?.logger.error("❌ SSE Connection error: \(error.localizedDescription)")
|
||||
DispatchQueue.main.async {
|
||||
self.subject.send(AnalysisProgress(
|
||||
currentAgent: "",
|
||||
message: "",
|
||||
reports: self.currentReports,
|
||||
isComplete: true,
|
||||
error: "Connection error: \(error.localizedDescription)"
|
||||
))
|
||||
}
|
||||
} else {
|
||||
service?.logger.info("✅ SSE Connection completed successfully")
|
||||
}
|
||||
}
|
||||
|
||||
private func processSSEEvent(_ event: SSEEvent, service: TradingAgentsService) {
|
||||
switch event.type {
|
||||
case "status":
|
||||
service.logger.info("📢 Status: \(event.message ?? "")")
|
||||
subject.send(AnalysisProgress(
|
||||
currentAgent: "Starting",
|
||||
message: event.message ?? "Starting analysis...",
|
||||
reports: currentReports,
|
||||
isComplete: false,
|
||||
error: nil
|
||||
))
|
||||
|
||||
case "agent_status":
|
||||
let agentName = service.formatAgentName(event.agent ?? "")
|
||||
let statusMessage = event.status == "completed" ?
|
||||
"✅ \(agentName) completed" :
|
||||
"🔄 Analyzing with \(agentName)..."
|
||||
|
||||
service.logger.info("👤 Agent: \(agentName), Status: \(event.status ?? "")")
|
||||
subject.send(AnalysisProgress(
|
||||
currentAgent: agentName,
|
||||
message: statusMessage,
|
||||
reports: currentReports,
|
||||
isComplete: false,
|
||||
error: nil
|
||||
))
|
||||
|
||||
case "progress":
|
||||
if let content = event.content, let percentage = Int(content) {
|
||||
service.logger.info("📊 Progress: \(percentage)%")
|
||||
subject.send(AnalysisProgress(
|
||||
currentAgent: service.progress.currentAgent,
|
||||
message: "Progress: \(percentage)%",
|
||||
reports: currentReports,
|
||||
isComplete: false,
|
||||
error: nil
|
||||
))
|
||||
}
|
||||
|
||||
case "report":
|
||||
if let section = event.section, let content = event.content {
|
||||
service.logger.info("📊 Report: \(section)")
|
||||
currentReports[section] = content
|
||||
subject.send(AnalysisProgress(
|
||||
currentAgent: service.progress.currentAgent,
|
||||
message: "📊 Updated \(service.formatSectionName(section))",
|
||||
reports: currentReports,
|
||||
isComplete: false,
|
||||
error: nil
|
||||
))
|
||||
}
|
||||
|
||||
case "complete":
|
||||
service.logger.info("✅ Analysis completed")
|
||||
subject.send(AnalysisProgress(
|
||||
currentAgent: "Complete",
|
||||
message: "✅ Analysis completed successfully",
|
||||
reports: currentReports,
|
||||
isComplete: true,
|
||||
error: nil
|
||||
))
|
||||
|
||||
case "error":
|
||||
service.logger.error("❌ Error: \(event.message ?? "")")
|
||||
subject.send(AnalysisProgress(
|
||||
currentAgent: "",
|
||||
message: "",
|
||||
reports: currentReports,
|
||||
isComplete: true,
|
||||
error: event.message ?? "Unknown error"
|
||||
))
|
||||
|
||||
default:
|
||||
service.logger.info("ℹ️ Unknown event type: \(event.type)")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - API Errors
|
||||
enum APIError: LocalizedError {
|
||||
case invalidURL
|
||||
case invalidResponse
|
||||
case httpError(statusCode: Int)
|
||||
case serverError(message: String)
|
||||
|
||||
var errorDescription: String? {
|
||||
switch self {
|
||||
case .invalidURL:
|
||||
return "Invalid API URL"
|
||||
case .invalidResponse:
|
||||
return "Invalid server response"
|
||||
case .httpError(let statusCode):
|
||||
return "HTTP error: \(statusCode)"
|
||||
case .serverError(let message):
|
||||
return "Server error: \(message)"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>com.apple.security.app-sandbox</key>
|
||||
<true/>
|
||||
<key>com.apple.security.files.user-selected.read-only</key>
|
||||
<true/>
|
||||
</dict>
|
||||
</plist>
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
//
|
||||
// TradingDummyApp.swift
|
||||
// TradingDummy
|
||||
//
|
||||
// Created by ByteDance on 6/28/25.
|
||||
//
|
||||
|
||||
import SwiftUI
|
||||
|
||||
@main
|
||||
struct TradingDummyApp: App {
|
||||
var body: some Scene {
|
||||
WindowGroup {
|
||||
TradingAnalysisView()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,141 @@
|
|||
import Foundation
|
||||
import Combine
|
||||
|
||||
// MARK: - View Model
|
||||
@MainActor
|
||||
class TradingAnalysisViewModel: ObservableObject {
|
||||
// MARK: - Published Properties
|
||||
@Published var ticker: String = ""
|
||||
@Published var isAnalyzing: Bool = false
|
||||
@Published var showingResults: Bool = false
|
||||
@Published var errorMessage: String?
|
||||
|
||||
// MARK: - Streaming Properties
|
||||
@Published var currentAgent: String = ""
|
||||
@Published var statusMessage: String = ""
|
||||
@Published var analysisProgress: Double = 0.0
|
||||
@Published var reports: [String: String] = [:]
|
||||
@Published var finalDecision: String = ""
|
||||
|
||||
// MARK: - Services
|
||||
private let tradingService = TradingAgentsService()
|
||||
private var cancellables = Set<AnyCancellable>()
|
||||
|
||||
// MARK: - Constants
|
||||
private let agentSteps = [
|
||||
"Starting", "Market Analyst", "Social Media Analyst",
|
||||
"News Analyst", "Fundamentals Analyst", "Bull Researcher",
|
||||
"Bear Researcher", "Trading Team", "Complete"
|
||||
]
|
||||
|
||||
init() {
|
||||
setupSubscriptions()
|
||||
}
|
||||
|
||||
private func setupSubscriptions() {
|
||||
// Subscribe to service progress updates
|
||||
tradingService.$progress
|
||||
.receive(on: DispatchQueue.main)
|
||||
.sink { [weak self] progress in
|
||||
self?.updateProgress(progress)
|
||||
}
|
||||
.store(in: &cancellables)
|
||||
}
|
||||
|
||||
private func updateProgress(_ progress: AnalysisProgress) {
|
||||
currentAgent = progress.currentAgent
|
||||
statusMessage = progress.message
|
||||
reports = progress.reports
|
||||
|
||||
// Update progress percentage based on current agent
|
||||
if let stepIndex = agentSteps.firstIndex(of: progress.currentAgent) {
|
||||
analysisProgress = Double(stepIndex) / Double(agentSteps.count - 1)
|
||||
}
|
||||
|
||||
// Handle completion
|
||||
if progress.isComplete {
|
||||
isAnalyzing = false
|
||||
if progress.error == nil {
|
||||
showingResults = true
|
||||
finalDecision = reports["final_trade_decision"] ?? ""
|
||||
} else {
|
||||
errorMessage = progress.error
|
||||
}
|
||||
}
|
||||
|
||||
// Handle errors
|
||||
if let error = progress.error {
|
||||
errorMessage = error
|
||||
isAnalyzing = false
|
||||
}
|
||||
}
|
||||
|
||||
func startAnalysis() {
|
||||
guard !ticker.isEmpty else {
|
||||
errorMessage = "Please enter a ticker symbol"
|
||||
return
|
||||
}
|
||||
|
||||
// Reset state
|
||||
isAnalyzing = true
|
||||
showingResults = false
|
||||
errorMessage = nil
|
||||
currentAgent = ""
|
||||
statusMessage = ""
|
||||
analysisProgress = 0.0
|
||||
reports = [:]
|
||||
finalDecision = ""
|
||||
|
||||
// Start streaming analysis
|
||||
tradingService.streamAnalysis(for: ticker.uppercased())
|
||||
.receive(on: DispatchQueue.main)
|
||||
.sink { [weak self] progress in
|
||||
self?.updateProgress(progress)
|
||||
}
|
||||
.store(in: &cancellables)
|
||||
}
|
||||
|
||||
func stopAnalysis() {
|
||||
tradingService.stopAnalysis()
|
||||
isAnalyzing = false
|
||||
currentAgent = ""
|
||||
statusMessage = "Analysis stopped"
|
||||
}
|
||||
|
||||
func resetAnalysis() {
|
||||
stopAnalysis()
|
||||
showingResults = false
|
||||
errorMessage = nil
|
||||
ticker = ""
|
||||
currentAgent = ""
|
||||
statusMessage = ""
|
||||
analysisProgress = 0.0
|
||||
reports = [:]
|
||||
finalDecision = ""
|
||||
}
|
||||
|
||||
// MARK: - Computed Properties
|
||||
var formattedReports: [(title: String, content: String)] {
|
||||
let reportOrder = [
|
||||
("market_report", "Market Analysis"),
|
||||
("sentiment_report", "Sentiment Analysis"),
|
||||
("news_report", "News Analysis"),
|
||||
("fundamentals_report", "Fundamentals Analysis"),
|
||||
("investment_plan", "Investment Plan"),
|
||||
("trader_investment_plan", "Trading Plan")
|
||||
]
|
||||
|
||||
return reportOrder.compactMap { key, title in
|
||||
guard let content = reports[key], !content.isEmpty else { return nil }
|
||||
return (title: title, content: content)
|
||||
}
|
||||
}
|
||||
|
||||
var hasReports: Bool {
|
||||
!reports.isEmpty
|
||||
}
|
||||
|
||||
var progressPercentage: Int {
|
||||
Int(analysisProgress * 100)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,139 @@
|
|||
import SwiftUI
|
||||
|
||||
// MARK: - Analysis Result View
|
||||
struct AnalysisResultView: View {
|
||||
let ticker: String
|
||||
let reports: [(title: String, content: String)]
|
||||
let finalDecision: String
|
||||
let onDismiss: () -> Void
|
||||
|
||||
var body: some View {
|
||||
NavigationView {
|
||||
ScrollView {
|
||||
VStack(alignment: .leading, spacing: 20) {
|
||||
// Header
|
||||
HeaderView(ticker: ticker)
|
||||
.padding(.horizontal)
|
||||
|
||||
// Reports
|
||||
VStack(spacing: 16) {
|
||||
ForEach(reports, id: \.title) { report in
|
||||
ReportCard(
|
||||
title: report.title,
|
||||
icon: iconForReport(report.title),
|
||||
content: report.content
|
||||
)
|
||||
}
|
||||
|
||||
// Final Decision
|
||||
if !finalDecision.isEmpty {
|
||||
ReportCard(
|
||||
title: "Final Decision",
|
||||
icon: "checkmark.seal.fill",
|
||||
content: finalDecision,
|
||||
isHighlighted: true
|
||||
)
|
||||
}
|
||||
}
|
||||
.padding(.horizontal)
|
||||
}
|
||||
.padding(.vertical)
|
||||
}
|
||||
.navigationTitle("Analysis Results")
|
||||
.toolbar {
|
||||
ToolbarItem(placement: .primaryAction) {
|
||||
Button("Done") {
|
||||
onDismiss()
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func iconForReport(_ title: String) -> String {
|
||||
switch title {
|
||||
case "Market Analysis": return "chart.line.uptrend.xyaxis"
|
||||
case "Sentiment Analysis": return "bubble.left.and.bubble.right"
|
||||
case "News Analysis": return "newspaper"
|
||||
case "Fundamentals Analysis": return "doc.text"
|
||||
case "Investment Plan": return "lightbulb"
|
||||
case "Trading Plan": return "chart.bar.fill"
|
||||
default: return "doc.text"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Header View
|
||||
struct HeaderView: View {
|
||||
let ticker: String
|
||||
|
||||
var body: some View {
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
HStack(alignment: .top) {
|
||||
VStack(alignment: .leading, spacing: 4) {
|
||||
Text(ticker)
|
||||
.font(.largeTitle)
|
||||
.fontWeight(.bold)
|
||||
|
||||
Text("Analysis Date: \(DateFormatter.shortDate.string(from: Date()))")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
|
||||
Spacer()
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Report Card
|
||||
struct ReportCard: View {
|
||||
let title: String
|
||||
let icon: String
|
||||
let content: String
|
||||
var isHighlighted: Bool = false
|
||||
|
||||
@State private var isExpanded: Bool = false
|
||||
|
||||
var body: some View {
|
||||
VStack(alignment: .leading, spacing: 12) {
|
||||
Button(action: { withAnimation { isExpanded.toggle() } }) {
|
||||
HStack {
|
||||
Image(systemName: icon)
|
||||
.font(.title3)
|
||||
.foregroundStyle(isHighlighted ? .white : .blue)
|
||||
|
||||
Text(title)
|
||||
.font(.headline)
|
||||
.foregroundStyle(isHighlighted ? .white : .primary)
|
||||
|
||||
Spacer()
|
||||
|
||||
Image(systemName: "chevron.right")
|
||||
.rotationEffect(.degrees(isExpanded ? 90 : 0))
|
||||
.foregroundStyle(isHighlighted ? .white : .secondary)
|
||||
}
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
|
||||
if isExpanded {
|
||||
Text(content)
|
||||
.font(.body)
|
||||
.foregroundStyle(isHighlighted ? .white : .primary)
|
||||
.fixedSize(horizontal: false, vertical: true)
|
||||
}
|
||||
}
|
||||
.padding()
|
||||
.background(isHighlighted ? Color.blue : Color.gray.opacity(0.1))
|
||||
.cornerRadius(12)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Extensions
|
||||
extension DateFormatter {
|
||||
static let shortDate: DateFormatter = {
|
||||
let formatter = DateFormatter()
|
||||
formatter.dateStyle = .short
|
||||
return formatter
|
||||
}()
|
||||
}
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
import SwiftUI
|
||||
|
||||
// MARK: - Loading View
|
||||
struct LoadingView: View {
|
||||
let ticker: String
|
||||
|
||||
var body: some View {
|
||||
VStack(spacing: 20) {
|
||||
ProgressView()
|
||||
.scaleEffect(1.5)
|
||||
|
||||
Text("Analyzing \(ticker)...")
