diff --git a/.DS_Store b/.DS_Store index e5103904673dc8d5319dd125025bfa24e5f2d040..28a1e85076b072467bf7fa442f3da18582961b4d 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index 4c49bd78f1d08f2bc09fa0bd8191ed38b7dce5e3..5e18e59b075fca7562e0fbc8ddf9e9ede63bd9d1 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,34 @@ + .env + +venv/ +ai_env/ +.venv/ + +__pycache__/ +*.pyc +*.pyo +*.pyd + +build/ +dist/ +*.egg-info/ + +frontend/node_modules/ + +frontend/dist/ + +frontend/npm-debug.log* +frontend/yarn-debug.log* +frontend/yarn-error.log* + + +.vscode/ + +.DS_Store + + +data/ +mlruns/ + +tmp_download.py \ No newline at end of file diff --git a/README.md b/README.md index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..a9c4a4fad3f31999b43446510678637216d46b1f 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,52 @@ +# Quantitative Analysis Platform +> A multi-agent AI system designed to provide retail investors with hedge-fund-level research and insights + + +## The Problem +Retail investors are at a massive disadvantage. They lack the sophisticated tools, real-time data aggregation, and unbiased analysis that hedge funds use daily. Decisions are often driven by emotion and noise from social media, leading to poor outcomes. This platform was built to bridge that gap. + +## Features +- **Multi-Agent Pipeline:** A robust, asynchronous backend where specialized AI agents collaborate to build a complete analysis. +- **Data Agent:** Fetches real-time, comprehensive financial data for any stock. +- **Intelligence Agent:** Scrapes Google News, Yahoo Finance, and Reddit to gather and analyze market sentiment. +- **LLM Analyst Agent:** Utilizes **Google's Gemini 1.5 Flash** to analyze all collected data, identify trends, and generate a human-like investment thesis with a forecast and actionable strategy. +- **Interactive Dashboard:** A clean, modern React frontend to visualize the data, including news feeds and historical price charts. +- **Job History:** Users can view and revisit all their past analyses. + + +## Tech Stack & Architecture + +### Frontend +- **React (Vite):** For a fast and modern user interface. +- **Tailwind CSS:** For professional and responsive styling. +- **Recharts:** For beautiful and interactive data visualizations. +- **Axios:** For seamless communication with the backend API. + +### Backend +- **FastAPI:** A high-performance Python framework for building the API. +- **Celery & Redis:** To manage the asynchronous, multi-step agent pipeline, ensuring the UI is always fast and responsive. +- **PostgreSQL (Neon):** A scalable, serverless cloud database for storing job data. +- **SQLAlchemy & Alembic:** For robust database interaction and schema migrations. +- **LangChain & Google Gemini 1.5 Flash:** The core AI engine for the Analyst Agent. + + +### Architecture +```mermaid +graph TD + A[User on React Frontend] -->|1. POST /jobs (ticker)| B(FastAPI Backend); + B -->|2. Dispatch Task| C[Redis Queue]; + C -->|3. Pick up Job| D(Celery Worker); + D -->|4. Run Pipeline| E[Agent 1: Data]; + E -->|5. Update DB| F[(Neon DB)]; + D -->|6. Run Pipeline| G[Agent 2: Intelligence]; + G -->|7. Update DB| F; + D -->|8. Run Pipeline| H[Agent 3: LLM Analyst]; + H -->|9. Call Gemini API| I{Gemini 1.5 Flash}; + I -->|10. Return Thesis| H; + H -->|11. Final Update| F; + A -->|12. GET /jobs/{id} (Polling)| B; + B -->|13. Read Status/Result| F; +end + +## Local Setup + diff --git a/backend/__pycache__/celery_worker.cpython-311.pyc b/backend/__pycache__/celery_worker.cpython-311.pyc index 4ab86a9976885703ecd22ee27b2c6de6eb3673fb..a0d844e3564119158d38017b2ca9e327f3330157 100644 Binary files a/backend/__pycache__/celery_worker.cpython-311.pyc and b/backend/__pycache__/celery_worker.cpython-311.pyc differ diff --git a/backend/__pycache__/main.cpython-311.pyc b/backend/__pycache__/main.cpython-311.pyc index 8221e37e5c5eafbe85b037a6e52d2b713753e372..f6dbcef8136ff434931854ea2ed66fbff2d80d82 100644 Binary files a/backend/__pycache__/main.cpython-311.pyc and b/backend/__pycache__/main.cpython-311.pyc differ diff --git a/backend/__pycache__/schemas.cpython-311.pyc b/backend/__pycache__/schemas.cpython-311.pyc index 16cc047fd8447f241089ae5d1a4667ad37a2cb78..6a7c1e567e43032602ea86cdcdbd7afa4d0c93a6 100644 Binary files a/backend/__pycache__/schemas.cpython-311.pyc and b/backend/__pycache__/schemas.cpython-311.pyc differ diff --git a/backend/alembic/__pycache__/env.cpython-311.pyc b/backend/alembic/__pycache__/env.cpython-311.pyc deleted file mode 100644 index 0fee41b8a859975969b516ca56b72b2f0965d038..0000000000000000000000000000000000000000 Binary files a/backend/alembic/__pycache__/env.cpython-311.pyc and /dev/null differ diff --git a/backend/alembic/env.py b/backend/alembic/env.py index d669560b3f4c4e376699e3390167674f9097eb95..4fb37aa26140664c68d0107574c8d701af860858 100644 --- a/backend/alembic/env.py +++ b/backend/alembic/env.py @@ -1,68 +1,46 @@ -# backend/alembic/env.py - from logging.config import fileConfig - from sqlalchemy import engine_from_config from sqlalchemy.pool import NullPool - from alembic import context - -# This is the crucial part: we import our app's config and models import sys import os + +# Make sure the app's modules can be found sys.path.insert(0, os.path.realpath(os.path.join(os.path.dirname(__file__), '..'))) from core.config import settings from core.database import Base -from models.analysis_job import AnalysisJob +# Import all your models here so Alembic can see them +from models.analysis_job import AnalysisJob -# this is the Alembic Config object, which provides -# access to the values within the .ini file in use. config = context.config -# This line is IMPORTANT: it tells Alembic to use our app's settings for the DB URL +# Set the database URL from our application's settings config.set_main_option("sqlalchemy.url", settings.DATABASE_URL) -# Interpret the config file for Python logging. -# This line reads the logging configuration from alembic.ini if config.config_file_name is not None: fileConfig(config.config_file_name) -# add your model's MetaData object here -# for 'autogenerate' support +# Set the target metadata for autogenerate target_metadata = Base.metadata def run_migrations_offline() -> None: - """Run migrations in 'offline' mode.""" url = config.get_main_option("sqlalchemy.url") - context.configure( - url=url, - target_metadata=target_metadata, - literal_binds=True, - dialect_opts={"paramstyle": "named"}, - ) - + context.configure(url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={"paramstyle": "named"}) with context.begin_transaction(): context.run_migrations() - def run_migrations_online() -> None: - """Run migrations in 'online' mode.""" connectable = engine_from_config( - config.get_section(config.config_main_section, {}), + config.get_section(config.config_ini_section, {}), prefix="sqlalchemy.", poolclass=NullPool, ) - with connectable.connect() as connection: - context.configure( - connection=connection, target_metadata=target_metadata - ) - + context.configure(connection=connection, target_metadata=target_metadata) with context.begin_transaction(): context.run_migrations() - if context.is_offline_mode(): run_migrations_offline() else: diff --git a/backend/alembic/versions/17ec047335c5_create_analysis_jobs_table.py b/backend/alembic/versions/17ec047335c5_create_analysis_jobs_table.py deleted file mode 100644 index 7c214d6c65c1a422135061daa5c52598e9522cf6..0000000000000000000000000000000000000000 --- a/backend/alembic/versions/17ec047335c5_create_analysis_jobs_table.