|
||||
.font(.headline)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Welcome View
|
||||
struct WelcomeView: View {
|
||||
var body: some View {
|
||||
VStack(spacing: 20) {
|
||||
Image(systemName: "chart.line.uptrend.xyaxis")
|
||||
.font(.system(size: 60))
|
||||
.foregroundStyle(.blue)
|
||||
|
||||
Text("Trading Agents Analysis")
|
||||
.font(.title)
|
||||
.fontWeight(.bold)
|
||||
|
||||
Text("Enter a stock ticker to get AI-powered trading analysis")
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(.secondary)
|
||||
.multilineTextAlignment(.center)
|
||||
.padding(.horizontal)
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Error View
|
||||
struct ErrorView: View {
|
||||
let error: String
|
||||
|
||||
var body: some View {
|
||||
VStack(spacing: 15) {
|
||||
Image(systemName: "exclamationmark.triangle.fill")
|
||||
.font(.system(size: 50))
|
||||
.foregroundStyle(.red)
|
||||
|
||||
Text("Error")
|
||||
.font(.title2)
|
||||
.fontWeight(.bold)
|
||||
|
||||
Text(error)
|
||||
.font(.body)
|
||||
.foregroundStyle(.secondary)
|
||||
.multilineTextAlignment(.center)
|
||||
.padding(.horizontal)
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,203 @@
|
|||
import SwiftUI
|
||||
|
||||
struct TradingAnalysisView: View {
|
||||
@StateObject private var viewModel = TradingAnalysisViewModel()
|
||||
|
||||
var body: some View {
|
||||
NavigationView {
|
||||
VStack(spacing: 20) {
|
||||
// Header
|
||||
headerSection
|
||||
|
||||
// Input Section
|
||||
inputSection
|
||||
|
||||
// Progress Section (shown during analysis)
|
||||
if viewModel.isAnalyzing {
|
||||
progressSection
|
||||
}
|
||||
|
||||
// Reports Section (shown as reports come in)
|
||||
if viewModel.hasReports && !viewModel.showingResults {
|
||||
reportsSection
|
||||
}
|
||||
|
||||
Spacer()
|
||||
}
|
||||
.padding()
|
||||
.navigationTitle("Trading Analysis")
|
||||
.alert("Error", isPresented: .constant(viewModel.errorMessage != nil)) {
|
||||
Button("OK") {
|
||||
viewModel.errorMessage = nil
|
||||
}
|
||||
} message: {
|
||||
Text(viewModel.errorMessage ?? "")
|
||||
}
|
||||
.sheet(isPresented: $viewModel.showingResults) {
|
||||
AnalysisResultView(
|
||||
ticker: viewModel.ticker,
|
||||
reports: viewModel.formattedReports,
|
||||
finalDecision: viewModel.finalDecision,
|
||||
onDismiss: {
|
||||
viewModel.resetAnalysis()
|
||||
}
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - View Components
|
||||
|
||||
private var headerSection: some View {
|
||||
VStack(spacing: 8) {
|
||||
Text("TradingAgents")
|
||||
.font(.largeTitle)
|
||||
.fontWeight(.bold)
|
||||
.foregroundColor(.primary)
|
||||
|
||||
Text("AI-Powered Stock Analysis")
|
||||
.font(.subheadline)
|
||||
.foregroundColor(.secondary)
|
||||
}
|
||||
}
|
||||
|
||||
private var inputSection: some View {
|
||||
VStack(spacing: 16) {
|
||||
HStack {
|
||||
TextField("Enter ticker symbol (e.g., AAPL)", text: $viewModel.ticker)
|
||||
.textFieldStyle(RoundedBorderTextFieldStyle())
|
||||
.autocapitalization(.allCharacters)
|
||||
.autocorrectionDisabled(true)
|
||||
.disabled(viewModel.isAnalyzing)
|
||||
|
||||
if viewModel.isAnalyzing {
|
||||
Button("Stop") {
|
||||
viewModel.stopAnalysis()
|
||||
}
|
||||
.foregroundColor(.red)
|
||||
} else {
|
||||
Button("Analyze") {
|
||||
viewModel.startAnalysis()
|
||||
}
|
||||
.disabled(viewModel.ticker.isEmpty)
|
||||
}
|
||||
}
|
||||
|
||||
if !viewModel.ticker.isEmpty && !viewModel.isAnalyzing {
|
||||
Text("Tap 'Analyze' to start real-time analysis")
|
||||
.font(.footnote)
|
||||
.foregroundColor(.secondary)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private var progressSection: some View {
|
||||
VStack(spacing: 16) {
|
||||
// Progress Bar
|
||||
VStack(spacing: 8) {
|
||||
HStack {
|
||||
Text("Analysis Progress")
|
||||
.font(.headline)
|
||||
Spacer()
|
||||
Text("\(viewModel.progressPercentage)%")
|
||||
.font(.caption)
|
||||
.foregroundColor(.secondary)
|
||||
}
|
||||
|
||||
ProgressView(value: viewModel.analysisProgress)
|
||||
.progressViewStyle(LinearProgressViewStyle(tint: .blue))
|
||||
}
|
||||
|
||||
// Current Agent Status
|
||||
if !viewModel.currentAgent.isEmpty {
|
||||
HStack {
|
||||
Image(systemName: "brain.head.profile")
|
||||
.foregroundColor(.blue)
|
||||
VStack(alignment: .leading) {
|
||||
Text(viewModel.currentAgent)
|
||||
.font(.subheadline)
|
||||
.fontWeight(.medium)
|
||||
if !viewModel.statusMessage.isEmpty {
|
||||
Text(viewModel.statusMessage)
|
||||
.font(.caption)
|
||||
.foregroundColor(.secondary)
|
||||
}
|
||||
}
|
||||
Spacer()
|
||||
}
|
||||
.padding()
|
||||
.background(Color.gray.opacity(0.1))
|
||||
.cornerRadius(8)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private var reportsSection: some View {
|
||||
VStack(spacing: 12) {
|
||||
HStack {
|
||||
Text("Live Reports")
|
||||
.font(.headline)
|
||||
Spacer()
|
||||
Text("\(viewModel.formattedReports.count) sections")
|
||||
.font(.caption)
|
||||
.foregroundColor(.secondary)
|
||||
}
|
||||
|
||||
ScrollView {
|
||||
LazyVStack(spacing: 8) {
|
||||
ForEach(viewModel.formattedReports, id: \.title) { report in
|
||||
ReportCardView(title: report.title, content: report.content)
|
||||
}
|
||||
}
|
||||
}
|
||||
.frame(maxHeight: 300)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Supporting Views
|
||||
|
||||
struct ReportCardView: View {
|
||||
let title: String
|
||||
let content: String
|
||||
@State private var isExpanded = false
|
||||
|
||||
var body: some View {
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
HStack {
|
||||
Text(title)
|
||||
.font(.subheadline)
|
||||
.fontWeight(.medium)
|
||||
Spacer()
|
||||
Image(systemName: isExpanded ? "chevron.up" : "chevron.down")
|
||||
.font(.caption)
|
||||
.foregroundColor(.secondary)
|
||||
}
|
||||
.onTapGesture {
|
||||
withAnimation(.easeInOut(duration: 0.2)) {
|
||||
isExpanded.toggle()
|
||||
}
|
||||
}
|
||||
|
||||
if isExpanded {
|
||||
Text(content)
|
||||
.font(.caption)
|
||||
.foregroundColor(.secondary)
|
||||
.lineLimit(nil)
|
||||
.transition(.opacity.combined(with: .slide))
|
||||
} else {
|
||||
Text(content)
|
||||
.font(.caption)
|
||||
.foregroundColor(.secondary)
|
||||
.lineLimit(2)
|
||||
}
|
||||
}
|
||||
.padding()
|
||||
.background(Color(.systemGray6))
|
||||
.cornerRadius(8)
|
||||
}
|
||||
}
|
||||
|
||||
#Preview {
|
||||
TradingAnalysisView()
|
||||
}
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
//
|
||||
// TradingDummyTests.swift
|
||||
// TradingDummyTests
|
||||
//
|
||||
// Created by ByteDance on 6/28/25.
|
||||
//
|
||||
|
||||
import Testing
|
||||
|
||||
struct TradingDummyTests {
|
||||
|
||||
@Test func example() async throws {
|
||||
// Write your test here and use APIs like `#expect(...)` to check expected conditions.
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
//
|
||||
// TradingDummyUITests.swift
|
||||
// TradingDummyUITests
|
||||
//
|
||||
// Created by ByteDance on 6/28/25.
|
||||
//
|
||||
|
||||
import XCTest
|
||||
|
||||
final class TradingDummyUITests: XCTestCase {
|
||||
|
||||
override func setUpWithError() throws {
|
||||
// Put setup code here. This method is called before the invocation of each test method in the class.
|
||||
|
||||
// In UI tests it is usually best to stop immediately when a failure occurs.
|
||||
continueAfterFailure = false
|
||||
|
||||
// In UI tests it’s important to set the initial state - such as interface orientation - required for your tests before they run. The setUp method is a good place to do this.
|
||||
}
|
||||
|
||||
override func tearDownWithError() throws {
|
||||
// Put teardown code here. This method is called after the invocation of each test method in the class.
|
||||
}
|
||||
|
||||
@MainActor
|
||||
func testExample() throws {
|
||||
// UI tests must launch the application that they test.
|
||||
let app = XCUIApplication()
|
||||
app.launch()
|
||||
|
||||
// Use XCTAssert and related functions to verify your tests produce the correct results.
|
||||
}
|
||||
|
||||
@MainActor
|
||||
func testLaunchPerformance() throws {
|
||||
if #available(macOS 10.15, iOS 13.0, tvOS 13.0, watchOS 7.0, *) {
|
||||
// This measures how long it takes to launch your application.
|
||||
measure(metrics: [XCTApplicationLaunchMetric()]) {
|
||||
XCUIApplication().launch()
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
//
|
||||
// TradingDummyUITestsLaunchTests.swift
|
||||
// TradingDummyUITests
|
||||
//
|
||||
// Created by ByteDance on 6/28/25.
|
||||
//
|
||||
|
||||
import XCTest
|
||||
|
||||
final class TradingDummyUITestsLaunchTests: XCTestCase {
|
||||
|
||||
override class var runsForEachTargetApplicationUIConfiguration: Bool {
|
||||
true
|
||||
}
|
||||
|
||||
override func setUpWithError() throws {
|
||||
continueAfterFailure = false
|
||||
}
|
||||
|
||||
@MainActor
|
||||
func testLaunch() throws {
|
||||
let app = XCUIApplication()
|
||||
app.launch()
|
||||
|
||||
// Insert steps here to perform after app launch but before taking a screenshot,
|
||||
// such as logging into a test account or navigating somewhere in the app
|
||||
|
||||
let attachment = XCTAttachment(screenshot: app.screenshot())
|
||||
attachment.name = "Launch Screen"
|
||||
attachment.lifetime = .keepAlways
|
||||
add(attachment)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,143 @@
|
|||
# Trading Agents Backend
|
||||
|
||||
This directory contains the Python backend for the Trading Agents system.
|
||||
|
||||
## Structure
|
||||
|
||||
- **`api.py`** - FastAPI server that exposes trading analysis endpoints
|
||||
- **`run_api.py`** - Script to run the FastAPI server
|
||||
- **`main.py`** - Main entry point for the trading system
|
||||
- **`tradingagents/`** - Core trading logic and agents
|
||||
- `agents/` - Various AI agents (analysts, researchers, traders, risk managers)
|
||||
- `dataflows/` - Data fetching and processing utilities
|
||||
- `graph/` - Trading decision graph and workflow
|
||||
- **`cli/`** - Command-line interface for the trading system
|
||||
- **`results/`** - Output directory for analysis results
|
||||
- **`requirements.txt`** - Python dependencies
|
||||
- **`setup.py`** - Package setup configuration
|
||||
- **`pyproject.toml`** - Modern Python project configuration
|
||||
|
||||
## Quick Start
|
||||
|
||||
1. Install dependencies:
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
2. Run the FastAPI server:
|
||||
```bash
|
||||
python run_api.py
|
||||
```
|
||||
|
||||
3. Or use the CLI:
|
||||
```bash
|
||||
python -m cli.main
|
||||
```
|
||||
|
||||
## API Endpoints
|
||||
|
||||
- `GET /` - Root endpoint (API status)
|
||||
- `GET /health` - Health check
|
||||
- `POST /analyze` - Analyze a stock ticker
|
||||
- Request: `{"ticker": "AAPL"}`
|
||||
- Response: Comprehensive analysis including market, sentiment, news, and trading decisions
|
||||
- `GET /docs` - Interactive API documentation (Swagger UI)
|
||||
|
||||
## Testing the API
|
||||
|
||||
### Quick Test
|
||||
```bash
|
||||
# Run the test script
|
||||
python test_api.py
|
||||
```
|
||||
|
||||
### Manual Testing
|
||||
1. **Using Browser**: Navigate to http://localhost:8000/docs for interactive testing
|
||||
2. **Using curl**: See `test_api.md` for detailed curl commands
|
||||
3. **Using Python**: Use the provided `test_api.py` script
|
||||
|
||||
For detailed testing instructions, see [test_api.md](test_api.md).
|
||||
|
||||
## Environment Variables
|
||||
|
||||
Create a `.env` file in the project root with:
|
||||
|
||||
### Required
|
||||
- `OPENAI_API_KEY` - Your OpenAI API key for AI agents
|
||||
- `FINNHUB_API_KEY` - For market data (free tier available)
|
||||
|
||||
### Optional
|
||||
- `REDDIT_CLIENT_ID` - For Reddit sentiment analysis
|
||||
- `REDDIT_CLIENT_SECRET` - For Reddit sentiment analysis
|
||||
- `TRADINGAGENTS_RESULTS_DIR` - Custom results directory (default: `backend/results`)
|
||||
- `TRADINGAGENTS_DATA_DIR` - Custom data directory (default: `backend/data`)
|
||||
- `TRADINGAGENTS_API_HOST` - API host (default: `localhost`)
|
||||
- `TRADINGAGENTS_API_PORT` - API port (default: `8000`)
|
||||
|
||||
### Example `.env` file:
|
||||
```bash
|
||||
# API Keys
|
||||
OPENAI_API_KEY=sk-your-key-here
|
||||
FINNHUB_API_KEY=your-finnhub-key
|
||||
|
||||
# Optional Reddit API
|
||||
REDDIT_CLIENT_ID=your-client-id
|
||||
REDDIT_CLIENT_SECRET=your-client-secret
|
||||
|
||||
# Optional custom directories
|
||||
# TRADINGAGENTS_RESULTS_DIR=/custom/path/to/results
|
||||
# TRADINGAGENTS_API_PORT=3000
|
||||
```
|
||||
|
||||
## Features
|
||||
|
||||
### Streaming Analysis (NEW!)
|
||||
|
||||
The TradingAgents CLI now supports real-time streaming of analysis reports as they're being generated by the AI agents. Instead of waiting for complete sections to be delivered, you can see the analysis content flowing in real-time.
|
||||
|
||||
#### Usage Options
|
||||
|
||||
1. **Dedicated Streaming Command:**
|
||||
```bash
|
||||
python -m cli.main stream
|
||||
python -m cli.main stream --advanced # With advanced configuration
|
||||
```
|
||||
|
||||
2. **Streaming Flag with Analyze Command:**
|
||||
```bash
|
||||
python -m cli.main analyze --streaming
|
||||
python -m cli.main analyze --streaming --advanced
|
||||
```
|
||||
|
||||
3. **Default Command with Streaming:**
|
||||
```bash
|
||||
python -m cli.main --streaming
|
||||
python -m cli.main --streaming --advanced
|
||||
```
|
||||
|
||||
#### Streaming vs. Regular Analysis
|
||||
|
||||
- **Regular Analysis**: Shows progress updates and agent statuses, but reports are delivered only when complete sections are finished
|
||||
- **Streaming Analysis**: Shows the same progress updates PLUS real-time streaming of report content as agents generate it
|
||||
|
||||
#### What You'll See in Streaming Mode
|
||||
|
||||
- 🔄 **Agent Progress**: Same as regular mode - shows which agents are working
|
||||
- 📡 **Live Streaming Panel**: Real-time content from the currently active agent
|
||||
- 🔴 **Live Indicator**: Shows which agent is currently generating content
|
||||
- ⚡ **Higher Refresh Rate**: Updates 8 times per second for smooth streaming
|
||||
|
||||
#### Benefits
|
||||
|
||||
- **Real-time Insights**: See analysis developing as it happens
|
||||
- **Better Understanding**: Watch the thought process of each agent
|
||||
- **Immediate Feedback**: Know immediately when agents start working on different aspects
|
||||
- **Enhanced Experience**: More engaging and informative than batch delivery
|
||||
|
||||
#### Technical Details
|
||||
|
||||
The streaming implementation:
|
||||
- Uses a `StreamingMessageBuffer` that extends the regular `MessageBuffer`
|
||||
- Detects agent transitions and content generation in real-time
|
||||
- Maintains the same final report quality while providing streaming experience
|
||||
- Automatically saves all content to report files as in regular mode
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
# TradingAgents Streaming Analysis - Usage Examples
|
||||
|
||||
## Quick Start
|
||||
|
||||
### 1. Basic Streaming Analysis
|
||||
```bash
|
||||
# Run streaming analysis with default settings
|
||||
python -m cli.main stream
|
||||
|
||||
# Or use the streaming flag with analyze command
|
||||
python -m cli.main analyze --streaming
|
||||
```
|
||||
|
||||
### 2. Advanced Streaming Analysis
|
||||
```bash
|
||||
# Run streaming analysis with advanced configuration
|
||||
python -m cli.main stream --advanced
|
||||
|
||||
# Or combine flags
|
||||
python -m cli.main analyze --streaming --advanced
|
||||
```
|
||||
|
||||
### 3. Default Command with Streaming
|
||||
```bash
|
||||
# Use streaming as default behavior
|
||||
python -m cli.main --streaming
|
||||
```
|
||||
|
||||
## What to Expect
|
||||
|
||||
### Regular Analysis Output
|
||||
```
|
||||
data: {"type": "status", "message": "Starting analysis for TEM..."}
|
||||
data: {"type": "agent_status", "agent": "Market Analyst"}
|
||||
data: {"type": "progress", "percentage": 16}
|
||||
...
|
||||
[Wait for completion, then get all reports at once]
|
||||
```
|
||||
|
||||
### Streaming Analysis Output
|
||||
```
|
||||
data: {"type": "status", "message": "Starting analysis for TEM..."}
|
||||
data: {"type": "agent_status", "agent": "Market Analyst"}
|
||||
data: {"type": "progress", "percentage": 16}
|
||||
|
||||
🔴 Live: Market Analyst
|
||||
Technical Analysis for TEM:
|
||||
Based on the recent price action and volume patterns...
|
||||
[Content streams in real-time as the agent thinks and writes]
|
||||
|
||||
Moving to fundamental analysis...
|
||||
The company's recent earnings report shows...
|
||||
[More content streaming live]
|
||||
|
||||
🔴 Live: Social Media Analyst
|
||||
Social sentiment analysis reveals...
|
||||
[Content continues streaming from next agent]
|
||||
```
|
||||
|
||||
## UI Layout in Streaming Mode
|
||||
|
||||
```
|
||||
┌─ Welcome to TradingAgents ─────────────────┐
|
||||
│ Welcome to TradingAgents CLI │
|
||||
│ © Tauric Research │
|
||||
└────────────────────────────────────────────┘
|
||||
|
||||
┌─ Agent Progress ──┬─ Recent Messages ──────┐
|
||||
│ Market Analyst ✅ │ 14:30:22 [System] TEM │
|
||||
│ Social Analyst 🔄 │ 14:30:25 [Reasoning] │
|
||||
│ News Analyst ⏳ │ 14:30:28 [Tool Call] │
|
||||
└───────────────────┴────────────────────────┘
|
||||
|
||||
┌─ 🔴 Live: Social Media Analyst ────────────┐
|
||||
│ Analyzing social media sentiment for TEM: │
|
||||
│ │
|
||||
│ Recent Twitter mentions show mixed │
|
||||
│ sentiment with 60% positive mentions... │
|
||||
│ [Content streaming in real-time] │
|
||||
└────────────────────────────────────────────┘
|
||||
|
||||
┌─ Latest Report Section ────────────────────┐
|
||||
│ ### Market Analysis │
|
||||
│ Technical indicators suggest... │
|
||||
│ [Shows most recently completed section] │
|
||||
└────────────────────────────────────────────┘
|
||||
|
||||
┌─ TradingAgents Streaming Analysis ─────────┐
|
||||
│ Press Ctrl+C to stop │
|
||||
└────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Advanced Usage Scenarios
|
||||
|
||||
### 1. Development and Debugging
|
||||
When developing or debugging agent behaviors, streaming allows you to:
|
||||
- See exactly where agents get stuck
|
||||
- Monitor real-time thought processes
|
||||
- Identify performance bottlenecks
|
||||
- Watch agent decision-making flow
|
||||
|
||||
### 2. Educational Use
|
||||
For learning how the system works:
|
||||
- Observe AI reasoning in real-time
|
||||
- Understand multi-agent collaboration
|
||||
- See how different analysis types build on each other
|
||||
- Learn from the agents' analytical approaches
|
||||
|
||||
### 3. Production Monitoring
|
||||
In production environments:
|
||||
- Monitor system health in real-time
|
||||
- Detect anomalies early
|
||||
- Provide better user experience with live updates
|
||||
- Enable real-time decision making
|
||||
|
||||
## Comparison: Regular vs Streaming
|
||||
|
||||
| Aspect | Regular Analysis | Streaming Analysis |
|
||||
|--------|------------------|-------------------|
|
||||
| **Content Delivery** | Batch (sections at once) | Real-time (as generated) |
|
||||
| **User Experience** | Wait then receive | Continuous feedback |
|
||||
| **Debugging** | Post-mortem only | Live debugging |
|
||||
| **Monitoring** | End result only | Process visibility |
|
||||
| **Engagement** | Passive waiting | Active observation |
|
||||
| **Resource Usage** | Lower CPU (4 FPS) | Higher CPU (8 FPS) |
|
||||
|
||||
## Technical Architecture
|
||||
|
||||
### StreamingMessageBuffer
|
||||
Extends the regular MessageBuffer with:
|
||||
- Real-time content streaming capabilities
|
||||
- Agent transition detection
|
||||
- Content buffering and delivery
|
||||
- Callback system for live updates
|
||||
|
||||
### Layout Differences
|
||||
- Additional "streaming_content" panel for live updates
|
||||
- Higher refresh rate (8 FPS vs 4 FPS)
|
||||
- Enhanced agent status indicators
|
||||
- Real-time content formatting
|
||||
|
||||
### Agent Detection
|
||||
The system detects agent transitions by:
|
||||
- Analyzing message content for agent keywords
|
||||
- Monitoring state changes in the graph
|
||||
- Tracking section completions
|
||||
- Managing content buffers per agent
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. When to Use Streaming
|
||||
- **Development**: Always use streaming for development
|
||||
- **Production**: Use for high-engagement scenarios
|
||||
- **Debugging**: Essential for troubleshooting
|
||||
- **Demos**: Great for showing system capabilities
|
||||
|
||||
### 2. When to Use Regular Analysis
|
||||
- **Batch Processing**: When processing many symbols
|
||||
- **Resource Constrained**: On slower systems
|
||||
- **Automated Systems**: When only final results matter
|
||||
- **Background Processing**: For scheduled runs
|
||||
|
||||
### 3. Performance Considerations
|
||||
- Streaming uses more CPU due to higher refresh rate
|
||||
- Network usage is similar (same data, different timing)
|
||||
- Memory usage slightly higher due to buffering
|
||||
- Terminal performance may vary based on content length
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
1. **Slow Terminal**: Reduce content display or use regular mode
|
||||
2. **Missing Content**: Check agent keyword detection
|
||||
3. **Jumbled Output**: Ensure terminal supports rich formatting
|
||||
4. **Performance**: Lower refresh rate or use regular analysis
|
||||
|
||||
### Debug Mode
|
||||
```bash
|
||||
# Enable debug output
|
||||
export DEBUG=1
|
||||
python -m cli.main stream --advanced
|
||||
```
|
||||
|
||||
This will show additional information about:
|
||||
- Agent detection logic
|
||||
- Content buffering
|
||||
- State transitions
|
||||
- Performance metrics
|
||||
|
|
@ -0,0 +1,473 @@
|
|||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.responses import StreamingResponse
|
||||
from pydantic import BaseModel
|
||||
from typing import Dict, Any, Optional
|
||||
import datetime
|
||||
import os
|
||||
import json
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Import trading agents
|
||||
from tradingagents.graph.trading_graph import TradingAgentsGraph
|
||||
from tradingagents.default_config import DEFAULT_CONFIG
|
||||
|
||||
# Create FastAPI app
|
||||
app = FastAPI(
|
||||
title="TradingAgents API",
|
||||
description="API for TradingAgents financial analysis",
|
||||
version="1.0.0"
|
||||
)
|
||||
|
||||
# Add CORS middleware for Swift app
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"], # In production, replace with your Swift app's URL
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
# Request model
|
||||
class AnalysisRequest(BaseModel):
|
||||
ticker: str
|
||||
|
||||
# Response model
|
||||
class AnalysisResponse(BaseModel):
|
||||
ticker: str
|
||||
analysis_date: str
|
||||
market_report: Optional[str] = None
|
||||
sentiment_report: Optional[str] = None
|
||||
news_report: Optional[str] = None
|
||||
fundamentals_report: Optional[str] = None
|
||||
investment_plan: Optional[str] = None
|
||||
trader_investment_plan: Optional[str] = None
|
||||
final_trade_decision: Optional[str] = None
|
||||
processed_signal: Optional[str] = None
|
||||
error: Optional[str] = None
|
||||
|
||||
# Simple configuration
|
||||
def get_config():
|
||||
config = DEFAULT_CONFIG.copy()
|
||||
config.update({
|
||||
"llm_provider": "openai",
|
||||
"deep_think_llm": os.getenv("DEEP_THINK_MODEL", "o3"),
|
||||
"quick_think_llm": os.getenv("QUICK_THINK_MODEL", "gpt-4o"),
|
||||
"backend_url": os.getenv("BACKEND_URL", "https://api.openai.com/v1"),
|
||||
"max_debate_rounds": 5,
|
||||
"max_risk_discuss_rounds": 3,
|
||||
"online_tools": True,
|
||||
})
|
||||
return config
|
||||
|
||||
# Shared OpenAI client factory (reused by interface.py tools)
|
||||
_shared_openai_client = None
|
||||
|
||||
def get_shared_openai_client():
|
||||
"""Get a shared OpenAI client with proper configuration"""
|
||||
global _shared_openai_client
|
||||
if _shared_openai_client is None:
|
||||
config = get_config()
|
||||
from openai import OpenAI
|
||||
_shared_openai_client = OpenAI(base_url=config["backend_url"])
|
||||
return _shared_openai_client
|
||||
|
||||
def get_compatible_model_for_tools():
|
||||
"""Get a model that's compatible with web_search_preview tools"""
|
||||
config = get_config()
|
||||
model = config["quick_think_llm"]
|
||||
|
||||
# Models that don't support web_search_preview
|
||||
incompatible_models = ["gpt-4.1-nano", "gpt-4.1-mini"]
|
||||
|
||||
if model in incompatible_models:
|
||||
# Fallback to a compatible model
|
||||
fallback_model = "gpt-4o-mini"
|
||||
print(f"⚠️ Model {model} doesn't support web_search_preview. Using {fallback_model} for tools.")
|
||||
return fallback_model
|
||||
|
||||
return model
|
||||
|
||||
def save_results_to_disk(ticker: str, analysis_date: str, results: dict, config: dict):
|
||||
"""Save analysis results to disk like the CLI does"""
|
||||
results_dir = Path(config["results_dir"]) / ticker / analysis_date
|
||||
results_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save full results as JSON
|
||||
results_file = results_dir / "api_analysis_results.json"
|
||||
with open(results_file, 'w') as f:
|
||||
json.dump(results, f, indent=2)
|
||||
|
||||
# Save individual reports
|
||||
reports_dir = results_dir / "reports"
|
||||
reports_dir.mkdir(exist_ok=True)
|
||||
|
||||
# Save each report as a separate file
|
||||
report_types = [
|
||||
('market_report', 'market_analysis.txt'),
|
||||
('sentiment_report', 'sentiment_analysis.txt'),
|
||||
('news_report', 'news_analysis.txt'),
|
||||
('fundamentals_report', 'fundamentals_analysis.txt'),
|
||||
('investment_plan', 'investment_plan.txt'),
|
||||
('trader_investment_plan', 'trader_investment_plan.txt'),
|
||||
('final_trade_decision', 'final_trade_decision.txt'),
|
||||
('processed_signal', 'signal.txt')
|
||||
]
|
||||
|
||||
for key, filename in report_types:
|
||||
if results.get(key):
|
||||
report_file = reports_dir / filename
|
||||
with open(report_file, 'w') as f:
|
||||
f.write(str(results[key]))
|
||||
|
||||
return str(results_dir)
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
return {"message": "TradingAgents API is running"}
|
||||
|
||||
@app.post("/analyze", response_model=AnalysisResponse)
|
||||
async def analyze_ticker(request: AnalysisRequest):
|
||||
"""Analyze a stock ticker and return trading recommendations"""
|
||||
try:
|
||||
# Validate ticker
|
||||
ticker = request.ticker.strip().upper()
|
||||
if not ticker:
|
||||
raise HTTPException(status_code=400, detail="Ticker cannot be empty")
|
||||
|
||||
# Use current date
|
||||
analysis_date = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
# Initialize trading graph with all analysts
|
||||
config = get_config()
|
||||
graph = TradingAgentsGraph(
|
||||
selected_analysts=["market", "social", "news", "fundamentals"],
|
||||
debug=False,
|
||||
config=config
|
||||
)
|
||||
|
||||
# Run analysis
|
||||
final_state, processed_signal = graph.propagate(ticker, analysis_date)
|
||||
|
||||
# Prepare results
|
||||
results = {
|
||||
"ticker": ticker,
|
||||
"analysis_date": analysis_date,
|
||||
"market_report": final_state.get("market_report"),
|
||||
"sentiment_report": final_state.get("sentiment_report"),
|
||||
"news_report": final_state.get("news_report"),
|
||||
"fundamentals_report": final_state.get("fundamentals_report"),
|
||||
"investment_plan": final_state.get("investment_plan"),
|
||||
"trader_investment_plan": final_state.get("trader_investment_plan"),
|
||||
"final_trade_decision": final_state.get("final_trade_decision"),
|
||||
"processed_signal": processed_signal
|
||||
}
|
||||
|
||||
# Save results to disk
|
||||
saved_path = save_results_to_disk(ticker, analysis_date, results, config)
|
||||
print(f"✅ Results saved to: {saved_path}")
|
||||
|
||||
# Return API response
|
||||
return AnalysisResponse(**results)
|
||||
|
||||
except Exception as e:
|
||||
# Return error in response
|
||||
return AnalysisResponse(
|
||||
ticker=request.ticker,
|
||||
analysis_date=datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
# Health check endpoint
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
return {"status": "healthy"}
|
||||
|
||||
# Simple SSE test endpoint
|
||||
@app.get("/test-stream")
|
||||
async def test_stream():
|
||||
"""Simple SSE test endpoint"""
|
||||
def event_stream():
|
||||
import time
|
||||
for i in range(5):
|
||||
yield f"data: {json.dumps({'count': i, 'message': f'Test message {i}'})}\n\n"
|
||||
time.sleep(1)
|
||||
yield f"data: {json.dumps({'message': 'Test complete'})}\n\n"
|
||||
|
||||
return StreamingResponse(
|
||||
event_stream(),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Access-Control-Allow-Origin": "*"
|
||||
}
|
||||
)
|
||||
|
||||
@app.get("/analyze/stream")
|
||||
async def stream_analysis(ticker: str):
|
||||
"""Stream real-time analysis updates using SSE"""
|
||||
print(f"\n🚀 NEW STREAM REQUEST: ticker={ticker}")
|
||||
try:
|
||||
# Validate ticker
|
||||
ticker = ticker.strip().upper()
|
||||
if not ticker:
|
||||
print("❌ Empty ticker provided")
|
||||
raise HTTPException(status_code=400, detail="Ticker cannot be empty")
|
||||
|
||||
print(f"✅ Validated ticker: {ticker}")
|
||||
|
||||
# Use current date
|
||||
analysis_date = datetime.datetime.now().strftime("%Y-%m-%d")
|
||||
print(f"📅 Analysis date: {analysis_date}")
|
||||
|
||||
async def event_stream():
|
||||
try:
|
||||
print(f"📡 Starting event stream for {ticker}")
|
||||
|
||||
# Send initial status immediately
|
||||
initial_event = json.dumps({'type': 'status', 'message': f'Starting analysis for {ticker}...'})
|
||||
print(f"📤 Sending initial status: {initial_event}")
|
||||
yield f"data: {initial_event}\n\n"
|
||||
|
||||
# Initialize trading graph with all analysts
|
||||
print("🔧 Initializing trading graph...")
|
||||
config = get_config()
|
||||
print(f"📋 Config: {config}")
|
||||
|
||||
graph = TradingAgentsGraph(
|
||||
selected_analysts=["market", "social", "news", "fundamentals"],
|
||||
debug=True, # Enable debug mode
|
||||
config=config
|
||||
)
|
||||
print("✅ Trading graph initialized")
|
||||
|
||||
# Initialize state and get graph args
|
||||
print("🔧 Creating initial state...")
|
||||
init_agent_state = graph.propagator.create_initial_state(ticker, analysis_date)
|
||||
print(f"📊 Initial state keys: {list(init_agent_state.keys()) if init_agent_state else 'None'}")
|
||||
|
||||
args = graph.propagator.get_graph_args()
|
||||
print(f"🔧 Graph args: {args}")
|
||||
|
||||
# Track progress and reports
|
||||
agent_progress = {
|
||||
"Market Analyst": "pending",
|
||||
"Social Media Analyst": "pending",
|
||||
"News Analyst": "pending",
|
||||
"Fundamentals Analyst": "pending",
|
||||
"Bull Researcher": "pending",
|
||||
"Bear Researcher": "pending",
|
||||
"Research Manager": "pending",
|
||||
"Trading Team": "pending",
|
||||
"Portfolio Manager": "pending"
|
||||
}
|
||||
print(f"📊 Initial agent progress: {agent_progress}")
|
||||
|
||||
reports_completed = []
|
||||
trace = []
|
||||
chunk_count = 0
|
||||
|
||||
print("🔄 Starting real-time streaming using graph.graph.stream()...")
|
||||
|
||||
# Real-time streaming using graph.stream()
|
||||
for chunk in graph.graph.stream(init_agent_state, **args):
|
||||
chunk_count += 1
|
||||
print(f"\n📦 CHUNK {chunk_count}: {list(chunk.keys()) if chunk else 'Empty'}")
|
||||
trace.append(chunk)
|
||||
|
||||
# Allow async event loop to process
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
if len(chunk.get("messages", [])) > 0:
|
||||
print(f"💬 Processing {len(chunk['messages'])} messages")
|
||||
|
||||
# Process messages for agent detection
|
||||
last_message = chunk["messages"][-1]
|
||||
print(f"📨 Last message type: {type(last_message)}")
|
||||
|
||||
if hasattr(last_message, "content"):
|
||||
content = str(last_message.content) if hasattr(last_message.content, '__str__') else str(last_message.content)
|
||||
|
||||
# Extract text content if it's a list
|
||||
if isinstance(last_message.content, list):
|
||||
text_parts = []
|
||||
for part in last_message.content:
|
||||
if hasattr(part, 'text'):
|
||||
text_parts.append(part.text)
|
||||
elif isinstance(part, str):
|
||||
text_parts.append(part)
|
||||
else:
|
||||
text_parts.append(str(part))
|
||||
content = " ".join(text_parts)
|
||||
|
||||
# Send reasoning updates
|
||||
reasoning_event = json.dumps({'type': 'reasoning', 'content': content[:500]})
|
||||
print(f"📤 Sending reasoning: {reasoning_event[:100]}...")
|
||||
yield f"data: {reasoning_event}\n\n"
|
||||
|
||||
# Handle section completions and send progress updates
|
||||
if "market_report" in chunk and chunk["market_report"] and "market_report" not in reports_completed:
|
||||
print("✅ Market report completed!")
|
||||
agent_progress["Market Analyst"] = "completed"
|
||||
agent_progress["Social Media Analyst"] = "in_progress"
|
||||
reports_completed.append("market_report")
|
||||
|
||||
events = [
|
||||
json.dumps({'type': 'agent_status', 'agent': 'market', 'status': 'completed'}),
|
||||
json.dumps({'type': 'agent_status', 'agent': 'social', 'status': 'in_progress'}),
|
||||
json.dumps({'type': 'report', 'section': 'market_report', 'content': chunk['market_report']}),
|
||||
json.dumps({'type': 'progress', 'content': '25'})
|
||||
]
|
||||
|
||||
for event in events:
|
||||
print(f"📤 Sending: {event[:100]}...")
|
||||
yield f"data: {event}\n\n"
|
||||
|
||||
if "sentiment_report" in chunk and chunk["sentiment_report"] and "sentiment_report" not in reports_completed:
|
||||
print("✅ Sentiment report completed!")