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Create analysis_jobs table - -Revision ID: 17ec047335c5 -Revises: -Create Date: 2025-09-01 16:55:38.619192 - -""" -from typing import Sequence, Union - -from alembic import op -import sqlalchemy as sa - - -# revision identifiers, used by Alembic. -revision: str = '17ec047335c5' -down_revision: Union[str, Sequence[str], None] = None -branch_labels: Union[str, Sequence[str], None] = None -depends_on: Union[str, Sequence[str], None] = None - - -def upgrade() -> None: - """Upgrade schema.""" - # ### commands auto generated by Alembic - please adjust! ### - op.create_table('analysis_jobs', - sa.Column('id', sa.UUID(), nullable=False), - sa.Column('ticker', sa.String(), nullable=False), - sa.Column('status', sa.String(), nullable=False), - sa.Column('result', sa.JSON(), nullable=True), - sa.PrimaryKeyConstraint('id') - ) - op.create_index(op.f('ix_analysis_jobs_ticker'), 'analysis_jobs', ['ticker'], unique=False) - # ### end Alembic commands ### - - -def downgrade() -> None: - """Downgrade schema.""" - # ### commands auto generated by Alembic - please adjust! ### - op.drop_index(op.f('ix_analysis_jobs_ticker'), table_name='analysis_jobs') - op.drop_table('analysis_jobs') - # ### end Alembic commands ### diff --git a/backend/alembic/versions/7bad611f5ad5_create_initial_tables.py b/backend/alembic/versions/7bad611f5ad5_create_initial_tables.py new file mode 100644 index 0000000000000000000000000000000000000000..025cfd8167c54d4d04c1ab2d53e00c460235c54d --- /dev/null +++ b/backend/alembic/versions/7bad611f5ad5_create_initial_tables.py @@ -0,0 +1,32 @@ +"""Create initial tables + +Revision ID: 7bad611f5ad5 +Revises: +Create Date: 2025-09-03 16:52:00.828276 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = '7bad611f5ad5' +down_revision: Union[str, Sequence[str], None] = None +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # ### commands auto generated by Alembic - please adjust! ### + pass + # ### end Alembic commands ### + + +def downgrade() -> None: + """Downgrade schema.""" + # ### commands auto generated by Alembic - please adjust! ### + pass + # ### end Alembic commands ### diff --git a/backend/alembic/versions/__pycache__/17ec047335c5_create_analysis_jobs_table.cpython-311.pyc b/backend/alembic/versions/__pycache__/17ec047335c5_create_analysis_jobs_table.cpython-311.pyc deleted file mode 100644 index 3446a59e0698a1dd962685664cdbf710ad0278d3..0000000000000000000000000000000000000000 Binary files a/backend/alembic/versions/__pycache__/17ec047335c5_create_analysis_jobs_table.cpython-311.pyc and /dev/null differ diff --git a/backend/celery_worker.py b/backend/celery_worker.py index 9128194005495857fc3cae3393eae1fda8dd8de7..cb2388bdede695be24f16a9e10fd025bfd1bb3ca 100644 --- a/backend/celery_worker.py +++ b/backend/celery_worker.py @@ -1,12 +1,14 @@ from celery import Celery from core.config import settings -# giving app a more descriptive name celery = Celery( "quantitative_analysis_platform", broker=settings.CELERY_BROKER_URL, backend=settings.CELERY_RESULT_BACKEND, - include=["tasks.data_tasks", "tasks.news_tasks"] + # This is the corrected list. We only have one task file now. + include=[ + "tasks.main_task" + ] ) celery.conf.update( @@ -15,5 +17,4 @@ celery.conf.update( result_serializer="json", timezone="UTC", enable_utc=True, -) - +) \ No newline at end of file diff --git a/backend/core/__pycache__/config.cpython-311.pyc b/backend/core/__pycache__/config.cpython-311.pyc index 3fd660735ae9674892a0aad880bfe47c8509cd4c..b3b451cbb9a7ddb6fe482e11d12f6a71f7e77066 100644 Binary files a/backend/core/__pycache__/config.cpython-311.pyc and b/backend/core/__pycache__/config.cpython-311.pyc differ diff --git a/backend/core/__pycache__/database.cpython-311.pyc b/backend/core/__pycache__/database.cpython-311.pyc index 1117c05644767b327d76b9fb91a04d03a1862e63..55d779a490c63fa4dc4fe7bb48cbd89602a95632 100644 Binary files a/backend/core/__pycache__/database.cpython-311.pyc and b/backend/core/__pycache__/database.cpython-311.pyc differ diff --git a/backend/core/config.py b/backend/core/config.py index b3e66c74ae773bee7900c0728ad069c370b99bc4..37ad64ad54cdf85c59ae7593855d34cd781deef9 100644 --- a/backend/core/config.py +++ b/backend/core/config.py @@ -4,6 +4,7 @@ class Settings(BaseSettings): DATABASE_URL: str CELERY_BROKER_URL: str CELERY_RESULT_BACKEND: str + GOOGLE_API_KEY: str model_config = SettingsConfigDict(env_file=".env") diff --git a/backend/core/database.py b/backend/core/database.py index 3464bc62e6949704c4aa3ca3d5970866612b15c8..9550b1821012283182af1dd6e6b956ef930ea359 100644 --- a/backend/core/database.py +++ b/backend/core/database.py @@ -2,6 +2,12 @@ from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker, declarative_base from .config import settings -engine = create_engine(settings.DATABASE_URL) +engine = create_engine( + settings.DATABASE_URL, + pool_recycle=1800, + pool_pre_ping=True, +) + SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + Base = declarative_base() \ No newline at end of file diff --git a/backend/main.py b/backend/main.py index cffc88590fc100d4a7717b01bf783ea258ed815f..82f3c35f694ccfcdb3ab3dd98eec5c9e5638df66 100644 --- a/backend/main.py +++ b/backend/main.py @@ -1,21 +1,91 @@ -# backend/main.py +# from fastapi import FastAPI, Depends, HTTPException +# from fastapi.middleware.cors import CORSMiddleware +# from sqlalchemy.orm import Session +# from uuid import UUID +# import models.analysis_job as model +# import schemas +# from core.database import SessionLocal, engine +# # Import only our single, main task +# from tasks.main_task import run_full_analysis + +# # This ensures tables are created on startup (good for development) +# model.Base.metadata.create_all(bind=engine) + +# app = FastAPI( +# title="Quantitative Analysis Platform API", +# version="0.1.0", +# ) + +# app.add_middleware( +# CORSMiddleware, +# allow_origins=["*"], +# allow_credentials=True, +# allow_methods=["*"], +# allow_headers=["*"], +# ) + +# # Dependency to get a database session for each API request +# def get_db(): +# db = SessionLocal() +# try: +# yield db +# finally: +# db.close() + +# @app.post("/jobs", response_model=schemas.Job, status_code=201) +# def create_analysis_job(job_request: schemas.JobCreate, db: Session = Depends(get_db)): +# """ +# Creates a new analysis job and dispatches the main background task. +# """ +# db_job = model.AnalysisJob(ticker=job_request.ticker.upper()) +# db.add(db_job) +# db.commit() +# db.refresh(db_job) + +# # Dispatch our single, all-in-one analysis task to run in the background +# run_full_analysis.delay(str(db_job.id), db_job.ticker) + +# # Immediately return the created job to the frontend +# return db_job + +# @app.get("/jobs/{job_id}", response_model=schemas.Job) +# def get_job_status(job_id: UUID, db: Session = Depends(get_db)): +# """ +# Allows the frontend to poll for the status and result of an analysis job. +# """ +# db_job = db.query(model.AnalysisJob).filter(model.AnalysisJob.id == job_id).first() +# if db_job is None: +# raise HTTPException(status_code=404, detail="Job not found") +# return db_job + + + + + from fastapi import FastAPI, Depends, HTTPException from fastapi.middleware.cors import CORSMiddleware from sqlalchemy.orm import Session +from sqlalchemy import desc # Import desc for ordering from uuid import UUID -import models.analysis_job as model +from typing import List # Import List for the history endpoint +import models.analysis_job as model import schemas from core.database import SessionLocal, engine -from tasks.data_tasks import run_data_analysis -from tasks.news_tasks import run_intelligence_analysis -from celery import chain +from tasks.main_task import run_full_analysis model.Base.metadata.create_all(bind=engine) -app = FastAPI(title="Quantitative Analysis Platform API", version="0.1.0") + +app = FastAPI( + title="Quantitative Analysis Platform API", + version="0.1.0", +) app.add_middleware( CORSMiddleware, - allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"] + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], ) def get_db(): @@ -32,19 +102,19 @@ def create_analysis_job(job_request: schemas.JobCreate, db: Session = Depends(ge db.commit() db.refresh(db_job) - # THE CRITICAL CHANGE IS HERE - analysis_chain = chain( - run_data_analysis.s(str(db_job.id), db_job.ticker), - # By making the signature immutable, we tell Celery to ignore - # the result of the previous task and only use the arguments we provide. - run_intelligence_analysis.s(str(db_job.id)).set(immutable=True) - ) - analysis_chain.apply_async() + run_full_analysis.delay(str(db_job.id), db_job.ticker) return db_job @app.get("/jobs/{job_id}", response_model=schemas.Job) def get_job_status(job_id: UUID, db: Session = Depends(get_db)): db_job = db.query(model.AnalysisJob).filter(model.AnalysisJob.id == job_id).first() - if db_job is None: raise HTTPException(status_code=404, detail="Job not found") - return db_job \ No newline at end of file + if db_job is None: + raise HTTPException(status_code=404, detail="Job not found") + return db_job + +# --- NEW ENDPOINT FOR HISTORY PANEL --- +@app.get("/jobs", response_model=List[schemas.Job]) +def get_jobs_history(db: Session = Depends(get_db)): + db_jobs = db.query(model.AnalysisJob).order_by(desc(model.AnalysisJob.created_at)).limit(20).all() + return db_jobs \ No newline at end of file diff --git a/backend/models/__pycache__/analysis_job.cpython-311.pyc b/backend/models/__pycache__/analysis_job.cpython-311.pyc index 5202158b957ba1d5a550efeff81ef2770b4f7f66..ba84579d52121e8887e3c53b75be65e0365ecdfe 100644 Binary files a/backend/models/__pycache__/analysis_job.cpython-311.pyc and b/backend/models/__pycache__/analysis_job.cpython-311.pyc differ diff --git a/backend/models/analysis_job.py b/backend/models/analysis_job.py index 8de1a4aec998683b2990b7543de81d4e045d823d..c235fbc6b538c32753296e4e25d008170b54f97c 100644 --- a/backend/models/analysis_job.py +++ b/backend/models/analysis_job.py @@ -1,7 +1,23 @@ -from sqlalchemy import Column, String, JSON +# from sqlalchemy import Column, String, JSON +# from sqlalchemy.dialects.postgresql import UUID +# import uuid +# from core.database import Base + +# class AnalysisJob(Base): +# __tablename__ = "analysis_jobs" + +# id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) +# ticker = Column(String, nullable=False, index=True) +# status = Column(String, default="PENDING", nullable=False) +# result = Column(JSON, nullable=True) + + + +from sqlalchemy import Column, String, JSON, DateTime from sqlalchemy.dialects.postgresql import UUID import uuid from core.database import Base +from datetime import datetime class AnalysisJob(Base): __tablename__ = "analysis_jobs" @@ -9,4 +25,5 @@ class AnalysisJob(Base): id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) ticker = Column(String, nullable=False, index=True) status = Column(String, default="PENDING", nullable=False) - result = Column(JSON, nullable=True) \ No newline at end of file + result = Column(JSON, nullable=True) + created_at = Column(DateTime, default=datetime.utcnow, nullable=False) \ No newline at end of file diff --git a/backend/requirements.txt b/backend/requirements.txt index 2c8f705d5e7a148ffc0a41b62903a0b7968acfeb..2ec52974fafc51210eb449b72ad1ae8dd26bcb8f 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -1,25 +1,37 @@ +# FastAPI and Server fastapi uvicorn[standard] +gunicorn pydantic-settings - +# Database sqlalchemy psycopg2-binary alembic -# task queue +# Task Queue celery -redis[redis-stack] +redis + +# --- CORE DATA SCIENCE LIBS (PINNING NUMPY) --- +numpy<2.0 # CRITICAL FIX: Pin numpy to a version compatible with pandas-ta +pandas +pandas-ta +matplotlib -# data agent +# Data Agent & Prediction yfinance -# news & sentiment agent +# Intelligence Agent newspaper3k lxml_html_clean +snscrape@git+https://github.com/JustAnotherArchivist/snscrape.git@master +requests +beautifulsoup4 + +# AI / ML / LLM torch transformers sentence-transformers - -# special install for Twitter scraping -snscrape@git+https://github.com/JustAnotherArchivist/snscrape.git@master \ No newline at end of file +langchain +langchain-google-genai diff --git a/backend/schemas.py b/backend/schemas.py index c25b534056b7f9031876b772978ce3f2ba0871b0..24fad2a301b3ec70fce0a178fb80249710abe293 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -1,7 +1,57 @@ from pydantic import BaseModel, ConfigDict from uuid import UUID -from typing import Optional, Dict, Any +from typing import Optional, Dict, Any, List +# --- Schemas for Intelligence Briefing --- +class NewsArticle(BaseModel): + title: str + url: str + source: str + sentiment: str + sentiment_score: float + +class SentimentSummary(BaseModel): + total_items: int + positive: int + negative: int + neutral: int + error: Optional[str] = None + +class IntelligenceBriefing(BaseModel): + articles: List[NewsArticle] + sentiment_summary: SentimentSummary + +# --- Schema for our LLM Analysis --- +class LLMReport(BaseModel): + llm_report: str + error: Optional[str] = None + +# --- Main Schema for the 'result' field --- +class JobResult(BaseModel): + # Data Agent fields + ticker: Optional[str] = None + company_name: Optional[str] = None + current_price: Optional[float] = None + previous_close: Optional[float] = None + market_cap: Optional[int] = None + pe_ratio: Optional[float] = None + pb_ratio: Optional[float] = None + dividend_yield: Optional[float] = None + sector: Optional[str] = None + industry: Optional[str] = None + summary: Optional[str] = None + website: Optional[str] = None + + # Intelligence Agent field + intelligence_briefing: Optional[IntelligenceBriefing] = None + + # LLM Analyst field - THIS IS THE FIX + llm_analysis: Optional[LLMReport] = None + + # General error field + error: Optional[str] = None + +# --- Main Job Schemas for API endpoints --- class JobCreate(BaseModel): ticker: str @@ -9,6 +59,6 @@ class Job(BaseModel): id: UUID ticker: str status: str - result: Optional[Dict[str, Any]] = None + result: Optional[JobResult] = None model_config = ConfigDict(from_attributes=True) \ No newline at end of file diff --git a/backend/tasks/__pycache__/data_tasks.cpython-311.pyc b/backend/tasks/__pycache__/data_tasks.cpython-311.pyc deleted file mode 100644 index 1733f4c198c0c1212540ccd98655eb59dbba3228..0000000000000000000000000000000000000000 Binary files a/backend/tasks/__pycache__/data_tasks.cpython-311.pyc and /dev/null differ diff --git a/backend/tasks/__pycache__/news_tasks.cpython-311.pyc b/backend/tasks/__pycache__/news_tasks.cpython-311.pyc deleted file mode 100644 index 3877b13675ddddfbd725da1fae960a67b54857bc..0000000000000000000000000000000000000000 Binary files a/backend/tasks/__pycache__/news_tasks.cpython-311.pyc and /dev/null differ diff --git a/backend/tasks/advisor_tasks.py b/backend/tasks/advisor_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..89d27f29a34c884cd62fea32b87bbf2faecbda3b --- /dev/null +++ b/backend/tasks/advisor_tasks.py @@ -0,0 +1,47 @@ +from celery_worker import celery +from core.database import SessionLocal +from models.analysis_job import AnalysisJob +from tools.advisor_tools import generate_investment_thesis +from uuid import UUID + +@celery.task +def run_advisor_analysis(job_id: str): + db = SessionLocal() + job = None + final_result = "" + try: + job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() + if not job or not job.result: + raise ValueError(f"Job {job_id} not found or has no result data for the advisor.") + + print(f"Status - SUMMARIZING for job {job_id}...") + job.status = "SUMMARIZING" + db.commit() + + current_data = job.result + + advisor_summary = generate_investment_thesis(current_data) + + new_result = current_data.copy() + new_result['advisor_summary'] = advisor_summary + job.result = new_result + + job.status = "SUCCESS" # This is the final successful step + db.commit() + + print(f"Advisor analysis for job {job_id} completed successfully.") + final_result = str(job.result) + + except Exception as e: + print(f"Error during advisor analysis for job {job_id}: {e}") + if job: + job.status = "FAILED" + error_data = job.result if job.result else {} + error_data['error'] = f"Advisor analysis failed: {str(e)}" + job.result = error_data + db.commit() + final_result = f"Error: {e}" + finally: + db.close() + + return final_result \ No newline at end of file diff --git a/backend/tasks/analyst_tasks.py b/backend/tasks/analyst_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..eda0aa2ee71b7013f925374c237444d032622379 --- /dev/null +++ b/backend/tasks/analyst_tasks.py @@ -0,0 +1,117 @@ +# from celery_worker import celery +# from core.database import SessionLocal +# from models.analysis_job import AnalysisJob +# from tools.analyst_tools import get_llm_analysis +# from uuid import UUID + +# @celery.task +# def run_llm_analysis(job_id: str): +# db = SessionLocal() +# job = None +# try: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if not job or not job.result: +# raise ValueError("Job not found or has no initial data.") + +# job.status = "ANALYZING" # New status for the frontend +# db.commit() + +# current_data = job.result +# ticker = current_data.get("ticker") +# company_name = current_data.get("company_name") +# intelligence_briefing = current_data.get("intelligence_briefing", {}) + +# llm_report_data = get_llm_analysis(ticker, company_name, intelligence_briefing) + +# new_result = current_data.copy() +# new_result['llm_analysis'] = llm_report_data +# job.result = new_result + +# job.status = "SUCCESS" +# db.commit() + +# print(f"LLM analysis for job {job_id} completed successfully.") + +# except Exception as e: +# print(f"Error during LLM analysis for job {job_id}: {e}") +# if job: +# job.status = "FAILED" +# error_data = job.result if job.result else {} +# error_data['error'] = f"LLM analysis failed: {str(e)}" +# job.result = error_data +# db.commit() +# finally: +# db.close() + + + + + + + + + + +# from celery_worker import celery +# from core.database import SessionLocal +# from models.analysis_job import AnalysisJob +# from tools.analyst_tools import get_llm_analysis +# from uuid import UUID + +# @celery.task +# def run_llm_analysis(job_id: str): +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if not job or not job.result: +# print(f"Job {job_id} not found or has no data for analyst.") +# return + +# try: +# job.status = "ANALYZING" +# db.commit() + +# current_data = job.result +# ticker = current_data.get("ticker") +# company_name = current_data.get("company_name") +# intelligence_briefing = current_data.get("intelligence_briefing", {}) + +# llm_report_data = get_llm_analysis(ticker, company_name, intelligence_briefing) + +# new_result = dict(current_data) +# new_result['llm_analysis'] = llm_report_data +# job.result = new_result + +# job.status = "SUCCESS" +# db.commit() + +# print(f"LLM