|
||||
agent_progress["Social Media Analyst"] = "completed"
|
||||
agent_progress["News Analyst"] = "in_progress"
|
||||
reports_completed.append("sentiment_report")
|
||||
|
||||
events = [
|
||||
json.dumps({'type': 'agent_status', 'agent': 'social', 'status': 'completed'}),
|
||||
json.dumps({'type': 'agent_status', 'agent': 'news', 'status': 'in_progress'}),
|
||||
json.dumps({'type': 'report', 'section': 'sentiment_report', 'content': chunk['sentiment_report']}),
|
||||
json.dumps({'type': 'progress', 'content': '40'})
|
||||
]
|
||||
|
||||
for event in events:
|
||||
print(f"📤 Sending: {event[:100]}...")
|
||||
yield f"data: {event}\n\n"
|
||||
|
||||
if "news_report" in chunk and chunk["news_report"] and "news_report" not in reports_completed:
|
||||
print("✅ News report completed!")
|
||||
agent_progress["News Analyst"] = "completed"
|
||||
agent_progress["Fundamentals Analyst"] = "in_progress"
|
||||
reports_completed.append("news_report")
|
||||
|
||||
events = [
|
||||
json.dumps({'type': 'agent_status', 'agent': 'news', 'status': 'completed'}),
|
||||
json.dumps({'type': 'agent_status', 'agent': 'fundamentals', 'status': 'in_progress'}),
|
||||
json.dumps({'type': 'report', 'section': 'news_report', 'content': chunk['news_report']}),
|
||||
json.dumps({'type': 'progress', 'content': '55'})
|
||||
]
|
||||
|
||||
for event in events:
|
||||
print(f"📤 Sending: {event[:100]}...")
|
||||
yield f"data: {event}\n\n"
|
||||
|
||||
if "fundamentals_report" in chunk and chunk["fundamentals_report"] and "fundamentals_report" not in reports_completed:
|
||||
print("✅ Fundamentals report completed!")
|
||||
agent_progress["Fundamentals Analyst"] = "completed"
|
||||
agent_progress["Bull Researcher"] = "in_progress"
|
||||
agent_progress["Bear Researcher"] = "in_progress"
|
||||
reports_completed.append("fundamentals_report")
|
||||
|
||||
events = [
|
||||
json.dumps({'type': 'agent_status', 'agent': 'fundamentals', 'status': 'completed'}),
|
||||
json.dumps({'type': 'agent_status', 'agent': 'bull_researcher', 'status': 'in_progress'}),
|
||||
json.dumps({'type': 'agent_status', 'agent': 'bear_researcher', 'status': 'in_progress'}),
|
||||
json.dumps({'type': 'report', 'section': 'fundamentals_report', 'content': chunk['fundamentals_report']}),
|
||||
json.dumps({'type': 'progress', 'content': '70'})
|
||||
]
|
||||
|
||||
for event in events:
|
||||
print(f"📤 Sending: {event[:100]}...")
|
||||
yield f"data: {event}\n\n"
|
||||
|
||||
# Handle research team debates
|
||||
if "investment_debate_state" in chunk and chunk["investment_debate_state"]:
|
||||
print("🔄 Processing investment debate state...")
|
||||
debate_state = chunk["investment_debate_state"]
|
||||
|
||||
if "judge_decision" in debate_state and debate_state["judge_decision"] and "investment_plan" not in reports_completed:
|
||||
print("✅ Investment plan completed!")
|
||||
agent_progress["Bull Researcher"] = "completed"
|
||||
agent_progress["Bear Researcher"] = "completed"
|
||||
agent_progress["Research Manager"] = "completed"
|
||||
agent_progress["Trading Team"] = "in_progress"
|
||||
reports_completed.append("investment_plan")
|
||||
|
||||
events = [
|
||||
json.dumps({'type': 'agent_status', 'agent': 'bull_researcher', 'status': 'completed'}),
|
||||
json.dumps({'type': 'agent_status', 'agent': 'bear_researcher', 'status': 'completed'}),
|
||||
json.dumps({'type': 'agent_status', 'agent': 'trader', 'status': 'in_progress'}),
|
||||
json.dumps({'type': 'report', 'section': 'investment_plan', 'content': debate_state['judge_decision']}),
|
||||
json.dumps({'type': 'progress', 'content': '85'})
|
||||
]
|
||||
|
||||
for event in events:
|
||||
print(f"📤 Sending: {event[:100]}...")
|
||||
yield f"data: {event}\n\n"
|
||||
|
||||
# Handle trading team
|
||||
if "trader_investment_plan" in chunk and chunk["trader_investment_plan"] and "trader_investment_plan" not in reports_completed:
|
||||
print("✅ Trading plan completed!")
|
||||
agent_progress["Trading Team"] = "completed"
|
||||
reports_completed.append("trader_investment_plan")
|
||||
|
||||
events = [
|
||||
json.dumps({'type': 'agent_status', 'agent': 'trader', 'status': 'completed'}),
|
||||
json.dumps({'type': 'report', 'section': 'trader_investment_plan', 'content': chunk['trader_investment_plan']}),
|
||||
json.dumps({'type': 'progress', 'content': '95'})
|
||||
]
|
||||
|
||||
for event in events:
|
||||
print(f"📤 Sending: {event[:100]}...")
|
||||
yield f"data: {event}\n\n"
|
||||
|
||||
# Handle final decision
|
||||
if "final_trade_decision" in chunk and chunk["final_trade_decision"] and "final_trade_decision" not in reports_completed:
|
||||
print("✅ Final decision completed!")
|
||||
reports_completed.append("final_trade_decision")
|
||||
|
||||
events = [
|
||||
json.dumps({'type': 'report', 'section': 'final_trade_decision', 'content': chunk['final_trade_decision']}),
|
||||
json.dumps({'type': 'progress', 'content': '100'})
|
||||
]
|
||||
|
||||
for event in events:
|
||||
print(f"📤 Sending: {event[:100]}...")
|
||||
yield f"data: {event}\n\n"
|
||||
|
||||
print(f"🔄 Streaming completed. Processed {chunk_count} chunks, {len(reports_completed)} reports completed")
|
||||
|
||||
# Get final state and process signal
|
||||
final_state = trace[-1] if trace else {}
|
||||
processed_signal = graph.process_signal(final_state.get("final_trade_decision", ""))
|
||||
|
||||
# Send completion
|
||||
completion_event = json.dumps({'type': 'complete', 'message': 'Analysis completed successfully', 'signal': processed_signal})
|
||||
print(f"📤 Sending completion: {completion_event}")
|
||||
yield f"data: {completion_event}\n\n"
|
||||
|
||||
except Exception as e:
|
||||
print(f"💥 Error in streaming: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
error_event = json.dumps({'type': 'error', 'message': str(e)})
|
||||
print(f"📤 Sending error: {error_event}")
|
||||
yield f"data: {error_event}\n\n"
|
||||
|
||||
return StreamingResponse(
|
||||
event_stream(),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Access-Control-Allow-Origin": "*",
|
||||
"Access-Control-Allow-Headers": "Cache-Control",
|
||||
"X-Accel-Buffering": "no" # Disable nginx buffering
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"💥 Error in stream_analysis: {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Check if the environment is properly set up for TradingAgents
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
|
||||
print("🔍 TradingAgents Environment Check")
|
||||
print("=" * 50)
|
||||
|
||||
# Check Python version
|
||||
print(f"✓ Python version: {sys.version.split()[0]}")
|
||||
|
||||
# Check required packages
|
||||
required_packages = [
|
||||
'fastapi',
|
||||
'uvicorn',
|
||||
'pydantic',
|
||||
'openai',
|
||||
'langchain',
|
||||
'langchain_openai',
|
||||
'langgraph'
|
||||
]
|
||||
|
||||
print("\nChecking required packages:")
|
||||
missing_packages = []
|
||||
for package in required_packages:
|
||||
try:
|
||||
__import__(package.replace('-', '_'))
|
||||
print(f"✓ {package} is installed")
|
||||
except ImportError:
|
||||
print(f"✗ {package} is NOT installed")
|
||||
missing_packages.append(package)
|
||||
|
||||
# Check environment variables
|
||||
print("\nChecking environment variables:")
|
||||
env_vars = {
|
||||
'OPENAI_API_KEY': 'Required for AI agents',
|
||||
'FINNHUB_API_KEY': 'Required for market data',
|
||||
'REDDIT_CLIENT_ID': 'Optional for sentiment analysis',
|
||||
'REDDIT_CLIENT_SECRET': 'Optional for sentiment analysis'
|
||||
}
|
||||
|
||||
missing_required = []
|
||||
for var, description in env_vars.items():
|
||||
value = os.getenv(var)
|
||||
if value:
|
||||
print(f"✓ {var} is set ({description})")
|
||||
else:
|
||||
if 'Required' in description:
|
||||
print(f"✗ {var} is NOT set ({description})")
|
||||
missing_required.append(var)
|
||||
else:
|
||||
print(f"⚠ {var} is not set ({description})")
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 50)
|
||||
if missing_packages:
|
||||
print("❌ Missing packages:")
|
||||
for pkg in missing_packages:
|
||||
print(f" - {pkg}")
|
||||
print("\nInstall with: pip install -r requirements.txt")
|
||||
|
||||
if missing_required:
|
||||
print("❌ Missing required environment variables:")
|
||||
for var in missing_required:
|
||||
print(f" - {var}")
|
||||
print("\nAdd these to your .env file")
|
||||
|
||||
if not missing_packages and not missing_required:
|
||||
print("✅ Environment is properly configured!")
|
||||
print("\nYou can now run:")
|
||||
print(" uv run python3 run_api.py")
|
||||
else:
|
||||
print("\n⚠️ Fix the issues above before running the API")
|
||||
|
|
@ -1182,10 +1182,32 @@ def analyze(
|
|||
"--advanced",
|
||||
"-a",
|
||||
help="Use advanced configuration mode with full customization options"
|
||||
),
|
||||
streaming: bool = typer.Option(
|
||||
False,
|
||||
"--streaming",
|
||||
"-s",
|
||||
help="Enable real-time streaming of analysis reports as they're generated"
|
||||
)
|
||||
):
|
||||
"""Run trading analysis with simplified or advanced configuration."""
|
||||
run_analysis(advanced_mode=advanced)
|
||||
if streaming:
|
||||
run_analysis_streaming(advanced_mode=advanced)
|
||||
else:
|
||||
run_analysis(advanced_mode=advanced)
|
||||
|
||||
|
||||
@app.command()
|
||||
def stream(
|
||||
advanced: bool = typer.Option(
|
||||
False,
|
||||
"--advanced",
|
||||
"-a",
|
||||
help="Use advanced configuration mode with full customization options"
|
||||
)
|
||||
):
|
||||
"""Run real-time streaming trading analysis."""
|
||||
run_analysis_streaming(advanced_mode=advanced)
|
||||
|
||||
|
||||
@app.callback(invoke_without_command=True)
|
||||
|
|
@ -1196,12 +1218,510 @@ def main(
|
|||
"--advanced",
|
||||
"-a",
|
||||
help="Use advanced configuration mode with full customization options"
|
||||
),
|
||||
streaming: bool = typer.Option(
|
||||
False,
|
||||
"--streaming",
|
||||
"-s",
|
||||
help="Enable real-time streaming of analysis reports as they're generated"
|
||||
)
|
||||
):
|
||||
"""TradingAgents CLI: Multi-Agents LLM Financial Trading Framework"""
|
||||
if ctx.invoked_subcommand is None:
|
||||
# Default behavior - run analysis
|
||||
run_analysis(advanced_mode=advanced)
|
||||
if streaming:
|
||||
run_analysis_streaming(advanced_mode=advanced)
|
||||
else:
|
||||
run_analysis(advanced_mode=advanced)
|
||||
|
||||
|
||||
class StreamingMessageBuffer(MessageBuffer):
|
||||
"""Enhanced MessageBuffer for real-time content streaming"""
|
||||
|
||||
def __init__(self, max_length=100):
|
||||
super().__init__(max_length)
|
||||
self.streaming_content = {
|
||||
"current_agent": None,
|
||||
"current_content": "",
|
||||
"content_buffer": "",
|
||||
"last_streamed_length": 0
|
||||
}
|
||||
self.content_callbacks = []
|
||||
|
||||
def add_content_callback(self, callback):
|
||||
"""Add a callback to be called when new content is streamed"""
|
||||
self.content_callbacks.append(callback)
|
||||
|
||||
def stream_content(self, agent_name, content_chunk):
|
||||
"""Stream content in real-time"""
|
||||
self.streaming_content["current_agent"] = agent_name
|
||||
self.streaming_content["content_buffer"] += content_chunk
|
||||
|
||||
# Call registered callbacks with new content
|
||||
for callback in self.content_callbacks:
|
||||
callback(agent_name, content_chunk, self.streaming_content["content_buffer"])
|
||||
|
||||
def finalize_streaming_content(self, section_name):
|
||||
"""Finalize the streaming content into a report section"""
|
||||
if self.streaming_content["content_buffer"]:
|
||||
self.update_report_section(section_name, self.streaming_content["content_buffer"])
|
||||
self.streaming_content["content_buffer"] = ""
|
||||
self.streaming_content["last_streamed_length"] = 0
|
||||
|
||||
|
||||
def create_streaming_layout():
|
||||
"""Create layout optimized for streaming content"""
|
||||
layout = Layout()
|
||||
layout.split_column(
|
||||
Layout(name="header", size=3),
|
||||
Layout(name="main"),
|
||||
Layout(name="footer", size=3),
|
||||
)
|
||||
layout["main"].split_column(
|
||||
Layout(name="upper", ratio=2),
|
||||
Layout(name="streaming_content", ratio=4),
|
||||
Layout(name="analysis", ratio=3)
|
||||
)
|
||||
layout["upper"].split_row(
|
||||
Layout(name="progress", ratio=2), Layout(name="messages", ratio=3)
|
||||
)
|
||||
return layout
|
||||
|
||||
|
||||
def update_streaming_display(layout, streaming_buffer, spinner_text=None):
|
||||
"""Update display with streaming content"""
|
||||
# Update header
|
||||
layout["header"].update(
|
||||
Panel(
|
||||
"[bold green]Welcome to TradingAgents CLI[/bold green]\n"
|
||||
"[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]",
|
||||
title="Welcome to TradingAgents",
|
||||
border_style="green",
|
||||
)
|
||||
)
|
||||
|
||||
# Update progress panel using streaming_buffer
|
||||
progress_table = Table(show_header=False, box=box.MINIMAL)
|
||||
progress_table.add_column("Agent", style="cyan", no_wrap=True)
|
||||
progress_table.add_column("Status", style="magenta")
|
||||
|
||||
for agent, status in streaming_buffer.agent_status.items():
|
||||
if status == "completed":
|
||||
status_icon = "✅"
|
||||
elif status == "in_progress":
|
||||
status_icon = "🔄"
|
||||
else:
|
||||
status_icon = "⏳"
|
||||
progress_table.add_row(agent, f"{status_icon} {status.title()}")
|
||||
|
||||
layout["progress"].update(
|
||||
Panel(
|
||||
progress_table,
|
||||
title="Agent Progress",
|
||||
border_style="blue"
|
||||
)
|
||||
)
|
||||
|
||||
# Update messages panel using streaming_buffer
|
||||
messages_content = []
|
||||
for timestamp, msg_type, content in list(streaming_buffer.messages)[-10:]: # Show last 10 messages
|
||||
messages_content.append(f"[dim]{timestamp}[/dim] [{msg_type}] {content}")
|
||||
|
||||
if spinner_text:
|
||||
messages_content.append(f"[yellow]⚡ {spinner_text}[/yellow]")
|
||||
|
||||
layout["messages"].update(
|
||||
Panel(
|
||||
"\n".join(messages_content),
|
||||
title="Recent Messages",
|
||||
border_style="yellow"
|
||||
)
|
||||
)
|
||||
|
||||
# Add streaming content panel
|
||||
if streaming_buffer.streaming_content["current_agent"] and streaming_buffer.streaming_content["content_buffer"]:
|
||||
agent_name = streaming_buffer.streaming_content["current_agent"]
|
||||
content = streaming_buffer.streaming_content["content_buffer"]
|
||||
|
||||
# Limit display content to prevent overwhelming the terminal
|
||||
display_content = content[-2000:] if len(content) > 2000 else content
|
||||
if len(content) > 2000:
|
||||
display_content = "...\n" + display_content
|
||||
|
||||
streaming_panel = Panel(
|
||||
Markdown(display_content),
|
||||
title=f"🔴 Live: {agent_name}",
|
||||
border_style="red",
|
||||
expand=True
|
||||
)
|
||||
layout["streaming_content"].update(streaming_panel)
|
||||
else:
|
||||
layout["streaming_content"].update(
|
||||
Panel(
|
||||
"[dim]Waiting for content to stream...[/dim]",
|
||||
title="📡 Streaming Content",
|
||||
border_style="dim"
|
||||
)
|
||||
)
|
||||
|
||||
# Update analysis panel using streaming_buffer
|
||||
if streaming_buffer.current_report:
|
||||
layout["analysis"].update(
|
||||
Panel(
|
||||
Markdown(streaming_buffer.current_report),
|
||||
title="Latest Report Section",
|
||||
border_style="green"
|
||||
)
|
||||
)
|
||||
else:
|
||||
layout["analysis"].update(
|
||||
Panel(
|
||||
"[dim]Analysis reports will appear here...[/dim]",
|
||||
title="Analysis Reports",
|
||||
border_style="dim"
|
||||
)
|
||||
)
|
||||
|
||||
# Footer with instructions
|
||||
layout["footer"].update(
|
||||
Panel(
|
||||
"[bold]TradingAgents Streaming Analysis[/bold] | Press Ctrl+C to stop",
|
||||
style="bold white on blue"