analysis for job {job_id} completed successfully.") +# return "LLM analysis successful." +# except Exception as e: +# print(f"Error during LLM analysis for job {job_id}: {e}") +# job.status = "FAILED" +# error_data = job.result if job.result else {} +# error_data['error'] = f"LLM analysis failed: {str(e)}" +# job.result = error_data +# db.commit() +# return f"LLM analysis failed: {e}" + + + + + + + + + + + +from celery_worker import celery +from tools.analyst_tools import get_llm_analysis + +@celery.task +def get_llm_analysis_task(full_job_result: dict): + print(f"Executing get_llm_analysis_task...") + ticker = full_job_result.get("ticker") + company_name = full_job_result.get("company_name") + intelligence_briefing = full_job_result.get("intelligence_briefing", {}) + return get_llm_analysis(ticker, company_name, intelligence_briefing) \ No newline at end of file diff --git a/backend/tasks/coordinator_task.py b/backend/tasks/coordinator_task.py new file mode 100644 index 0000000000000000000000000000000000000000..94588fd7cf4529620b93ff8fb35795e4e196e603 --- /dev/null +++ b/backend/tasks/coordinator_task.py @@ -0,0 +1,38 @@ +from celery_worker import celery +from core.database import SessionLocal +from models.analysis_job import AnalysisJob +from uuid import UUID + +@celery.task(bind=True) +def coordinator_task(self, results, job_id: str): + """ + This task receives the results from all previous tasks, assembles the + final result, and saves it to the database ONCE. + """ + print(f"Coordinator task started for job {job_id}...") + with SessionLocal() as db: + job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() + if not job: + print(f"Job {job_id} not found in coordinator.") + return + + try: + # results[0] is from get_data_task + # results[1] is from get_intelligence_task + # results[2] is from get_llm_analysis_task + + final_result = { + **results[0], # Unpack the dictionary from the data task + "intelligence_briefing": results[1], + "llm_analysis": results[2], + } + + job.result = final_result + job.status = "SUCCESS" + db.commit() + print(f"Coordinator task for job {job_id} successfully saved final result.") + except Exception as e: + print(f"Error in coordinator for job {job_id}: {e}") + job.status = "FAILED" + job.result = {"error": f"Final assembly failed: {str(e)}"} + db.commit() \ No newline at end of file diff --git a/backend/tasks/data_tasks.py b/backend/tasks/data_tasks.py index 5c4577980a695822d830fb6a36bb63ebb2cdf28a..401dabbf220edd46a368d0f4063b93e3c9fb0fd9 100644 --- a/backend/tasks/data_tasks.py +++ b/backend/tasks/data_tasks.py @@ -1,43 +1,97 @@ +# from celery_worker import celery +# from core.database import SessionLocal +# from models.analysis_job import AnalysisJob +# from tools.data_tools import get_stock_data +# from uuid import UUID + +# @celery.task +# def run_data_analysis(job_id: str, ticker: str): +# db = SessionLocal() +# job = None +# final_result = "" +# try: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if not job: +# raise ValueError(f"Job {job_id} not found in database.") + +# print(f"Status - DATA_FETCHING for job {job_id}...") +# job.status = "DATA_FETCHING" +# db.commit() + +# data = get_stock_data(ticker) + +# if "error" in data: +# raise ValueError(data["error"]) + +# job.result = data +# db.commit() +# print(f"Data analysis for job {job_id} completed successfully.") + +# final_result = str(job.result) + +# except Exception as e: +# print(f"Error during data analysis for job {job_id}: {e}") +# if job: +# job.status = "FAILED" +# job.result = {"error": f"Data analysis failed: {str(e)}"} +# db.commit() +# final_result = f"Error: {e}" +# finally: +# db.close() + +# return final_result + + + + + + + +# from celery_worker import celery +# from core.database import SessionLocal +# from models.analysis_job import AnalysisJob +# from tools.data_tools import get_stock_data +# from uuid import UUID + +# @celery.task +# def run_data_analysis(job_id: str, ticker: str): +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if not job: +# print(f"Job {job_id} not found.") +# return + +# try: +# job.status = "DATA_FETCHING" +# db.commit() + +# data = get_stock_data(ticker) +# if "error" in data: +# raise ValueError(data["error"]) + +# job.result = data +# db.commit() +# print(f"Data analysis for job {job_id} completed successfully.") +# return "Data fetching successful." +# except Exception as e: +# print(f"Error during data analysis for job {job_id}: {e}") +# job.status = "FAILED" +# job.result = {"error": f"Data analysis failed: {str(e)}"} +# db.commit() +# return f"Data fetching failed: {e}" + + + + + + + + + from celery_worker import celery -from core.database import SessionLocal -from models.analysis_job import AnalysisJob from tools.data_tools import get_stock_data -from uuid import UUID @celery.task -def run_data_analysis(job_id: str, ticker: str): - db = SessionLocal() - job = None - try: - job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() - if not job: - print(f"Job {job_id} not found in database.") - return - - print(f"Starting data analysis for job {job_id}, ticker {ticker}...") - job.status = "RUNNING" - db.commit() - - data = get_stock_data(ticker) - - job.result = data - job.status = "SUCCESS" - db.commit() - print(f"Data analysis for job {job_id} completed successfully.") - - # get the result BEFORE closing the session - final_result = str(job.result) - - except Exception as e: - print(f"Error during data analysis for job {job_id}: {e}") - if job: - job.status = "FAILED" - job.result = {"error": str(e)} - db.commit() - final_result = f"Error: {e}" - finally: - # always close the database session - db.close() - - # return the variable that is no longer attached to the session - return final_result \ No newline at end of file +def get_data_task(ticker: str): + print(f"Executing get_data_task for {ticker}...") + return get_stock_data(ticker) \ No newline at end of file diff --git a/backend/tasks/main_task.py b/backend/tasks/main_task.py new file mode 100644 index 0000000000000000000000000000000000000000..d3f71487d9cf4b7b3de74e8ba426e94e6d2d07db --- /dev/null +++ b/backend/tasks/main_task.py @@ -0,0 +1,203 @@ +# from celery_worker import celery +# from core.database import SessionLocal +# from models.analysis_job import AnalysisJob +# from tools.data_tools import get_stock_data +# from tools.news_tools import get_combined_news_and_sentiment +# from tools.analyst_tools import get_llm_analysis +# from uuid import UUID +# import json + +# @celery.task +# def run_full_analysis(job_id: str, ticker: str): +# print(f"\n--- [START] Full Analysis for Job ID: {job_id} ---") + +# # --- Stage 1: Data Fetching --- +# try: +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if not job: raise ValueError("Job not found") +# job.status = "DATA_FETCHING" +# db.commit() +# print("[LOG] STATUS UPDATE: DATA_FETCHING") + +# data_result = get_stock_data(ticker) +# if "error" in data_result: raise ValueError(data_result['error']) +# company_name = data_result.get("company_name", ticker) + +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# job.result = data_result +# db.commit() +# db.refresh(job) # Force reload from DB +# print(f"[LOG] DB SAVE 1 (Data): Result keys are now: {list(job.result.keys())}") + +# except Exception as e: +# print(f"!!! [FAILURE] Stage 1 (Data): {e}") +# # ... error handling ... +# return + +# # --- Stage 2: Intelligence Gathering --- +# try: +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# job.status = "INTELLIGENCE_GATHERING" +# db.commit() +# print("[LOG] STATUS UPDATE: INTELLIGENCE_GATHERING") + +# intelligence_result = get_combined_news_and_sentiment(ticker, company_name) + +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# current_result = dict(job.result) +# current_result['intelligence_briefing'] = intelligence_result +# job.result = current_result +# db.commit() +# db.refresh(job) # Force reload +# print(f"[LOG] DB SAVE 2 (Intelligence): Result keys are now: {list(job.result.keys())}") +# except Exception as e: +# print(f"!!! [FAILURE] Stage 2 (Intelligence): {e}") +# # ... error handling ... +# return + +# # --- Stage 3: LLM Analysis --- +# try: +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# job.status = "ANALYZING" +# db.commit() +# print("[LOG] STATUS UPDATE: ANALYZING") + +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# data_for_llm = job.result + +# llm_result = get_llm_analysis(ticker, company_name, data_for_llm.get("intelligence_briefing", {})) +# if "error" in llm_result: raise ValueError(llm_result['error']) + +# # --- Final Assembly and Save --- +# print("[LOG] Finalizing results...") +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# final_result_data = dict(job.result) +# final_result_data['llm_analysis'] = llm_result +# job.result = final_result_data +# job.status = "SUCCESS" +# db.commit() +# db.refresh(job) +# print(f"[LOG] DB SAVE 3 (Final): Result keys are now: {list(job.result.keys())}") + +# print(f"--- [SUCCESS] Full analysis for {job_id} complete. ---") + +# except Exception as e: +# print(f"!!! [FAILURE] Stage 3 (LLM): {e}") +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if job: +# job.status = "FAILED" +# error_data = job.result if job.result else {} +# error_data['error'] = str(e) +# job.result = error_data +# db.commit() + + + + + + + + + + +from celery_worker import celery +from core.database import SessionLocal +from models.analysis_job import AnalysisJob +from tools.data_tools import get_stock_data +from tools.news_tools import get_combined_news_and_sentiment +from tools.analyst_tools import get_llm_analysis +from uuid import UUID +import json + +@celery.task +def run_full_analysis(job_id: str, ticker: str): + """ + The single, main task that orchestrates the entire analysis pipeline. + """ + print(f"\n--- [START] Full Analysis for Job ID: {job_id} ---") + + # We will use one job object throughout and update it, committing as we go. + # This requires careful session management. + db = SessionLocal() + job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() + + if not job: + print(f"Job {job_id} not found. Aborting.") + db.close() + return + + try: + # --- Stage 1: Data Fetching --- + print(f"Stage 1: DATA_FETCHING for job {job_id}") + job.status = "DATA_FETCHING" + db.commit() + + data_result = get_stock_data(ticker) + if "error" in data_result: + raise ValueError(f"Data fetching failed: {data_result['error']}") + + company_name = data_result.get("company_name", ticker) + + job.result = data_result + db.commit() + print("-> Data fetching stage complete.") + + # --- Stage 2: Intelligence Gathering --- + print(f"Stage 2: INTELLIGENCE_GATHERING for job {job_id}") + job.status = "INTELLIGENCE_GATHERING" + db.commit() + + intelligence_result = get_combined_news_and_sentiment(ticker, company_name) + + current_result = dict(job.result) + current_result['intelligence_briefing'] = intelligence_result + job.result = current_result + db.commit() + print("-> Intelligence gathering stage complete.") + + # --- Stage 3: LLM Analysis --- + print(f"Stage 3: ANALYZING for job {job_id}") + job.status = "ANALYZING" + db.commit() + + # We need to refresh the job object to get the latest result for the LLM + db.refresh(job) + data_for_llm = job.result + + llm_result = get_llm_analysis(ticker, company_name, data_for_llm.get("intelligence_briefing", {})) + if "error" in llm_result: + raise ValueError(f"LLM analysis failed: {llm_result['error']}") + + # --- Final Assembly and Save --- + print("Finalizing results for job {job_id}") + final_result_data = dict(job.result) + final_result_data['llm_analysis'] = llm_result + + job.result = final_result_data + job.status = "SUCCESS" + db.commit() + + print(f"--- [SUCCESS] Full analysis for {job_id} complete. ---") + + except Exception as e: + error_message = str(e) + print(f"!!! [FAILURE] Full analysis for {job_id} FAILED: {error_message}") + if job: + job.status = "FAILED" + # Provide a cleaner error message for the user, while keeping technical details + user_friendly_error = f"Analysis failed for ticker '{ticker}'. This stock may not be listed or there was a problem fetching its data. Please check the ticker symbol and try again. (Details: {error_message})" + + error_data = job.result if job.result else {} + error_data['error'] = user_friendly_error + job.result = error_data + db.commit() + finally: + db.close() \ No newline at end of file diff --git a/backend/tasks/news_tasks.py b/backend/tasks/news_tasks.py index eb692da97797440a01f221bbbdca20c83efbdb01..f83e23c72fb42ae152a2a7c6f7f85c725d2a70cf 100644 --- a/backend/tasks/news_tasks.py +++ b/backend/tasks/news_tasks.py @@ -1,43 +1,348 @@ +# # tasks/news_tasks.py - SIMPLIFIED VERSION THAT ALWAYS WORKS + +# from celery_worker import celery +# from core.database import SessionLocal +# from models.analysis_job import AnalysisJob +# from uuid import UUID +# import logging +# from datetime import datetime +# import yfinance as yf + +# logger = logging.getLogger(__name__) + +# def get_stock_basic_info(ticker: str): +# """Get basic stock information to create realistic content""" +# try: +# stock = yf.Ticker(ticker) +# info = stock.info +# return { +# 'name': info.get('longName', ticker.replace('.NS', '')), +# 'sector': info.get('sector', 'Unknown'), +# 'industry': info.get('industry', 'Unknown'), +# 'current_price': info.get('currentPrice', 0), +# 'previous_close': info.get('previousClose', 0) +# } +# except Exception as e: +# logger.warning(f"Could not get stock info for {ticker}: {e}") +# return { +# 'name': ticker.replace('.NS', ''), +# 'sector': 'Unknown', +# 'industry': 'Unknown', +# 'current_price': 0, +# 'previous_close': 0 +# } + +# def create_realistic_articles(ticker: str, company_name: str, stock_info: dict): +# """Create realistic articles based on stock information""" + +# # Calculate price movement for realistic sentiment +# current_price = stock_info.get('current_price', 0) +# previous_close = stock_info.get('previous_close', 0) + +# price_change = 0 +# if current_price and previous_close: +# price_change = ((current_price - previous_close) / previous_close) * 100 + +# # Generate articles based on actual stock performance +# articles = [] + +# if price_change > 2: +# articles.extend([ +# { +# "title": f"{company_name} Shares Rally {price_change:.1f}% on Strong Market Sentiment", +# "url": f"https://finance.yahoo.com/quote/{ticker}", +# "source": "Market Analysis", +# "sentiment": "Positive", +# "sentiment_score": 0.8 +# }, +# { +# "title": f"Investors Show Confidence in {company_name} as Stock Gains Momentum", +# "url": f"https://www.moneycontrol.com/india/stockpricequote/{ticker}", +# "source": "Financial Express", +# "sentiment": "Positive", +# "sentiment_score": 0.7 +# } +# ]) +# elif price_change < -2: +# articles.extend([ +# { +# "title": f"{company_name} Stock Declines {abs(price_change):.1f}% Amid Market Volatility", +# "url": f"https://finance.yahoo.com/quote/{ticker}", +# "source": "Market Watch", +# "sentiment": "Negative", +# "sentiment_score": 0.8 +# }, +# { +# "title": f"Market Correction Impacts {company_name} Share Price", +# "url": f"https://www.moneycontrol.com/india/stockpricequote/{ticker}", +# "source": "Economic Times", +# "sentiment": "Negative", +# "sentiment_score": 0.6 +# } +# ]) +# else: +# articles.extend([ +# { +# "title": f"{company_name} Stock Shows Steady Performance in Current Market", +# "url": f"https://finance.yahoo.com/quote/{ticker}", +# "source": "Yahoo Finance", +# "sentiment": "Neutral", +# "sentiment_score": 0.5 +# }, +# { +# "title": f"Technical Analysis: {company_name} Maintains Stable Trading Range", +# "url": f"https://www.moneycontrol.com/india/stockpricequote/{ticker}", +# "source": "Market Analysis", +# "sentiment": "Neutral", +# "sentiment_score": 0.5 +# } +# ]) + +# # Add sector-specific articles +# sector = stock_info.get('sector', 'Unknown') +# if sector != 'Unknown': +# articles.extend([ +# { +# "title": f"{sector} Sector Update: Key Players Including {company_name} in Focus", +# "url": "https://example.com/sector-analysis", +# "source": "Sector Reports", +# "sentiment": "Neutral", +# "sentiment_score": 0.6 +# }, +# { +# "title": f"Industry Outlook: {stock_info.get('industry', 'Market')} Trends Affecting {company_name}", +# "url": "https://example.com/industry-outlook", +# "source": "Industry Analysis", +# "sentiment": "Positive", +# "sentiment_score": 0.6 +# } +# ]) + +# # Add general market articles +# articles.extend([ +# { +# "title": f"Quarterly Performance Review: {company_name} Financials and Market Position", +# "url": f"https://finance.yahoo.com/quote/{ticker}/financials", +# "source": "Financial Reports", +# "sentiment": "Neutral", +# "sentiment_score": 0.5 +# }, +# { +# "title": f"Analyst Coverage: Investment Recommendations for {company_name} Stock", +# "url": "https://example.com/analyst-coverage", +# "source": "Research Reports", +# "sentiment": "Positive", +# "sentiment_score": 0.7 +# }, +# { +# "title": f"Market Sentiment Analysis: Retail vs Institutional Interest in {company_name}", +# "url": "https://example.com/market-sentiment", +# "source": "Market Research", +# "sentiment": "Neutral", +# "sentiment_score": 0.5 +# } +# ]) + +# return articles[:8] # Return top 8 articles + +# def try_real_news_sources(ticker: str, company_name: str): +# """Attempt to get real news, but don't fail if it doesn't work""" +# real_articles = [] + +# try: +# # Try Yahoo Finance news (most reliable) +# logger.info(f"Attempting to fetch real Yahoo Finance news for {ticker}") +# stock = yf.Ticker(ticker) +# news = stock.news + +# if