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def update_research_team_status_streaming(streaming_buffer, status):
|
||||
"""Update all research team agent statuses for streaming"""
|
||||
research_agents = ["Bull Researcher", "Bear Researcher", "Research Manager"]
|
||||
for agent in research_agents:
|
||||
streaming_buffer.update_agent_status(agent, status)
|
||||
|
||||
|
||||
def run_analysis_streaming(advanced_mode=False):
|
||||
"""
|
||||
Streaming version of run_analysis that delivers reports in real-time
|
||||
"""
|
||||
# Get user selections based on mode
|
||||
if advanced_mode:
|
||||
selections = get_user_selections_advanced()
|
||||
else:
|
||||
selections = get_user_selections()
|
||||
|
||||
# Create config with selected research depth
|
||||
config = DEFAULT_CONFIG.copy()
|
||||
config["max_debate_rounds"] = selections["research_depth"]
|
||||
config["max_risk_discuss_rounds"] = selections["research_depth"]
|
||||
config["quick_think_llm"] = selections["shallow_thinker"]
|
||||
config["deep_think_llm"] = selections["deep_thinker"]
|
||||
config["backend_url"] = selections["backend_url"]
|
||||
config["llm_provider"] = selections["llm_provider"].lower()
|
||||
|
||||
# Initialize the graph
|
||||
graph = TradingAgentsGraph(
|
||||
[analyst.value for analyst in selections["analysts"]], config=config, debug=True
|
||||
)
|
||||
|
||||
# Create result directory
|
||||
results_dir = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"]
|
||||
results_dir.mkdir(parents=True, exist_ok=True)
|
||||
report_dir = results_dir / "reports"
|
||||
report_dir.mkdir(parents=True, exist_ok=True)
|
||||
log_file = results_dir / "message_tool.log"
|
||||
log_file.touch(exist_ok=True)
|
||||
|
||||
# Use streaming message buffer instead of regular one
|
||||
streaming_buffer = StreamingMessageBuffer()
|
||||
|
||||
def save_message_decorator(obj, func_name):
|
||||
func = getattr(obj, func_name)
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
func(*args, **kwargs)
|
||||
timestamp, message_type, content = obj.messages[-1]
|
||||
content = content.replace("\n", " ") # Replace newlines with spaces
|
||||
with open(log_file, "a") as f:
|
||||
f.write(f"{timestamp} [{message_type}] {content}\n")
|
||||
return wrapper
|
||||
|
||||
def save_tool_call_decorator(obj, func_name):
|
||||
func = getattr(obj, func_name)
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
func(*args, **kwargs)
|
||||
timestamp, tool_name, args = obj.tool_calls[-1]
|
||||
args_str = ", ".join(f"{k}={v}" for k, v in args.items())
|
||||
with open(log_file, "a") as f:
|
||||
f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
|
||||
return wrapper
|
||||
|
||||
def save_report_section_decorator(obj, func_name):
|
||||
func = getattr(obj, func_name)
|
||||
@wraps(func)
|
||||
def wrapper(section_name, content):
|
||||
func(section_name, content)
|
||||
if section_name in obj.report_sections and obj.report_sections[section_name] is not None:
|
||||
content = obj.report_sections[section_name]
|
||||
if content:
|
||||
file_name = f"{section_name}.md"
|
||||
with open(report_dir / file_name, "w") as f:
|
||||
f.write(content)
|
||||
return wrapper
|
||||
|
||||
streaming_buffer.add_message = save_message_decorator(streaming_buffer, "add_message")
|
||||
streaming_buffer.add_tool_call = save_tool_call_decorator(streaming_buffer, "add_tool_call")
|
||||
streaming_buffer.update_report_section = save_report_section_decorator(streaming_buffer, "update_report_section")
|
||||
|
||||
# Create streaming layout
|
||||
layout = create_streaming_layout()
|
||||
|
||||
# Agent mapping for streaming
|
||||
agent_mapping = {
|
||||
"market": "Market Analyst",
|
||||
"social": "Social Media Analyst",
|
||||
"news": "News Analyst",
|
||||
"fundamentals": "Fundamentals Analyst",
|
||||
"bull": "Bull Researcher",
|
||||
"bear": "Bear Researcher",
|
||||
"research_manager": "Research Manager",
|
||||
"trader": "Trading Team",
|
||||
"risky": "Risky Analyst",
|
||||
"safe": "Safe Analyst",
|
||||
"neutral": "Neutral Analyst",
|
||||
"portfolio": "Portfolio Manager"
|
||||
}
|
||||
|
||||
with Live(layout, refresh_per_second=8) as live: # Higher refresh rate for streaming
|
||||
# Initial display
|
||||
update_streaming_display(layout, streaming_buffer)
|
||||
|
||||
# Add initial messages
|
||||
streaming_buffer.add_message("System", f"Selected ticker: {selections['ticker']}")
|
||||
streaming_buffer.add_message(
|
||||
"System", f"Analysis date: {selections['analysis_date']}"
|
||||
)
|
||||
streaming_buffer.add_message(
|
||||
"System",
|
||||
f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}",
|
||||
)
|
||||
update_streaming_display(layout, streaming_buffer)
|
||||
|
||||
# Reset agent statuses
|
||||
for agent in streaming_buffer.agent_status:
|
||||
streaming_buffer.update_agent_status(agent, "pending")
|
||||
|
||||
# Reset report sections
|
||||
for section in streaming_buffer.report_sections:
|
||||
streaming_buffer.report_sections[section] = None
|
||||
streaming_buffer.current_report = None
|
||||
streaming_buffer.final_report = None
|
||||
|
||||
# Update agent status to in_progress for the first analyst
|
||||
first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst"
|
||||
streaming_buffer.update_agent_status(first_analyst, "in_progress")
|
||||
update_streaming_display(layout, streaming_buffer)
|
||||
|
||||
# Create spinner text
|
||||
spinner_text = (
|
||||
f"Analyzing {selections['ticker']} on {selections['analysis_date']}..."
|
||||
)
|
||||
update_streaming_display(layout, streaming_buffer, spinner_text)
|
||||
|
||||
# Initialize state and get graph args
|
||||
init_agent_state = graph.propagator.create_initial_state(
|
||||
selections["ticker"], selections["analysis_date"]
|
||||
)
|
||||
args = graph.propagator.get_graph_args()
|
||||
|
||||
# Stream the analysis with real-time content delivery
|
||||
trace = []
|
||||
current_streaming_agent = None
|
||||
|
||||
for chunk in graph.graph.stream(init_agent_state, **args):
|
||||
if len(chunk["messages"]) > 0:
|
||||
# Get the last message from the chunk
|
||||
last_message = chunk["messages"][-1]
|
||||
|
||||
# Extract message content and type
|
||||
if hasattr(last_message, "content"):
|
||||
content = extract_content_string(last_message.content)
|
||||
msg_type = "Reasoning"
|
||||
|
||||
# Detect which agent is currently speaking and stream content
|
||||
agent_detected = None
|
||||
for key, agent_name in agent_mapping.items():
|
||||
if any(keyword in content.lower() for keyword in [key, agent_name.lower()]):
|
||||
agent_detected = agent_name
|
||||
break
|
||||
|
||||
# If we detected an agent or have ongoing streaming
|
||||
if agent_detected or current_streaming_agent:
|
||||
if agent_detected and agent_detected != current_streaming_agent:
|
||||
# New agent started - finalize previous and start new
|
||||
if current_streaming_agent:
|
||||
section_map = {
|
||||
"Market Analyst": "market_report",
|
||||
"Social Media Analyst": "sentiment_report",
|
||||
"News Analyst": "news_report",
|
||||
"Fundamentals Analyst": "fundamentals_report",
|
||||
"Research Manager": "investment_plan",
|
||||
"Trading Team": "trader_investment_plan",
|
||||
"Portfolio Manager": "final_trade_decision"
|
||||
}
|
||||
if current_streaming_agent in section_map:
|
||||
streaming_buffer.finalize_streaming_content(section_map[current_streaming_agent])
|
||||
|
||||
current_streaming_agent = agent_detected
|
||||
streaming_buffer.update_agent_status(agent_detected, "in_progress")
|
||||
|
||||
# Stream the content in real-time
|
||||
if current_streaming_agent:
|
||||
streaming_buffer.stream_content(current_streaming_agent, content + "\n")
|
||||
|
||||
else:
|
||||
content = str(last_message)
|
||||
msg_type = "System"
|
||||
|
||||
# Add message to buffer
|
||||
streaming_buffer.add_message(msg_type, content[:200] + "..." if len(content) > 200 else content)
|
||||
|
||||
# Handle tool calls
|
||||
if hasattr(last_message, "tool_calls"):
|
||||
for tool_call in last_message.tool_calls:
|
||||
if isinstance(tool_call, dict):
|
||||
streaming_buffer.add_tool_call(
|
||||
tool_call["name"], tool_call["args"]
|
||||
)
|
||||
else:
|
||||
streaming_buffer.add_tool_call(tool_call.name, tool_call.args)
|
||||
|
||||
# Handle section completions and agent status updates
|
||||
# Analyst Team Reports
|
||||
if "market_report" in chunk and chunk["market_report"]:
|
||||
streaming_buffer.update_report_section("market_report", chunk["market_report"])
|
||||
streaming_buffer.update_agent_status("Market Analyst", "completed")
|
||||
current_streaming_agent = None
|
||||
if "social" in [a.value for a in selections["analysts"]]:
|
||||
streaming_buffer.update_agent_status("Social Media Analyst", "in_progress")
|
||||
|
||||
if "sentiment_report" in chunk and chunk["sentiment_report"]:
|
||||
streaming_buffer.update_report_section("sentiment_report", chunk["sentiment_report"])
|
||||
streaming_buffer.update_agent_status("Social Media Analyst", "completed")
|
||||
current_streaming_agent = None
|
||||
if "news" in [a.value for a in selections["analysts"]]:
|
||||
streaming_buffer.update_agent_status("News Analyst", "in_progress")
|
||||
|
||||
if "news_report" in chunk and chunk["news_report"]:
|
||||
streaming_buffer.update_report_section("news_report", chunk["news_report"])
|
||||
streaming_buffer.update_agent_status("News Analyst", "completed")
|
||||
current_streaming_agent = None
|
||||
if "fundamentals" in [a.value for a in selections["analysts"]]:
|
||||
streaming_buffer.update_agent_status("Fundamentals Analyst", "in_progress")
|
||||
|
||||
if "fundamentals_report" in chunk and chunk["fundamentals_report"]:
|
||||
streaming_buffer.update_report_section("fundamentals_report", chunk["fundamentals_report"])
|
||||
streaming_buffer.update_agent_status("Fundamentals Analyst", "completed")
|
||||
current_streaming_agent = None
|
||||
update_research_team_status_streaming(streaming_buffer, "in_progress")
|
||||
|
||||
# Research Team - Handle Investment Debate State with streaming
|
||||
if "investment_debate_state" in chunk and chunk["investment_debate_state"]:
|
||||
debate_state = chunk["investment_debate_state"]
|
||||
|
||||
if "bull_history" in debate_state and debate_state["bull_history"]:
|
||||
update_research_team_status_streaming(streaming_buffer, "in_progress")
|
||||
bull_responses = debate_state["bull_history"].split("\n")
|
||||
latest_bull = bull_responses[-1] if bull_responses else ""
|
||||
if latest_bull:
|
||||
streaming_buffer.stream_content("Bull Researcher", latest_bull + "\n")
|
||||
|
||||
if "bear_history" in debate_state and debate_state["bear_history"]:
|
||||
update_research_team_status_streaming(streaming_buffer, "in_progress")
|
||||
bear_responses = debate_state["bear_history"].split("\n")
|
||||
latest_bear = bear_responses[-1] if bear_responses else ""
|
||||
if latest_bear:
|
||||
streaming_buffer.stream_content("Bear Researcher", latest_bear + "\n")
|
||||
|
||||
if "judge_decision" in debate_state and debate_state["judge_decision"]:
|
||||
streaming_buffer.stream_content("Research Manager", debate_state["judge_decision"] + "\n")
|
||||
streaming_buffer.finalize_streaming_content("investment_plan")
|
||||
update_research_team_status_streaming(streaming_buffer, "completed")
|
||||
streaming_buffer.update_agent_status("Risky Analyst", "in_progress")
|
||||
current_streaming_agent = None
|
||||
|
||||
# Trading Team with streaming
|
||||
if "trader_investment_plan" in chunk and chunk["trader_investment_plan"]:
|
||||
streaming_buffer.update_report_section("trader_investment_plan", chunk["trader_investment_plan"])
|
||||
streaming_buffer.update_agent_status("Risky Analyst", "in_progress")
|
||||
current_streaming_agent = None
|
||||
|
||||
# Risk Management Team with streaming
|
||||
if "risk_debate_state" in chunk and chunk["risk_debate_state"]:
|
||||
risk_state = chunk["risk_debate_state"]
|
||||
|
||||
if "current_risky_response" in risk_state and risk_state["current_risky_response"]:
|
||||
streaming_buffer.update_agent_status("Risky Analyst", "in_progress")
|
||||
streaming_buffer.stream_content("Risky Analyst", risk_state["current_risky_response"] + "\n")
|
||||
|
||||
if "current_safe_response" in risk_state and risk_state["current_safe_response"]:
|
||||
streaming_buffer.update_agent_status("Safe Analyst", "in_progress")
|
||||
streaming_buffer.stream_content("Safe Analyst", risk_state["current_safe_response"] + "\n")
|
||||
|
||||
if "current_neutral_response" in risk_state and risk_state["current_neutral_response"]:
|
||||
streaming_buffer.update_agent_status("Neutral Analyst", "in_progress")
|
||||
streaming_buffer.stream_content("Neutral Analyst", risk_state["current_neutral_response"] + "\n")
|
||||
|
||||
if "judge_decision" in risk_state and risk_state["judge_decision"]:
|
||||
streaming_buffer.stream_content("Portfolio Manager", risk_state["judge_decision"] + "\n")
|
||||
streaming_buffer.finalize_streaming_content("final_trade_decision")
|
||||
|
||||
# Mark all risk team as completed
|
||||
streaming_buffer.update_agent_status("Risky Analyst", "completed")
|
||||
streaming_buffer.update_agent_status("Safe Analyst", "completed")
|
||||
streaming_buffer.update_agent_status("Neutral Analyst", "completed")
|
||||
streaming_buffer.update_agent_status("Portfolio Manager", "completed")
|
||||
current_streaming_agent = None
|
||||
|
||||
# Update the display with streaming content
|
||||
update_streaming_display(layout, streaming_buffer)
|
||||
|
||||
trace.append(chunk)
|
||||
|
||||
# Finalize any remaining streaming content
|
||||
if current_streaming_agent:
|
||||
section_map = {
|
||||
"Market Analyst": "market_report",
|
||||
"Social Media Analyst": "sentiment_report",
|
||||
"News Analyst": "news_report",
|
||||
"Fundamentals Analyst": "fundamentals_report",
|
||||
"Research Manager": "investment_plan",
|
||||
"Trading Team": "trader_investment_plan",
|
||||
"Portfolio Manager": "final_trade_decision"
|
||||
}
|
||||
if current_streaming_agent in section_map:
|
||||
streaming_buffer.finalize_streaming_content(section_map[current_streaming_agent])
|
||||
|
||||
# Get final state and decision
|
||||
final_state = trace[-1]
|
||||
decision = graph.process_signal(final_state["final_trade_decision"])
|
||||
|
||||
# Update all agent statuses to completed
|
||||
for agent in streaming_buffer.agent_status:
|
||||
streaming_buffer.update_agent_status(agent, "completed")
|
||||
|
||||
streaming_buffer.add_message(
|
||||
"Analysis", f"Completed streaming analysis for {selections['analysis_date']}"
|
||||
)
|
||||
|
||||
# Update final report sections
|
||||
for section in streaming_buffer.report_sections.keys():
|
||||
if section in final_state:
|
||||
streaming_buffer.update_report_section(section, final_state[section])
|
||||
|
||||
# Display the complete final report
|
||||
display_complete_report(final_state)
|
||||
|
||||
update_streaming_display(layout, streaming_buffer)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
@ -24,3 +24,7 @@ rich
|
|||
questionary
|
||||
langchain_anthropic
|
||||
langchain-google-genai
|
||||
fastapi
|
||||
pydantic
|
||||
uvicorn[standard]
|
||||
python-dotenv
|
||||
|
|
@ -0,0 +1,132 @@
|
|||
#!/bin/bash
|
||||
|
||||
# TradingAgents API Restart Script
|
||||
# This script kills all existing API processes and restarts the server cleanly
|
||||
|
||||
echo "🔄 TradingAgents API Restart Script"
|
||||
echo "=================================="
|
||||
|
||||
# Function to kill processes by pattern
|
||||
kill_processes() {
|
||||
local pattern=$1
|
||||
local description=$2
|
||||
|
||||
echo "🔍 Looking for $description..."
|
||||
pids=$(ps aux | grep "$pattern" | grep -v grep | awk '{print $2}')
|
||||
|
||||
if [ -n "$pids" ]; then
|
||||
echo "💀 Killing $description: $pids"
|
||||
echo "$pids" | xargs kill -9 2>/dev/null || true
|
||||
sleep 1
|
||||
else
|
||||
echo "✅ No $description found"
|
||||
fi
|
||||
}
|
||||
|
||||
# Function to kill process on specific port
|
||||
kill_port() {
|
||||
local port=$1
|
||||
echo "🔍 Looking for processes on port $port..."
|
||||
|
||||
# Try lsof first
|
||||
if command -v lsof >/dev/null 2>&1; then
|
||||
pids=$(lsof -ti :$port 2>/dev/null)
|
||||
if [ -n "$pids" ]; then
|
||||
echo "💀 Killing processes on port $port: $pids"
|
||||
echo "$pids" | xargs kill -9 2>/dev/null || true
|
||||
sleep 1
|
||||
else
|
||||
echo "✅ No processes found on port $port"
|
||||
fi
|
||||
else
|
||||
echo "⚠️ lsof not available, skipping port check"
|
||||
fi
|
||||
}
|
||||
|
||||
# Step 1: Kill all Python API processes
|
||||
echo
|
||||
echo "📋 Step 1: Killing existing API processes..."
|
||||
kill_processes "run_api.py" "run_api.py processes"
|
||||
kill_processes "uvicorn" "uvicorn processes"
|
||||
kill_processes "TradingAgents" "TradingAgents processes"
|
||||
|
||||
# Step 2: Kill processes on port 8000
|
||||
echo
|
||||
echo "📋 Step 2: Killing processes on port 8000..."
|
||||
kill_port 8000
|
||||
|
||||
# Step 3: Wait for cleanup
|
||||
echo
|
||||
echo "📋 Step 3: Waiting for cleanup..."
|
||||
sleep 3
|
||||
|
||||
# Step 4: Verify port is free
|
||||
echo
|
||||
echo "📋 Step 4: Verifying port 8000 is free..."
|
||||
if command -v lsof >/dev/null 2>&1; then
|
||||
if lsof -ti :8000 >/dev/null 2>&1; then
|
||||
echo "⚠️ Port 8000 is still in use. Trying force kill..."
|
||||
lsof -ti :8000 | xargs kill -9 2>/dev/null || true
|
||||
sleep 2
|
||||
fi
|
||||
fi
|
||||
|
||||
# Step 5: Change to backend directory
|
||||
echo
|
||||
echo "📋 Step 5: Changing to backend directory..."
|
||||
if [ ! -f "run_api.py" ]; then
|
||||
if [ -f "../backend/run_api.py" ]; then
|
||||
cd ../backend
|
||||
echo "✅ Changed to backend directory"
|
||||
elif [ -f "backend/run_api.py" ]; then
|
||||
cd backend
|
||||
echo "✅ Changed to backend directory"
|
||||
else
|
||||
echo "❌ Cannot find run_api.py. Please run this script from the project root or backend directory."
|
||||
exit 1
|
||||
fi
|
||||
else
|
||||
echo "✅ Already in backend directory"
|
||||
fi
|
||||
|
||||
# Step 6: Activate virtual environment
|
||||
echo
|
||||
echo "📋 Step 6: Activating virtual environment..."
|
||||
if [ -f "venv/bin/activate" ]; then
|
||||
source venv/bin/activate
|
||||
echo "✅ Virtual environment activated"
|
||||
elif [ -f "../venv/bin/activate" ]; then
|
||||
source ../venv/bin/activate
|
||||
echo "✅ Virtual environment activated"
|
||||
else
|
||||
echo "⚠️ Virtual environment not found. Continuing without activation..."
|
||||
fi
|
||||
|
||||
# Step 7: Check if port is really free
|
||||
echo
|
||||
echo "📋 Step 7: Final port check..."
|
||||
if command -v nc >/dev/null 2>&1; then
|
||||
if nc -z localhost 8000 2>/dev/null; then
|
||||
echo "❌ Port 8000 is still occupied!"
|
||||
echo "💡 Try running: sudo lsof -ti :8000 | xargs sudo kill -9"
|
||||
echo "💡 Or use a different port in the script"
|
||||
exit 1
|
||||
else
|
||||
echo "✅ Port 8000 is free"
|
||||
fi
|
||||
fi
|
||||
|
||||
# Step 8: Start the API server
|
||||
echo
|
||||
echo "📋 Step 8: Starting TradingAgents API server..."
|
||||
echo "🚀 Server will be available at http://localhost:8000"
|
||||
echo "🚀 Server will be available at http://192.168.4.223:8000 (for iOS)"
|
||||
echo "📚 API docs will be at http://localhost:8000/docs"
|
||||
echo
|
||||
echo "💡 Press Ctrl+C to stop the server"
|
||||
echo "💡 Enhanced logging is active - you'll see detailed tool usage"
|
||||
echo
|
||||
echo "=================================="
|
||||
|
||||
# Start the server (not in background so we can see logs)
|
||||
python run_api.py
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
#!/usr/bin/env python
|
||||
"""
|
||||
Run the TradingAgents FastAPI server
|
||||
"""
|
||||
import uvicorn
|
||||
from tradingagents.default_config import DEFAULT_CONFIG
|
||||
|
||||
if __name__ == "__main__":
|
||||
host = "0.0.0.0" # Allow connections from any interface
|
||||
port = DEFAULT_CONFIG["api_port"]
|
||||
|
||||
print(f"\n🚀 Starting TradingAgents API server...")
|
||||
print(f"📍 Server will be available at http://localhost:{port}")
|
||||
print(f"📚 API docs will be at http://localhost:{port}/docs\n")
|
||||
|
||||
uvicorn.run(
|
||||
"api:app",
|
||||
host=host,
|
||||
port=port,
|
||||
reload=True,
|
||||
log_level="info"
|
||||
)
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
# Testing the TradingAgents API
|
||||
|
||||
## 1. Start the API Server
|
||||
|
||||
First, make sure you're in the backend directory and have your environment activated:
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
source venv/bin/activate # On Windows: venv\Scripts\activate
|
||||
python run_api.py
|
||||
```
|
||||
|
||||
You should see:
|
||||
```
|
||||
🚀 Starting TradingAgents API server...
|
||||
📍 Server will be available at http://localhost:8000
|
||||
📚 API docs will be at http://localhost:8000/docs
|
||||
```
|
||||
|
||||
## 2. Test Using FastAPI Interactive Docs
|
||||
|
||||
The easiest way to test is using FastAPI's built-in documentation:
|
||||
|
||||
1. Open your browser and go to: http://localhost:8000/docs
|
||||
2. You'll see an interactive API documentation (Swagger UI)
|
||||
3. Click on any endpoint to expand it
|
||||
4. Click "Try it out" to test the endpoint
|
||||
|
||||
## 3. Test Individual Endpoints
|
||||
|
||||
### A. Test Root Endpoint
|
||||
```bash
|
||||
curl http://localhost:8000/
|
||||
```
|
||||
|
||||
Expected response:
|
||||
```json
|
||||
{"message": "TradingAgents API is running"}
|
||||
```
|
||||
|
||||
### B. Test Health Check
|
||||
```bash
|
||||
curl http://localhost:8000/health
|
||||
```
|
||||
|
||||
Expected response:
|
||||
```json
|
||||
{"status": "healthy"}
|
||||
```
|
||||
|
||||
### C. Test Analysis Endpoint (Main Functionality)
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/analyze \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"ticker": "AAPL"}'
|
||||
```
|
||||
|
||||
Expected response structure:
|
||||
```json
|
||||
{
|
||||
"ticker": "AAPL",
|
||||
"analysis_date": "2024-06-29",
|
||||
"market_report": "...",
|
||||
"sentiment_report": "...",
|
||||
"news_report": "...",
|
||||
"fundamentals_report": "...",
|
||||
"investment_plan": "...",
|
||||
"trader_investment_plan": "...",
|
||||
"final_trade_decision": "...",
|
||||
"processed_signal": "BUY/SELL/HOLD",
|
||||
"error": null
|
||||
}
|
||||
```
|
||||
|
||||
## 4. Test Using Python
|
||||
|
||||
Create a test script `test_api.py`:
|
||||
|
||||
```python
|
||||
import requests
|
||||
import json
|
||||
|
||||
# Test root endpoint
|
||||
response = requests.get("http://localhost:8000/")
|
||||
print("Root endpoint:", response.json())
|
||||
|
||||
# Test health check
|
||||
response = requests.get("http://localhost:8000/health")
|
||||
print("Health check:", response.json())
|
||||
|
||||
# Test analysis
|
||||
data = {"ticker": "AAPL"}
|
||||
response = requests.post(
|
||||
"http://localhost:8000/analyze",
|
||||
json=data
|
||||
)
|
||||
print("Analysis response status:", response.status_code)
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
print(f"Ticker: {result['ticker']}")
|
||||
print(f"Date: {result['analysis_date']}")
|
||||
print(f"Signal: {result.get('processed_signal', 'N/A')}")
|
||||
print(f"Error: {result.get('error', 'None')}")
|
||||
```
|
||||
|
||||
## 5. Common Issues and Solutions
|
||||
|
||||
### Issue: Connection Refused
|
||||
- **Solution**: Make sure the server is running (`python run_api.py`)
|
||||
|
||||
### Issue: Missing API Keys
|
||||
- **Solution**: Ensure your `.env` file has:
|
||||
```
|
||||
OPENAI_API_KEY=your_key_here
|
||||
FINNHUB_API_KEY=your_key_here
|
||||
```
|
||||
|
||||
### Issue: Timeout or Slow Response
|
||||
- **Solution**: The analysis can take 30-60 seconds as it involves multiple AI agents
|
||||
|
||||
## 6. Test from iOS App
|
||||
|
||||
1. Make sure the API server is running
|
||||
2. Open the iOS app in Xcode
|
||||
3. Run the app (⌘+R)
|
||||
4. Enter a ticker and tap "Analyze"
|
||||
5. Check Xcode console for any network errors
|
||||
|
|
@ -0,0 +1,101 @@
|
|||
#!/usr/bin/env python
|
||||
"""
|
||||
Simple script to test the TradingAgents API
|
||||
"""
|
||||
import requests
|
||||
import json
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
# API base URL
|
||||
BASE_URL = "http://localhost:8000"
|
||||
|
||||
def test_root():
|
||||
"""Test root endpoint"""
|
||||
print("Testing root endpoint...")
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/")
|
||||
print(f"✅ Status: {response.status_code}")
|
||||
print(f"✅ Response: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
print()
|
||||
|
||||
def test_health():
|
||||
"""Test health check endpoint"""
|
||||
print("Testing health endpoint...")
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/health")
|
||||
print(f"✅ Status: {response.status_code}")
|
||||
print(f"✅ Response: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
print()
|
||||
|
||||
def test_analysis(ticker="AAPL"):
|
||||
"""Test analysis endpoint"""
|
||||
print(f"Testing analysis endpoint with ticker: {ticker}")
|
||||
print("⏳ This may take 30-60 seconds...")
|
||||
|
||||
try:
|
||||
start_time = time.time()
|
||||
response = requests.post(
|
||||
f"{BASE_URL}/analyze",
|
||||
json={"ticker": ticker}
|
||||
)
|
||||
end_time = time.time()
|
||||
|
||||
print(f"✅ Status: {response.status_code}")
|
||||
print(f"✅ Time taken: {end_time - start_time:.2f} seconds")
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
print(f"✅ Ticker: {result['ticker']}")
|
||||
print(f"✅ Date: {result['analysis_date']}")
|
||||
print(f"✅ Signal: {result.get('processed_signal', 'N/A')}")
|
||||
|
||||
if result.get('error'):
|
||||
print(f"⚠️ Error in analysis: {result['error']}")
|
||||
else:
|
||||
print("✅ Analysis completed successfully!")
|
||||
|
||||
# Show available reports
|
||||
reports = [
|
||||
'market_report', 'sentiment_report', 'news_report',
|
||||
'fundamentals_report', 'final_trade_decision'
|
||||
]
|
||||
available_reports = [r for r in reports if result.get(r)]
|
||||
print(f"✅ Available reports: {', '.join(available_reports)}")
|
||||
else:
|
||||
print(f"❌ Error response: {response.text}")
|
||||
|
||||
except requests.exceptions.ConnectionError:
|
||||
print("❌ Error: Could not connect to API. Is the server running?")