news: +# logger.info(f"Found {len(news)} Yahoo Finance articles") +# for article in news[:5]: # Take first 5 +# if article.get('title'): +# # Simple sentiment analysis +# title_lower = article['title'].lower() +# if any(word in title_lower for word in ['gain', 'rise', 'growth', 'profit', 'strong']): +# sentiment = 'Positive' +# score = 0.7 +# elif any(word in title_lower for word in ['fall', 'decline', 'loss', 'weak', 'drop']): +# sentiment = 'Negative' +# score = 0.7 +# else: +# sentiment = 'Neutral' +# score = 0.5 + +# real_articles.append({ +# "title": article['title'].strip(), +# "url": article.get('link', ''), +# "source": article.get('publisher', 'Yahoo Finance'), +# "sentiment": sentiment, +# "sentiment_score": score, +# "is_real": True +# }) + +# logger.info(f"Successfully retrieved {len(real_articles)} real articles") + +# except Exception as e: +# logger.warning(f"Could not fetch real news: {e}") + +# return real_articles + +# @celery.task +# def run_intelligence_analysis(job_id: str): +# """Simplified intelligence analysis that always provides results""" +# db = SessionLocal() +# job = None + +# try: +# logger.info(f"Starting intelligence analysis for job {job_id}") + +# # Get job +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if not job or not job.result: +# raise ValueError(f"Job {job_id} not found or has no initial data.") + +# job.status = "INTELLIGENCE_GATHERING" +# db.commit() + +# current_data = job.result +# ticker = current_data.get("ticker") +# company_name = current_data.get("company_name", ticker.replace('.NS', '')) + +# logger.info(f"Analyzing {company_name} ({ticker})") + +# # Get basic stock information +# stock_info = get_stock_basic_info(ticker) +# logger.info(f"Stock info: {stock_info['name']} - {stock_info['sector']}") + +# # Try to get real news first +# real_articles = try_real_news_sources(ticker, company_name) + +# # Create realistic articles +# realistic_articles = create_realistic_articles(ticker, company_name, stock_info) + +# # Combine real and realistic articles +# all_articles = real_articles + realistic_articles + +# # Remove duplicates and limit to 10 articles +# seen_titles = set() +# unique_articles = [] +# for article in all_articles: +# if article['title'] not in seen_titles: +# seen_titles.add(article['title']) +# unique_articles.append(article) + +# final_articles = unique_articles[:10] + +# # Count sentiments +# sentiment_counts = {'Positive': 0, 'Negative': 0, 'Neutral': 0} +# for article in final_articles: +# sentiment_counts[article['sentiment']] += 1 + +# # Create intelligence briefing +# intelligence_briefing = { +# "articles": final_articles, +# "sentiment_summary": { +# "total_items": len(final_articles), +# "positive": sentiment_counts['Positive'], +# "negative": sentiment_counts['Negative'], +# "neutral": sentiment_counts['Neutral'], +# "real_articles": len(real_articles), +# "generated_articles": len(realistic_articles), +# "analysis_timestamp": datetime.now().isoformat() +# } +# } + +# # Update job result +# new_result = current_data.copy() +# new_result['intelligence_briefing'] = intelligence_briefing +# job.result = new_result +# job.status = "INTELLIGENCE_COMPLETE" + +# db.commit() + +# logger.info(f"Intelligence analysis completed successfully:") +# logger.info(f"- Total articles: {len(final_articles)}") +# logger.info(f"- Real articles: {len(real_articles)}") +# logger.info(f"- Generated articles: {len(realistic_articles)}") +# logger.info(f"- Sentiment: {sentiment_counts}") + +# return str(job.result) + +# except Exception as e: +# logger.error(f"Intelligence analysis failed for job {job_id}: {e}") + +# if job: +# job.status = "FAILED" +# error_data = job.result if job.result else {} +# error_data['error'] = f"Intelligence analysis failed: {str(e)}" +# job.result = error_data +# db.commit() + +# return f"Error: {e}" + +# finally: +# db.close() + + + + + + + + + +# from celery_worker import celery +# from core.database import SessionLocal +# from models.analysis_job import AnalysisJob +# from tools.news_tools import get_combined_news_and_sentiment +# from uuid import UUID + +# @celery.task +# def run_intelligence_analysis(job_id: str): +# with SessionLocal() as db: +# job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() +# if not job or not job.result: +# print(f"Job {job_id} not found or has no data for intelligence.") +# return + +# try: +# job.status = "INTELLIGENCE_GATHERING" +# db.commit() + +# current_data = job.result +# ticker = current_data.get("ticker") +# company_name = current_data.get("company_name") + +# intelligence_briefing = get_combined_news_and_sentiment(ticker, company_name) + +# new_result = dict(current_data) +# new_result['intelligence_briefing'] = intelligence_briefing +# job.result = new_result + +# db.commit() +# print(f"Intelligence analysis for job {job_id} completed successfully.") +# return "Intelligence gathering successful." +# except Exception as e: +# print(f"Error during intelligence analysis for job {job_id}: {e}") +# job.status = "FAILED" +# error_data = job.result if job.result else {} +# error_data['error'] = f"Intelligence analysis failed: {str(e)}" +# job.result = error_data +# db.commit() +# return f"Intelligence gathering failed: {e}" + + + + + + + + from celery_worker import celery -from core.database import SessionLocal -from models.analysis_job import AnalysisJob -from tools.news_tools import get_news_and_sentiment, get_twitter_sentiment -from uuid import UUID +from tools.news_tools import get_combined_news_and_sentiment @celery.task -def run_intelligence_analysis(job_id: str): - db = SessionLocal() - job = None - try: - job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() - if not job or not job.result: raise ValueError(f"Job {job_id} not found or has no data.") - - current_data = job.result - company_name = current_data.get("company_name") - if not company_name: raise ValueError("Company name not found in data.") - - print(f"Starting intelligence analysis for {company_name}...") - - news = get_news_and_sentiment(current_data.get("ticker"), company_name) - twitter = get_twitter_sentiment(f"{company_name} stock") - - current_data['intelligence_briefing'] = {"news": news, "twitter": twitter} - job.result = current_data - job.status = "SUCCESS" - db.commit() - - print(f"Intelligence analysis for job {job_id} completed.") - final_result = str(job.result) - - except Exception as e: - print(f"Error during intelligence analysis for job {job_id}: {e}") - if job: - job.status = "FAILED" - error_data = job.result if job.result else {} - error_data['error'] = f"Intelligence analysis failed: {str(e)}" - job.result = error_data - db.commit() - final_result = f"Error: {e}" - finally: - db.close() - return final_result \ No newline at end of file +def get_intelligence_task(ticker: str, company_name: str): + print(f"Executing get_intelligence_task for {company_name}...") + # This task now depends on the company_name from the first task's result + return get_combined_news_and_sentiment(ticker, company_name) \ No newline at end of file diff --git a/backend/tasks/prediction_tasks.py b/backend/tasks/prediction_tasks.py new file mode 100644 index 0000000000000000000000000000000000000000..8195fc5372757b4e16e50e54731799f93c206ae6 --- /dev/null +++ b/backend/tasks/prediction_tasks.py @@ -0,0 +1,52 @@ +from celery_worker import celery +from core.database import SessionLocal +from models.analysis_job import AnalysisJob +from tools.prediction_tools import generate_forecast +from uuid import UUID + +@celery.task +def run_prediction_analysis(job_id: str): + db = SessionLocal() + job = None + final_result = "" + try: + job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first() + if not job or not job.result: + raise ValueError(f"Job {job_id} not found or has no initial data.") + + print(f"Status - PREDICTING for job {job_id}...") + job.status = "PREDICTING" + db.commit() + + current_data = job.result + ticker = current_data.get("ticker") + if not ticker: + raise ValueError("Ticker not found in initial data.") + + forecast_data = generate_forecast(ticker) + + if "error" in forecast_data: + raise ValueError(forecast_data["error"]) + + new_result = current_data.copy() + new_result['prediction_analysis'] = forecast_data + job.result = new_result + + db.commit() + + print(f"Prediction analysis for job {job_id} completed successfully.") + final_result = str(job.result) + + except Exception as e: + print(f"Error during prediction analysis for job {job_id}: {e}") + if job: + job.status = "FAILED" + error_data = job.result if job.result else {} + error_data['error'] = f"Prediction analysis failed: {str(e)}" + job.result = error_data + db.commit() + final_result = f"Error: {e}" + finally: + db.close() + + return final_result \ No newline at end of file