|
||||
print(" Run: python run_api.py")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
print()
|
||||
|
||||
def main():
|
||||
print("🚀 TradingAgents API Test Suite")
|
||||
print(f"📍 Testing API at: {BASE_URL}")
|
||||
print(f"🕐 Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print("-" * 50)
|
||||
|
||||
# Test endpoints
|
||||
test_root()
|
||||
test_health()
|
||||
|
||||
# Ask user if they want to test analysis
|
||||
user_input = input("Do you want to test the analysis endpoint? (y/n): ").lower()
|
||||
if user_input == 'y':
|
||||
ticker = input("Enter ticker to analyze (default: AAPL): ").strip().upper()
|
||||
if not ticker:
|
||||
ticker = "AAPL"
|
||||
test_analysis(ticker)
|
||||
|
||||
print("-" * 50)
|
||||
print("✅ Testing complete!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -11,7 +11,7 @@ class FinancialSituationMemory:
|
|||
self.embedding = "text-embedding-3-small"
|
||||
self.client = OpenAI(base_url=config["backend_url"])
|
||||
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
|
||||
self.situation_collection = self.chroma_client.create_collection(name=name)
|
||||
self.situation_collection = self.chroma_client.get_or_create_collection(name=name)
|
||||
|
||||
def get_embedding(self, text):
|
||||
"""Get OpenAI embedding for a text"""
|
||||
|
|
@ -73,11 +73,28 @@ def getNewsData(query, start_date, end_date):
|
|||
|
||||
for el in results_on_page:
|
||||
try:
|
||||
link = el.find("a")["href"]
|
||||
title = el.select_one("div.MBeuO").get_text()
|
||||
snippet = el.select_one(".GI74Re").get_text()
|
||||
date = el.select_one(".LfVVr").get_text()
|
||||
source = el.select_one(".NUnG9d span").get_text()
|
||||
# Extract link
|
||||
link_el = el.find("a")
|
||||
if not link_el or "href" not in link_el.attrs:
|
||||
continue
|
||||
link = link_el["href"]
|
||||
|
||||
# Extract title with null check
|
||||
title_el = el.select_one("div.MBeuO")
|
||||
title = title_el.get_text() if title_el else "No title"
|
||||
|
||||
# Extract snippet with null check
|
||||
snippet_el = el.select_one(".GI74Re")
|
||||
snippet = snippet_el.get_text() if snippet_el else "No snippet"
|
||||
|
||||
# Extract date with null check
|
||||
date_el = el.select_one(".LfVVr")
|
||||
date = date_el.get_text() if date_el else "No date"
|
||||
|
||||
# Extract source with null check
|
||||
source_el = el.select_one(".NUnG9d span")
|
||||
source = source_el.get_text() if source_el else "Unknown source"
|
||||
|
||||
news_results.append(
|
||||
{
|
||||
"link": link,
|
||||
|
|
@ -14,6 +14,10 @@ from tqdm import tqdm
|
|||
import yfinance as yf
|
||||
from openai import OpenAI
|
||||
from .config import get_config, set_config, DATA_DIR
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables so OpenAI tools can access API keys
|
||||
load_dotenv()
|
||||
|
||||
|
||||
def get_finnhub_news(
|
||||
|
|
@ -287,25 +291,47 @@ def get_google_news(
|
|||
curr_date: Annotated[str, "Curr date in yyyy-mm-dd format"],
|
||||
look_back_days: Annotated[int, "how many days to look back"],
|
||||
) -> str:
|
||||
query = query.replace(" ", "+")
|
||||
import logging
|
||||
import time
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Enhanced logging - Tool entry (for comparison with failing tools)
|
||||
start_time = time.time()
|
||||
logger.info(f"🔧 TOOL START: get_google_news | Agent: News Analyst | Query: {query} | Date: {curr_date}")
|
||||
|
||||
try:
|
||||
query = query.replace(" ", "+")
|
||||
|
||||
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = start_date - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
before = start_date - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
|
||||
news_results = getNewsData(query, before, curr_date)
|
||||
news_results = getNewsData(query, before, curr_date)
|
||||
|
||||
news_str = ""
|
||||
news_str = ""
|
||||
|
||||
for news in news_results:
|
||||
news_str += (
|
||||
f"### {news['title']} (source: {news['source']}) \n\n{news['snippet']}\n\n"
|
||||
)
|
||||
for news in news_results:
|
||||
news_str += (
|
||||
f"### {news['title']} (source: {news['source']}) \n\n{news['snippet']}\n\n"
|
||||
)
|
||||
|
||||
if len(news_results) == 0:
|
||||
return ""
|
||||
|
||||
return f"## {query} Google News, from {before} to {curr_date}:\n\n{news_str}"
|
||||
if len(news_results) == 0:
|
||||
result = ""
|
||||
else:
|
||||
result = f"## {query} Google News, from {before} to {curr_date}:\n\n{news_str}"
|
||||
|
||||
# Enhanced logging - Success
|
||||
duration = time.time() - start_time
|
||||
logger.info(f"✅ TOOL SUCCESS: get_google_news | Duration: {duration:.2f}s | Results count: {len(news_results)}")
|
||||
logger.info(f"📋 TOOL OUTPUT LENGTH: {len(result)} characters")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
# Enhanced logging - Error (for comparison)
|
||||
duration = time.time() - start_time
|
||||
logger.error(f"❌ TOOL ERROR: get_google_news | Duration: {duration:.2f}s")
|
||||
logger.error(f"🚨 FULL ERROR DETAILS: {type(e).__name__}: {str(e)}")
|
||||
raise e
|
||||
|
||||
|
||||
def get_reddit_global_news(
|
||||
|
|
@ -630,41 +656,60 @@ def get_YFin_data_online(
|
|||
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
|
||||
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
|
||||
):
|
||||
import logging
|
||||
import time
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Enhanced logging - Tool entry (for comparison with failing tools)
|
||||
start_time = time.time()
|
||||
logger.info(f"🔧 TOOL START: get_YFin_data_online | Agent: Market Analyst | Symbol: {symbol} | Range: {start_date} to {end_date}")
|
||||
|
||||
try:
|
||||
datetime.strptime(start_date, "%Y-%m-%d")
|
||||
datetime.strptime(end_date, "%Y-%m-%d")
|
||||
|
||||
datetime.strptime(start_date, "%Y-%m-%d")
|
||||
datetime.strptime(end_date, "%Y-%m-%d")
|
||||
# Create ticker object
|
||||
ticker = yf.Ticker(symbol.upper())
|
||||
|
||||
# Create ticker object
|
||||
ticker = yf.Ticker(symbol.upper())
|
||||
# Fetch historical data for the specified date range
|
||||
data = ticker.history(start=start_date, end=end_date)
|
||||
|
||||
# Fetch historical data for the specified date range
|
||||
data = ticker.history(start=start_date, end=end_date)
|
||||
# Check if data is empty
|
||||
if data.empty:
|
||||
result = f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
|
||||
else:
|
||||
# Remove timezone info from index for cleaner output
|
||||
if data.index.tz is not None:
|
||||
data.index = data.index.tz_localize(None)
|
||||
|
||||
# Check if data is empty
|
||||
if data.empty:
|
||||
return (
|
||||
f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
|
||||
)
|
||||
# Round numerical values to 2 decimal places for cleaner display
|
||||
numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
|
||||
for col in numeric_columns:
|
||||
if col in data.columns:
|
||||
data[col] = data[col].round(2)
|
||||
|
||||
# Remove timezone info from index for cleaner output
|
||||
if data.index.tz is not None:
|
||||
data.index = data.index.tz_localize(None)
|
||||
# Convert DataFrame to CSV string
|
||||
csv_string = data.to_csv()
|
||||
|
||||
# Round numerical values to 2 decimal places for cleaner display
|
||||
numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
|
||||
for col in numeric_columns:
|
||||
if col in data.columns:
|
||||
data[col] = data[col].round(2)
|
||||
# Add header information
|
||||
header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
|
||||
header += f"# Total records: {len(data)}\n"
|
||||
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||||
|
||||
# Convert DataFrame to CSV string
|
||||
csv_string = data.to_csv()
|
||||
|
||||
# Add header information
|
||||
header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
|
||||
header += f"# Total records: {len(data)}\n"
|
||||
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||||
|
||||
return header + csv_string
|
||||
result = header + csv_string
|
||||
|
||||
# Enhanced logging - Success
|
||||
duration = time.time() - start_time
|
||||
logger.info(f"✅ TOOL SUCCESS: get_YFin_data_online | Duration: {duration:.2f}s | Records: {len(data) if not data.empty else 0}")
|
||||
logger.info(f"📋 TOOL OUTPUT LENGTH: {len(result)} characters")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
# Enhanced logging - Error (for comparison)
|
||||
duration = time.time() - start_time
|
||||
logger.error(f"❌ TOOL ERROR: get_YFin_data_online | Duration: {duration:.2f}s")
|
||||
logger.error(f"🚨 FULL ERROR DETAILS: {type(e).__name__}: {str(e)}")
|
||||
raise e
|
||||
|
||||
|
||||
def get_YFin_data(
|
||||
|
|
@ -703,12 +748,27 @@ def get_YFin_data(
|
|||
|
||||
|
||||
def get_stock_news_openai(ticker, curr_date):
|
||||
config = get_config()
|
||||
client = OpenAI(base_url=config["backend_url"])
|
||||
|
||||
response = client.responses.create(
|
||||
model=config["quick_think_llm"],
|
||||
input=[
|
||||
import logging
|
||||
import time
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Import shared client functions from api.py
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
|
||||
from api import get_shared_openai_client, get_compatible_model_for_tools
|
||||
|
||||
# Use shared client and compatible model
|
||||
client = get_shared_openai_client()
|
||||
model = get_compatible_model_for_tools()
|
||||
|
||||
# Enhanced logging - Tool entry
|
||||
start_time = time.time()
|
||||
logger.info(f"🔧 TOOL START: get_stock_news_openai | Agent: Social Media Analyst | Ticker: {ticker} | Date: {curr_date} | Model: {model}")
|
||||
|
||||
request_params = {
|
||||
"model": model,
|
||||
"input": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": [
|
||||
|
|
@ -719,31 +779,68 @@ def get_stock_news_openai(ticker, curr_date):
|
|||
],
|
||||
}
|
||||
],
|
||||
text={"format": {"type": "text"}},
|
||||
reasoning={},
|
||||
tools=[
|
||||
"text": {"format": {"type": "text"}},
|
||||
"reasoning": {},
|
||||
"tools": [
|
||||
{
|
||||
"type": "web_search_preview",
|
||||
"user_location": {"type": "approximate"},
|
||||
"search_context_size": "low",
|
||||
}
|
||||
],
|
||||
temperature=1,
|
||||
max_output_tokens=4096,
|
||||
top_p=1,
|
||||
store=True,
|
||||
)
|
||||
"temperature": 1,
|
||||
"max_output_tokens": 4096,
|
||||
"top_p": 1,
|
||||
"store": True,
|
||||
}
|
||||
|
||||
# Log full request parameters
|
||||
logger.info(f"📤 TOOL REQUEST PARAMS: {request_params}")
|
||||
|
||||
return response.output[1].content[0].text
|
||||
try:
|
||||
response = client.responses.create(**request_params)
|
||||
|
||||
# Enhanced logging - Success
|
||||
duration = time.time() - start_time
|
||||
logger.info(f"✅ TOOL SUCCESS: get_stock_news_openai | Duration: {duration:.2f}s")
|
||||
logger.info(f"📥 TOOL RESPONSE STRUCTURE: {type(response)} | Available attrs: {dir(response)}")
|
||||
|
||||
result = response.output[1].content[0].text
|
||||
logger.info(f"📋 TOOL OUTPUT LENGTH: {len(result)} characters")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
# Enhanced logging - Error
|
||||
duration = time.time() - start_time
|
||||
logger.error(f"❌ TOOL ERROR: get_stock_news_openai | Duration: {duration:.2f}s")
|
||||
logger.error(f"🚨 FULL ERROR DETAILS: {type(e).__name__}: {str(e)}")
|
||||
if hasattr(e, 'response'):
|
||||
logger.error(f"🔍 ERROR RESPONSE: {e.response.text if hasattr(e.response, 'text') else 'No response text'}")
|
||||
raise e
|
||||
|
||||
|
||||
def get_global_news_openai(curr_date):
|
||||
config = get_config()
|
||||
client = OpenAI(base_url=config["backend_url"])
|
||||
|
||||
response = client.responses.create(
|
||||
model=config["quick_think_llm"],
|
||||
input=[
|
||||
import logging
|
||||
import time
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Import shared client functions from api.py
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
|
||||
from api import get_shared_openai_client, get_compatible_model_for_tools
|
||||
|
||||
# Use shared client and compatible model
|
||||
client = get_shared_openai_client()
|
||||
model = get_compatible_model_for_tools()
|
||||
|
||||
# Enhanced logging - Tool entry
|
||||
start_time = time.time()
|
||||
logger.info(f"🔧 TOOL START: get_global_news_openai | Agent: News Analyst | Date: {curr_date} | Model: {model}")
|
||||
|
||||
request_params = {
|
||||
"model": model,
|
||||
"input": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": [
|
||||
|
|
@ -754,31 +851,68 @@ def get_global_news_openai(curr_date):
|
|||
],
|
||||
}
|
||||
],
|
||||
text={"format": {"type": "text"}},
|
||||
reasoning={},
|
||||
tools=[
|
||||
"text": {"format": {"type": "text"}},
|
||||
"reasoning": {},
|
||||
"tools": [
|
||||
{
|
||||
"type": "web_search_preview",
|
||||
"user_location": {"type": "approximate"},
|
||||
"search_context_size": "low",
|
||||
}
|
||||
],
|
||||
temperature=1,
|
||||
max_output_tokens=4096,
|
||||
top_p=1,
|
||||
store=True,
|
||||
)
|
||||
"temperature": 1,
|
||||
"max_output_tokens": 4096,
|
||||
"top_p": 1,
|
||||
"store": True,
|
||||
}
|
||||
|
||||
# Log full request parameters
|
||||
logger.info(f"📤 TOOL REQUEST PARAMS: {request_params}")
|
||||
|
||||
return response.output[1].content[0].text
|
||||
try:
|
||||
response = client.responses.create(**request_params)
|
||||
|
||||
# Enhanced logging - Success
|
||||
duration = time.time() - start_time
|
||||
logger.info(f"✅ TOOL SUCCESS: get_global_news_openai | Duration: {duration:.2f}s")
|
||||
logger.info(f"📥 TOOL RESPONSE STRUCTURE: {type(response)} | Available attrs: {dir(response)}")
|
||||
|
||||
result = response.output[1].content[0].text
|
||||
logger.info(f"📋 TOOL OUTPUT LENGTH: {len(result)} characters")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
# Enhanced logging - Error
|
||||
duration = time.time() - start_time
|
||||
logger.error(f"❌ TOOL ERROR: get_global_news_openai | Duration: {duration:.2f}s")
|
||||
logger.error(f"🚨 FULL ERROR DETAILS: {type(e).__name__}: {str(e)}")
|
||||
if hasattr(e, 'response'):
|
||||
logger.error(f"🔍 ERROR RESPONSE: {e.response.text if hasattr(e.response, 'text') else 'No response text'}")
|
||||
raise e
|
||||
|
||||
|
||||
def get_fundamentals_openai(ticker, curr_date):
|
||||
config = get_config()
|
||||
client = OpenAI(base_url=config["backend_url"])
|
||||
|
||||
response = client.responses.create(
|
||||
model=config["quick_think_llm"],
|
||||
input=[
|
||||
import logging
|
||||
import time
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Import shared client functions from api.py
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
|
||||
from api import get_shared_openai_client, get_compatible_model_for_tools
|
||||
|
||||
# Use shared client and compatible model
|
||||
client = get_shared_openai_client()
|
||||
model = get_compatible_model_for_tools()
|
||||
|
||||
# Enhanced logging - Tool entry
|
||||
start_time = time.time()
|
||||
logger.info(f"🔧 TOOL START: get_fundamentals_openai | Agent: Fundamentals Analyst | Ticker: {ticker} | Date: {curr_date} | Model: {model}")
|
||||
|
||||
request_params = {
|
||||
"model": model,
|
||||
"input": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": [
|
||||
|
|
@ -789,19 +923,41 @@ def get_fundamentals_openai(ticker, curr_date):
|
|||
],
|
||||
}
|
||||
],
|
||||
text={"format": {"type": "text"}},
|
||||
reasoning={},
|
||||
tools=[
|
||||
"text": {"format": {"type": "text"}},
|
||||
"reasoning": {},
|
||||
"tools": [
|
||||
{
|
||||
"type": "web_search_preview",
|
||||
"user_location": {"type": "approximate"},
|
||||
"search_context_size": "low",
|
||||
}
|
||||
],
|
||||
temperature=1,
|
||||
max_output_tokens=4096,
|
||||
top_p=1,
|
||||
store=True,
|
||||
)
|
||||
"temperature": 1,
|
||||
"max_output_tokens": 4096,
|
||||
"top_p": 1,
|
||||
"store": True,
|
||||
}
|
||||
|
||||
# Log full request parameters
|
||||
logger.info(f"📤 TOOL REQUEST PARAMS: {request_params}")
|
||||
|
||||
return response.output[1].content[0].text
|
||||
try:
|
||||
response = client.responses.create(**request_params)
|
||||
|
||||
# Enhanced logging - Success
|
||||
duration = time.time() - start_time
|
||||
logger.info(f"✅ TOOL SUCCESS: get_fundamentals_openai | Duration: {duration:.2f}s")
|
||||
logger.info(f"📥 TOOL RESPONSE STRUCTURE: {type(response)} | Available attrs: {dir(response)}")
|
||||
|
||||
result = response.output[1].content[0].text
|
||||
logger.info(f"📋 TOOL OUTPUT LENGTH: {len(result)} characters")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
# Enhanced logging - Error
|
||||
duration = time.time() - start_time
|
||||
logger.error(f"❌ TOOL ERROR: get_fundamentals_openai | Duration: {duration:.2f}s")
|
||||
logger.error(f"🚨 FULL ERROR DETAILS: {type(e).__name__}: {str(e)}")
|
||||
if hasattr(e, 'response'):
|
||||
logger.error(f"🔍 ERROR RESPONSE: {e.response.text if hasattr(e.response, 'text') else 'No response text'}")
|
||||
raise e
|
||||
|
|
@ -0,0 +1,211 @@
|
|||
import pandas as pd
|
||||
import yfinance as yf
|
||||
from stockstats import wrap
|
||||
from typing import Annotated
|
||||
import os
|
||||
import re
|
||||
from .config import get_config
|
||||
|
||||
|
||||
class StockstatsUtils:
|
||||
@staticmethod
|
||||
def clean_date_data(data):
|
||||
"""Clean malformed date data where years might be duplicated and ensure proper datetime format"""
|
||||
if 'Date' in data.columns:
|
||||
# Convert Date column to string if it's not already
|
||||
data['Date'] = data['Date'].astype(str)
|
||||
|
||||
# Fix malformed dates like "20182018-04-02" -> "2018-04-02"
|
||||
# Pattern: match YYYY + YYYY + rest of date, capture the first year and rest
|
||||
pattern = r'(\d{4})\1(.*)$' # \1 refers to the first captured group (year)
|
||||
data['Date'] = data['Date'].str.replace(pattern, r'\1\2', regex=True)
|
||||
|
||||
# Also handle any other malformed date patterns
|
||||
# Remove any duplicate year patterns more broadly
|
||||
data['Date'] = data['Date'].str.replace(r'(\d{4})(\d{4})-', r'\1-', regex=True)
|
||||
|
||||
# Now convert to proper datetime format
|
||||
try:
|
||||
data['Date'] = pd.to_datetime(data['Date'], errors='coerce')
|
||||
# Drop any rows where date conversion failed (NaT values)
|
||||
data = data.dropna(subset=['Date'])
|
||||
|
||||
# Ensure we have a proper datetime column
|
||||
if not pd.api.types.is_datetime64_any_dtype(data['Date']):
|
||||
# If still not datetime, try alternative parsing
|
||||
data['Date'] = pd.to_datetime(data['Date'], format='%Y-%m-%d', errors='coerce')
|
||||
data = data.dropna(subset=['Date'])
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not convert Date column to datetime: {e}")
|
||||
# Fall back to string format but ensure consistency
|
||||
data['Date'] = data['Date'].astype(str)
|
||||
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def prepare_data_for_stockstats(data):
|
||||
"""Prepare data specifically for stockstats processing"""
|
||||
if data is None or data.empty:
|
||||
return data
|
||||
|
||||
# Ensure we have the required columns for stockstats
|
||||
required_columns = ['Open', 'High', 'Low', 'Close', 'Volume']
|
||||
missing_columns = [col for col in required_columns if col not in data.columns]
|
||||
|
||||
if missing_columns:
|
||||
print(f"Warning: Missing required columns for stockstats: {missing_columns}")
|
||||
return data
|
||||
|
||||
# Clean and prepare data
|
||||
data = StockstatsUtils.clean_date_data(data)
|
||||
|
||||
# If Date is still not datetime, convert it
|
||||
if 'Date' in data.columns and not pd.api.types.is_datetime64_any_dtype(data['Date']):
|
||||
try:
|
||||
data['Date'] = pd.to_datetime(data['Date'], errors='coerce')
|
||||
data = data.dropna(subset=['Date'])
|
||||
except Exception as e:
|
||||
print(f"Final datetime conversion failed: {e}")
|
||||
|
||||
# Sort by date to ensure proper order
|
||||
if 'Date' in data.columns:
|
||||
data = data.sort_values('Date').reset_index(drop=True)
|
||||
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def get_stock_stats(
|
||||
symbol: Annotated[str, "ticker symbol for the company"],
|
||||
indicator: Annotated[
|
||||
str, "quantitative indicators based off of the stock data for the company"
|
||||
],
|
||||
curr_date: Annotated[
|
||||
str, "curr date for retrieving stock price data, YYYY-mm-dd"
|
||||
],
|
||||
data_dir: Annotated[
|
||||
str,
|
||||
"directory where the stock data is stored.",
|
||||
],
|
||||
online: Annotated[
|
||||
bool,
|
||||
"whether to use online tools to fetch data or offline tools. If True, will use online tools.",
|
||||
] = False,
|
||||
):
|
||||
df = None
|
||||
data = None
|
||||
|
||||
if not online:
|
||||
try:
|
||||
data = pd.read_csv(
|
||||
os.path.join(
|
||||
data_dir,
|
||||
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
|
||||
)
|
||||
)
|
||||
# Prepare data for stockstats processing
|
||||
data = StockstatsUtils.prepare_data_for_stockstats(data)
|
||||
|
||||
if data.empty:
|
||||
return "Error: No valid data after cleaning"
|
||||
|
||||
df = wrap(data)
|
||||
except FileNotFoundError:
|
||||
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
|
||||
except Exception as e:
|
||||
print(f"Error processing offline data for {symbol}: {e}")
|
||||
return f"Error: {str(e)}"
|
||||
else:
|
||||
# Get today's date as YYYY-mm-dd to add to cache
|
||||
today_date = pd.Timestamp.today()
|
||||
curr_date_dt = pd.to_datetime(curr_date)
|
||||
|
||||
end_date = today_date
|
||||
start_date = today_date - pd.DateOffset(years=15)
|
||||
start_date = start_date.strftime("%Y-%m-%d")
|
||||
end_date = end_date.strftime("%Y-%m-%d")
|
||||
|
||||
# Get config and ensure cache directory exists
|
||||
config = get_config()
|
||||
os.makedirs(config["data_cache_dir"], exist_ok=True)
|
||||
|
||||
data_file = os.path.join(
|
||||
config["data_cache_dir"],
|
||||
f"{symbol}-YFin-data-{start_date}-{end_date}.csv",
|
||||
)
|
||||
|
||||
if os.path.exists(data_file):
|
||||
try:
|
||||
data = pd.read_csv(data_file)
|
||||
# Prepare data for stockstats processing
|
||||
data = StockstatsUtils.prepare_data_for_stockstats(data)
|
||||
|
||||
if data.empty:
|
||||
print(f"No valid data found in cache for {symbol}")
|
||||
# Remove the corrupted cache file and re-download
|
||||
os.remove(data_file)
|
||||
return StockstatsUtils.get_stock_stats(symbol, indicator, curr_date, data_dir, online=True)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error reading cached data for {symbol}: {e}")
|
||||
# Remove corrupted cache file and retry
|
||||
if os.path.exists(data_file):
|
||||
os.remove(data_file)
|
||||
return StockstatsUtils.get_stock_stats(symbol, indicator, curr_date, data_dir, online=True)
|
||||
else:
|
||||
try:
|
||||
data = yf.download(
|
||||
symbol,
|
||||
start=start_date,
|
||||
end=end_date,
|
||||
multi_level_index=False,
|
||||
progress=False,
|
||||
auto_adjust=True,
|
||||
)
|
||||
if data.empty:
|
||||
return f"Error: No data available for {symbol}"
|
||||
|
||||
data = data.reset_index()
|
||||
data.to_csv(data_file, index=False)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error downloading data for {symbol}: {e}")
|
||||
return f"Error: {str(e)}"
|
||||
|
||||
# Prepare data for stockstats processing
|
||||
data = StockstatsUtils.prepare_data_for_stockstats(data)
|
||||
|
||||
if data.empty:
|
||||
return "Error: No valid data after cleaning"
|
||||
|
||||
try:
|
||||
df = wrap(data)
|
||||
# Convert curr_date back to string format for comparison
|
||||
curr_date = curr_date_dt.strftime("%Y-%m-%d")
|
||||
except Exception as e:
|
||||
print(f"Error wrapping data with stockstats for {symbol}: {e}")
|
||||
return f"Error: Failed to process data with stockstats - {str(e)}"
|
||||
|
||||
try:
|
||||
# Trigger stockstats to calculate the indicator
|
||||
df[indicator]
|
||||
|
||||
# Convert Date column to string for comparison if it's datetime
|
||||
if 'Date' in df.columns and pd.api.types.is_datetime64_any_dtype(df['Date']):
|
||||
date_strings = df['Date'].dt.strftime('%Y-%m-%d')
|
||||
matching_rows = df[date_strings == curr_date]
|
||||
else:
|
||||
# If Date is already string, use string comparison
|
||||
matching_rows = df[df["Date"].astype(str).str.startswith(curr_date)]
|
||||
|
||||
if not matching_rows.empty:
|
||||
indicator_value = matching_rows[indicator].values[0]
|
||||
return indicator_value
|
||||
else:
|
||||
return "N/A: Not a trading day (weekend or holiday)"
|
||||
except KeyError as e:
|
||||
print(f"Error: Indicator '{indicator}' not found. Available indicators: {list(df.columns)}")
|
||||
return f"Error: Invalid indicator '{indicator}'"
|
||||
except Exception as e:
|
||||
print(f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}")
|
||||
return f"Error: {str(e)}"
|
||||
|
|
@ -1,13 +1,20 @@
|
|||
import os
|
||||
|
||||
# Get the backend directory (parent of tradingagents package)
|
||||
BACKEND_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
|
||||
PROJECT_ROOT = os.path.abspath(os.path.join(BACKEND_DIR, ".."))