diff --git a/backend/tmp.py b/backend/tmp.py new file mode 100644 index 0000000000000000000000000000000000000000..bf87e71f5eb599d72ff1fd8e7118db42a7bc3524 --- /dev/null +++ b/backend/tmp.py @@ -0,0 +1,25 @@ +# test_news.py - Run this to test the news functionality +from tools.news_tools import get_combined_news_and_sentiment_debug + +def test_news(): + # Test with a popular Indian stock + ticker = "RELIANCE.NS" + company_name = "Reliance Industries" + + print(f"Testing news scraping for {ticker} ({company_name})") + result = get_combined_news_and_sentiment_debug(ticker, company_name) + + print(f"\nResults:") + print(f"Total articles: {result['sentiment_summary']['total_items']}") + print(f"Positive: {result['sentiment_summary']['positive']}") + print(f"Negative: {result['sentiment_summary']['negative']}") + print(f"Neutral: {result['sentiment_summary']['neutral']}") + + if result['articles']: + print(f"\nSample articles:") + for i, article in enumerate(result['articles'][:3]): + print(f"{i+1}. {article['title']}") + print(f" Source: {article['source']} | Sentiment: {article['sentiment']}") + +if __name__ == "__main__": + test_news() \ No newline at end of file diff --git a/backend/tools/__pycache__/data_tools.cpython-311.pyc b/backend/tools/__pycache__/data_tools.cpython-311.pyc index 5973fcf32f6ed9c35781e561f77d46787598ac04..a89917787a3ca0e6c0f3dec86f5da9471a035eaa 100644 Binary files a/backend/tools/__pycache__/data_tools.cpython-311.pyc and b/backend/tools/__pycache__/data_tools.cpython-311.pyc differ diff --git a/backend/tools/__pycache__/news_tools.cpython-311.pyc b/backend/tools/__pycache__/news_tools.cpython-311.pyc index 0c9ebec9236c3e0d96df1b46a38e9774423d3e30..c52f65222051bbee814a32295b2e997c94285069 100644 Binary files a/backend/tools/__pycache__/news_tools.cpython-311.pyc and b/backend/tools/__pycache__/news_tools.cpython-311.pyc differ diff --git a/backend/tools/advisor_tools.py b/backend/tools/advisor_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..ed2717526aaa92e652106d1b230e4af13eb9df10 --- /dev/null +++ b/backend/tools/advisor_tools.py @@ -0,0 +1,70 @@ +from langchain_google_genai import ChatGoogleGenerativeAI +from langchain.prompts import PromptTemplate +from langchain.chains import LLMChain +from typing import Dict, Any +import json + +def generate_investment_thesis(full_job_result: Dict[str, Any]) -> str: + """ + Uses the Gemini 1.5 Flash model to generate a qualitative investment thesis. + """ + print("Generating investment thesis with Gemini 1.5 Flash...") + + # Initialize the LLM + llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash") + + # Create a simplified summary of the data to pass to the LLM + # This prevents sending thousands of characters of raw data + fundamentals_summary = ( + f"Company: {full_job_result.get('company_name', 'N/A')}\n" + f"Current Price: {full_job_result.get('current_price', 'N/A')}\n" + f"Market Cap: {full_job_result.get('market_cap', 'N/A')}\n" + f"P/E Ratio: {full_job_result.get('pe_ratio', 'N/A'):.2f}\n" + f"Sector: {full_job_result.get('sector', 'N/A')}" + ) + + prediction_summary = full_job_result.get('prediction_analysis', {}).get('summary', 'No prediction summary available.') + + # We need to handle the case where intelligence gathering might have failed + intelligence_briefing = full_job_result.get('intelligence_briefing', {}) + if intelligence_briefing and intelligence_briefing.get('news'): + news_summary = ", ".join([f"'{article['title']}' ({article['sentiment']})" for article in intelligence_briefing['news'][:2]]) + else: + news_summary = "No news articles found." + + # Define the prompt template + prompt = PromptTemplate( + input_variables=["fundamentals", "prediction", "news"], + template=""" + You are a sharp, concise senior financial analyst for an Indian market-focused fund. + Your task is to provide a clear investment thesis based on the data provided. + Do not offer financial advice. Analyze the data objectively. + + **Data Overview:** + - **Fundamentals:** {fundamentals} + - **Quantitative Forecast:** {prediction} + - **Recent News Headlines & Sentiment:** {news} + + **Your Analysis (in Markdown format):** + **1. Executive Summary:** A 2-sentence summary of the company's current situation based on the data. + **2. Bull Case:** 2-3 bullet points on the positive signals from the data. + **3. Bear Case:** 2-3 bullet points on the primary risks or negative signals. + **4. Final Recommendation:** State ONE of the following: 'Strong Buy', 'Buy', 'Hold', 'Sell', or 'Strong Sell' and provide a brief 1-sentence justification based purely on the provided data mix. + """ + ) + + # Create the LangChain chain + chain = LLMChain(llm=llm, prompt=prompt) + + # Run the chain with our summarized data + try: + response = chain.run( + fundamentals=fundamentals_summary, + prediction=prediction_summary, + news=news_summary + ) + print("Successfully generated thesis from Gemini.") + return response + except Exception as e: + print(f"Error calling Gemini API: {e}") + return "Error: Could not generate the advisor summary." \ No newline at end of file diff --git a/backend/tools/analyst_tools.py b/backend/tools/analyst_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..9ffe798c5ce43fe07f1aa9b13b480db721bc745d --- /dev/null +++ b/backend/tools/analyst_tools.py @@ -0,0 +1,151 @@ +import yfinance as yf +import pandas as pd +from langchain_google_genai import ChatGoogleGenerativeAI +from langchain.prompts import PromptTemplate +from langchain.chains import LLMChain +from typing import Dict, Any +import time + +def get_llm_analysis(ticker: str, company_name: str, intelligence_briefing: Dict[str, Any]) -> Dict[str, Any]: + """ + Uses Gemini 1.5 Flash to analyze historical data and news to generate + a forecast and a complete investment thesis. + """ + print(f"Starting LLM-powered analysis for {ticker} with Gemini 1.5 Flash...") + + # clean and format ticker properly + original_ticker = ticker + if not ticker.endswith(('.NS', '.BO', '.L', '.TO')): + # For Indian stocks, try NSE first + ticker = f"{ticker}.NS" + print(f"Formatted ticker from '{original_ticker}' to '{ticker}' for Yahoo Finance") + + historical_data_text = "Could not fetch historical price data due to repeated errors." + + for attempt in range(3): + try: + print(f"Attempt {attempt + 1}/3 to download historical data for {ticker}...") + stock_data = yf.download(ticker, period="100d", interval="1d", progress=False) + + if not stock_data.empty: + # Convert to a more readable format for the LLM + stock_data = stock_data.round(2) # Round to 2 decimal places + # Include only the last 20 days for LLM context efficiency + recent_data = stock_data.tail(20) + historical_data_text = f"Recent 20 days of data for {ticker}:\n{recent_data.to_string()}" + print("-> Successfully downloaded historical data.") + break + else: + raise ValueError("Downloaded data is empty.") + + except Exception as e: + print(f"-> Attempt {attempt + 1} failed: {e}") + # If NSE fails, try BSE + if attempt == 0 and ticker.endswith('.NS'): + ticker = f"{original_ticker}.BO" + print(f"Retrying with BSE ticker: {ticker}") + elif attempt < 2: + print(" Waiting 2 seconds before retrying...") + time.sleep(2) + + # If all attempts failed, check if we can get basic info + if "Could not fetch historical price data" in historical_data_text: + try: + print("Attempting to get basic stock info as fallback...") + stock = yf.Ticker(ticker) + info = stock.info + if info and info.get('regularMarketPrice'): + current_price = info.get('regularMarketPrice') + previous_close = info.get('previousClose', current_price) + historical_data_text = f"Limited data available for {ticker}:\nCurrent Price: ₹{current_price}\nPrevious Close: ₹{previous_close}" + print("-> Got basic stock info as fallback.") + else: + print("-> No data available from any source.") + except Exception as e: + print(f"-> Fallback also failed: {e}") + + # 2. Summarize the news data into a simple text block + articles = intelligence_briefing.get('articles', []) + if articles: + news_summary = "\n".join([f"- {article['title']} (Source: {article['source']}, Sentiment: {article['sentiment']})" for article in articles[:10]]) # Limit to 10 articles + else: + news_summary = "No recent news or social media mentions found." + + # 3. Initialize the LLM + try: + llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", temperature=0.3) + except Exception as e: + print(f"Error initializing Gemini: {e}") + return {"error": f"Failed to initialize Gemini API: {str(e)}"} + + # 4. Enhanced prompt that handles missing data gracefully + prompt = PromptTemplate( + input_variables=["ticker", "company_name", "historical_data", "news_summary"], + template=""" + You are a Senior Financial Analyst for an Indian market-focused investment fund. + Your task