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
|
||||
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
|
||||
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", os.path.join(BACKEND_DIR, "results")),
|
||||
"data_dir": os.getenv("TRADINGAGENTS_DATA_DIR", os.path.join(BACKEND_DIR, "data")),
|
||||
"data_cache_dir": os.path.join(
|
||||
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"dataflows/data_cache",
|
||||
),
|
||||
# API Server settings
|
||||
"api_host": os.getenv("TRADINGAGENTS_API_HOST", "localhost"),
|
||||
"api_port": int(os.getenv("TRADINGAGENTS_API_PORT", "8000")),
|
||||
# LLM settings
|
||||
"llm_provider": "openai",
|
||||
"deep_think_llm": "o4-mini",
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Colors for output
|
||||
GREEN='\033[0;32m'
|
||||
RED='\033[0;31m'
|
||||
YELLOW='\033[1;33m'
|
||||
NC='\033[0m' # No Color
|
||||
|
||||
echo "🚀 TradingAgents API Verification"
|
||||
echo "================================"
|
||||
|
||||
# Check if server is running
|
||||
echo -n "Checking if API server is running... "
|
||||
if curl -s http://localhost:8000/ > /dev/null; then
|
||||
echo -e "${GREEN}✓ Server is running${NC}"
|
||||
else
|
||||
echo -e "${RED}✗ Server is not running${NC}"
|
||||
echo -e "${YELLOW}Please start the server with: python run_api.py${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Test root endpoint
|
||||
echo -n "Testing root endpoint... "
|
||||
ROOT_RESPONSE=$(curl -s http://localhost:8000/)
|
||||
if [[ $ROOT_RESPONSE == *"TradingAgents API is running"* ]]; then
|
||||
echo -e "${GREEN}✓ OK${NC}"
|
||||
else
|
||||
echo -e "${RED}✗ Failed${NC}"
|
||||
fi
|
||||
|
||||
# Test health endpoint
|
||||
echo -n "Testing health endpoint... "
|
||||
HEALTH_RESPONSE=$(curl -s http://localhost:8000/health)
|
||||
if [[ $HEALTH_RESPONSE == *"healthy"* ]]; then
|
||||
echo -e "${GREEN}✓ OK${NC}"
|
||||
else
|
||||
echo -e "${RED}✗ Failed${NC}"
|
||||
fi
|
||||
|
||||
# Check environment variables
|
||||
echo ""
|
||||
echo "Checking environment variables:"
|
||||
if [ -z "$OPENAI_API_KEY" ]; then
|
||||
echo -e "${RED}✗ OPENAI_API_KEY not set${NC}"
|
||||
else
|
||||
echo -e "${GREEN}✓ OPENAI_API_KEY is set${NC}"
|
||||
fi
|
||||
|
||||
if [ -z "$FINNHUB_API_KEY" ]; then
|
||||
echo -e "${RED}✗ FINNHUB_API_KEY not set${NC}"
|
||||
else
|
||||
echo -e "${GREEN}✓ FINNHUB_API_KEY is set${NC}"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "================================"
|
||||
echo -e "${GREEN}API verification complete!${NC}"
|
||||
echo ""
|
||||
echo "Next steps:"
|
||||
echo "1. Visit http://localhost:8000/docs for interactive API docs"
|
||||
echo "2. Run 'python test_api.py' for comprehensive testing"
|
||||
echo "3. Test from iOS app or use curl commands"
|
||||
|
|
@ -0,0 +1,153 @@
|
|||
# TradingAgents FastAPI Setup Guide
|
||||
|
||||
## Overview
|
||||
This guide explains how to run TradingAgents as a FastAPI server and integrate it with your Swift app.
|
||||
|
||||
## Prerequisites
|
||||
- Python 3.8+
|
||||
- API keys for OpenAI (or other LLM providers)
|
||||
- Swift/SwiftUI project with ReSwift
|
||||
|
||||
## Python Server Setup
|
||||
|
||||
### 1. Install Dependencies
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
### 2. Set Environment Variables
|
||||
Create a `.env` file in the project root:
|
||||
```bash
|
||||
# Required
|
||||
OPENAI_API_KEY=your_openai_api_key_here
|
||||
|
||||
# Optional (defaults shown)
|
||||
DEEP_THINK_MODEL=gpt-4o-mini
|
||||
QUICK_THINK_MODEL=gpt-4o-mini
|
||||
BACKEND_URL=https://api.openai.com/v1
|
||||
```
|
||||
|
||||
### 3. Run the FastAPI Server
|
||||
```bash
|
||||
python run_api.py
|
||||
```
|
||||
|
||||
The server will start at `http://localhost:8000`
|
||||
|
||||
### 4. Test the API
|
||||
```bash
|
||||
# Test health endpoint
|
||||
curl http://localhost:8000/health
|
||||
|
||||
# Test analysis endpoint
|
||||
curl -X POST http://localhost:8000/analyze \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"ticker": "AAPL"}'
|
||||
```
|
||||
|
||||
## Swift Integration
|
||||
|
||||
### 1. Add ReSwift to Your Project
|
||||
In Xcode, go to File → Add Package Dependencies and add:
|
||||
```
|
||||
https://github.com/ReSwift/ReSwift
|
||||
```
|
||||
|
||||
### 2. Copy Swift Files
|
||||
Copy these files to your Swift project:
|
||||
- `TradingAgents_Swift_Integration.swift` - Redux architecture and networking
|
||||
- `TradingAnalysisView.swift` - SwiftUI views
|
||||
|
||||
### 3. Update Your App
|
||||
In your main app file, initialize the store and show the view:
|
||||
|
||||
```swift
|
||||
import SwiftUI
|
||||
import ReSwift
|
||||
|
||||
@main
|
||||
struct YourApp: App {
|
||||
var body: some Scene {
|
||||
WindowGroup {
|
||||
TradingAnalysisView()
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 4. Configure API URL
|
||||
If running on a real device or different network, update the base URL in `TradingAgentsAPIService`:
|
||||
```swift
|
||||
private let baseURL = "http://your-server-ip:8000"
|
||||
```
|
||||
|
||||
## API Endpoints
|
||||
|
||||
### POST /analyze
|
||||
Analyzes a stock ticker.
|
||||
|
||||
**Request:**
|
||||
```json
|
||||
{
|
||||
"ticker": "AAPL"
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"ticker": "AAPL",
|
||||
"analysis_date": "2024-01-15",
|
||||
"market_report": "Technical analysis...",
|
||||
"sentiment_report": "Social sentiment analysis...",
|
||||
"news_report": "Recent news analysis...",
|
||||
"fundamentals_report": "Fundamental analysis...",
|
||||
"investment_plan": "Research team recommendation...",
|
||||
"trader_investment_plan": "Trading strategy...",
|
||||
"final_trade_decision": "Final decision...",
|
||||
"processed_signal": "BUY"
|
||||
}
|
||||
```
|
||||
|
||||
## Production Deployment
|
||||
|
||||
### Docker (Recommended)
|
||||
Create a `Dockerfile`:
|
||||
```dockerfile
|
||||
FROM python:3.9-slim
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
COPY requirements.txt .
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
|
||||
COPY . .
|
||||
|
||||
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]
|
||||
```
|
||||
|
||||
Build and run:
|
||||
```bash
|
||||
docker build -t tradingagents-api .
|
||||
docker run -p 8000:8000 --env-file .env tradingagents-api
|
||||
```
|
||||
|
||||
### Security Considerations
|
||||
1. Use HTTPS in production
|
||||
2. Add API authentication
|
||||
3. Implement rate limiting
|
||||
4. Validate and sanitize inputs
|
||||
5. Use environment-specific configurations
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
1. **Import errors**: Ensure all dependencies are installed
|
||||
2. **API key errors**: Check your `.env` file
|
||||
3. **Connection refused**: Verify the server is running and accessible
|
||||
4. **CORS errors**: Check CORS configuration matches your Swift app's needs
|
||||
|
||||
### Performance Tips
|
||||
- The analysis can take 30-60 seconds depending on the LLM models
|
||||
- Consider implementing caching for repeated requests
|
||||
- Use background processing for long-running analyses
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
# Trading Agents Documentation
|
||||
|
||||
This directory contains all project documentation.
|
||||
|
||||
## Available Documents
|
||||
|
||||
### Project Documentation
|
||||
- **`PRD.md`** - Product Requirements Document
|
||||
- Project overview and objectives
|
||||
- System architecture
|
||||
- Feature specifications
|
||||
|
||||
- **`DOCUMENTATION.md`** - Technical Documentation
|
||||
- Detailed system design
|
||||
- Component descriptions
|
||||
- Integration guides
|
||||
|
||||
### Setup Guides
|
||||
- **`FASTAPI_SETUP.md`** - FastAPI Server Setup
|
||||
- API endpoint documentation
|
||||
- Server configuration
|
||||
- Deployment instructions
|
||||
|
||||
- **`SIMPLIFIED_CLI_GUIDE.md`** - CLI Usage Guide
|
||||
- Command-line interface tutorial
|
||||
- Available commands
|
||||
- Usage examples
|
||||
|
||||
### Development
|
||||
- **`TODO.md`** - Project Roadmap
|
||||
- Planned features
|
||||
- Known issues
|
||||
- Future improvements
|
||||
|
||||
## Quick Links
|
||||
|
||||
- [Backend Setup](../backend/README.md)
|
||||
- [iOS App Setup](../ios/README.md)
|
||||
- [Main Project README](../README.md)
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
import pandas as pd
|
||||
import yfinance as yf
|
||||
from stockstats import wrap
|
||||
from typing import Annotated
|
||||
import os
|
||||
from .config import get_config
|
||||
|
||||
|
||||
class StockstatsUtils:
|
||||
@staticmethod
|
||||
def get_stock_stats(
|
||||
symbol: Annotated[str, "ticker symbol for the company"],
|
||||
indicator: Annotated[
|
||||
str, "quantitative indicators based off of the stock data for the company"
|
||||
],
|
||||
curr_date: Annotated[
|
||||
str, "curr date for retrieving stock price data, YYYY-mm-dd"
|
||||
],
|
||||
data_dir: Annotated[
|
||||
str,
|
||||
"directory where the stock data is stored.",
|
||||
],
|
||||
online: Annotated[
|
||||
bool,
|
||||
"whether to use online tools to fetch data or offline tools. If True, will use online tools.",
|
||||
] = False,
|
||||
):
|
||||
df = None
|
||||
data = None
|
||||
|
||||
if not online:
|
||||
try:
|
||||
data = pd.read_csv(
|
||||
os.path.join(
|
||||
data_dir,
|
||||
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
|
||||
)
|
||||
)
|
||||
df = wrap(data)
|
||||
except FileNotFoundError:
|
||||
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
|
||||
else:
|
||||
# Get today's date as YYYY-mm-dd to add to cache
|
||||
today_date = pd.Timestamp.today()
|
||||
curr_date = pd.to_datetime(curr_date)
|
||||
|
||||
end_date = today_date
|
||||
start_date = today_date - pd.DateOffset(years=15)
|
||||
start_date = start_date.strftime("%Y-%m-%d")
|
||||
end_date = end_date.strftime("%Y-%m-%d")
|
||||
|
||||
# Get config and ensure cache directory exists
|
||||
config = get_config()
|
||||
os.makedirs(config["data_cache_dir"], exist_ok=True)
|
||||
|
||||
data_file = os.path.join(
|
||||
config["data_cache_dir"],
|
||||
f"{symbol}-YFin-data-{start_date}-{end_date}.csv",
|
||||
)
|
||||
|
||||
if os.path.exists(data_file):
|
||||
data = pd.read_csv(data_file)
|
||||
data["Date"] = pd.to_datetime(data["Date"])
|
||||
else:
|
||||
data = yf.download(
|
||||
symbol,
|
||||
start=start_date,
|
||||
end=end_date,
|
||||
multi_level_index=False,
|
||||
progress=False,
|
||||
auto_adjust=True,
|
||||
)
|
||||
data = data.reset_index()
|
||||
data.to_csv(data_file, index=False)
|
||||
|
||||
df = wrap(data)
|
||||
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
|
||||
curr_date = curr_date.strftime("%Y-%m-%d")
|
||||
|
||||
df[indicator] # trigger stockstats to calculate the indicator
|
||||
matching_rows = df[df["Date"].str.startswith(curr_date)]
|
||||
|
||||
if not matching_rows.empty:
|
||||
indicator_value = matching_rows[indicator].values[0]
|
||||
return indicator_value
|
||||
else:
|
||||
return "N/A: Not a trading day (weekend or holiday)"
|
||||
Loading…
Reference in New Issue