is to provide a comprehensive analysis and a 30-day forecast for the stock: {ticker} ({company_name}). + + **Important Instructions:** + - If historical data is limited or unavailable, focus your analysis on news sentiment and general market conditions + - Always provide a forecast range even with limited data, but adjust confidence accordingly + - Base your analysis ONLY on the provided data + + **Data Provided:** + + 1. **Historical Price Data:** + ``` + {historical_data} + ``` + + 2. **Recent News & Social Media Headlines:** + {news_summary} + + **Your Analysis Report (in Markdown format):** + + ## 30-Day Price Forecast + + **Analysis:** Analyze available data (price trends if available, news sentiment, market conditions). If price data is limited, focus on sentiment analysis and sector trends. + + **Predicted Range:** Provide a realistic price range for 30 days (e.g., ₹1500 - ₹1650). If no current price available, state "Unable to provide specific range due to data limitations." + + **Justification:** Explain your forecast based on available information. + + **Confidence:** High/Moderate/Low based on data quality and availability. + + ## Investment Thesis + + **Bull Case:** + - Point 1 based on positive signals from available data + - Point 2 based on news sentiment or market conditions + - Point 3 if sufficient data available + + **Bear Case:** + - Point 1 based on negative signals or risks + - Point 2 based on market concerns + - Point 3 if sufficient data available + + ## Actionable Strategy + + **Signal:** Buy/Sell/Hold (choose one) + + **Strategy:** Provide 1-2 sentences with specific actionable advice based on the analysis above. + + **Risk Management:** Brief note on stop-loss or position sizing if relevant. + """ + ) + + # 5. Run the LangChain chain with error handling + chain = LLMChain(llm=llm, prompt=prompt) + try: + response = chain.run( + ticker=ticker, + company_name=company_name, + historical_data=historical_data_text, + news_summary=news_summary + ) + print("Successfully generated analysis from Gemini.") + return {"llm_report": response} + except Exception as e: + error_msg = f"Failed to generate analysis from Gemini: {str(e)}" + print(f"Error calling Gemini API: {e}") + return {"error": error_msg} \ No newline at end of file diff --git a/backend/tools/data_tools.py b/backend/tools/data_tools.py index 973577f92f9d12333b3a0b494c722f9e1721a052..03338b2744b9dcf6574c7dad2b08c11f38d29d73 100644 --- a/backend/tools/data_tools.py +++ b/backend/tools/data_tools.py @@ -2,42 +2,42 @@ import yfinance as yf from typing import Dict, Any def get_stock_data(ticker: str) -> Dict[str, Any]: - - # for NSE stocks, yfinance expects the '.NS' suffix. For BSE, it's '.BO'. - # assume NSE by default if no suffix is provided. if not ticker.endswith(('.NS', '.BO')): - print(f"Ticker '{ticker}' has no exchange suffix. Assuming NSE and appending '.NS'.") ticker = f"{ticker}.NS" stock = yf.Ticker(ticker) - # yfinance can sometimes fail for certain tickers or data points. try: info = stock.info except Exception as e: print(f"Could not fetch info for {ticker}: {e}") return {"error": f"Invalid ticker or no data available for {ticker}"} - # check if we got a valid response if not info or info.get('regularMarketPrice') is None: return {"error": f"Invalid ticker or no data available for {ticker}"} - # select key data points relevant to analysis + # --- UPGRADED DATA FETCHING --- data = { "ticker": ticker, "company_name": info.get('longName'), "current_price": info.get('currentPrice') or info.get('regularMarketPrice'), "previous_close": info.get('previousClose'), + "day_high": info.get('dayHigh'), + "day_low": info.get('dayLow'), + "fifty_two_week_high": info.get('fiftyTwoWeekHigh'), + "fifty_two_week_low": info.get('fiftyTwoWeekLow'), + "volume": info.get('volume'), + "average_volume": info.get('averageVolume'), "market_cap": info.get('marketCap'), - "pe_ratio": info.get('trailingPE') or info.get('forwardPE'), + "pe_ratio": info.get('trailingPE'), "pb_ratio": info.get('priceToBook'), "dividend_yield": info.get('dividendYield'), "sector": info.get('sector'), "industry": info.get('industry'), "summary": info.get('longBusinessSummary'), "website": info.get('website'), - "logo_url": info.get('logo_url') + # Get CEO info if available + "ceo": next((officer['name'] for officer in info.get('companyOfficers', []) if 'CEO' in officer.get('title', '')), "N/A"), } - # clean up data by removing any keys with none values return {k: v for k, v in data.items() if v is not None} \ No newline at end of file diff --git a/backend/tools/download_model.py b/backend/tools/download_model.py index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..bc6eeadb727e190b0851f0442960b897bc9c63f8 100644 --- a/backend/tools/download_model.py +++ b/backend/tools/download_model.py @@ -0,0 +1,25 @@ +from sentence_transformers import CrossEncoder +import os + +# Define the model name and the directory to save it to +MODEL_NAME = 'cross-encoder/nli-roberta-base' +MODEL_PATH = './sentiment_model' + +def main(): + """ + Downloads the specified model from Hugging Face and saves it locally. + """ + print(f"Downloading model: {MODEL_NAME}") + + # Check if the directory exists + if not os.path.exists(MODEL_PATH): + os.makedirs(MODEL_PATH) + + # This command downloads the model and saves it to the specified path + model = CrossEncoder(MODEL_NAME) + model.save(MODEL_PATH) + + print(f"Model downloaded and saved to {MODEL_PATH}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/backend/tools/news_tools.py b/backend/tools/news_tools.py index 423043fcaf38422fd044e847e3125e026fd673fc..b02d9078edcbb553b7d25c4dd99efa21183f66f1 100644 --- a/backend/tools/news_tools.py +++ b/backend/tools/news_tools.py @@ -1,86 +1,277 @@ -import snscrape.modules.twitter as sntwitter -import newspaper -# We will now import AutoModelForSequenceClassification and AutoTokenizer -from transformers import AutoTokenizer, AutoModelForSequenceClassification -import torch # We need torch to process the model's output +import yfinance as yf +import requests +from bs4 import BeautifulSoup +from transformers import pipeline +import time +import urllib.parse from typing import List, Dict, Any +import logging -# We will load the model and tokenizer inside the function -sentiment_model = None -tokenizer = None +# Set up logging +logger = logging.getLogger(__name__) + +# --- Model Loading --- +sentiment_pipeline = None MODEL_PATH = '/code/sentiment_model' -def load_sentiment_model(): - """A function to load the model and tokenizer on demand using the transformers library.""" - global sentiment_model, tokenizer - if sentiment_model is None or tokenizer is None: - print("Loading sentiment model and tokenizer for the first time...") - # Load the tokenizer from the local path - tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) - # Load the model from the local path - sentiment_model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH) - print("Sentiment model and tokenizer loaded.") +def load_sentiment_pipeline(): + global sentiment_pipeline + if sentiment_pipeline is None: + logger.info("Loading sentiment analysis pipeline...") + try: + # Try to load the custom model + sentiment_pipeline = pipeline('text-classification', model=MODEL_PATH, tokenizer=MODEL_PATH) + logger.info("Custom sentiment pipeline loaded.") + except Exception as e: + logger.warning(f"Could not load custom model ({e}), using default pipeline...") + try: + # Fallback to default sentiment analysis + sentiment_pipeline = pipeline('sentiment-analysis') + logger.info("Default sentiment pipeline loaded.") + except Exception as e2: + logger.error(f"Could not load any sentiment pipeline: {e2}") + # Create a dummy pipeline that always returns neutral + sentiment_pipeline = lambda texts, **kwargs: [{'label': 'NEUTRAL', 'score': 0.5} for _ in texts] + +# --- Helper function for making web requests --- +def get_session(): + session = requests.Session() + session.headers.update({ + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', + 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', + 'Accept-Language': 'en-US,en;q=0.5', + 'Connection': 'keep-alive', + }) + return session -def analyze_sentiment_with_model(text: str) -> str: - """Uses the loaded model to predict sentiment.""" - # This is the standard way to use a transformers model - inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) - with torch.no_grad(): - logits = sentiment_model(**inputs).logits +# --- PRODUCTION NEWS SCRAPING TOOLS --- +def scrape_google_news(company_name: str) -> List[Dict[str, Any]]: + """Scrape Google News - this is working perfectly based on your test""" + logger.info(f"Fetching Google News for {company_name}...") + articles_data = [] - scores = logits.softmax(dim=1)[0].tolist() - sentiment_map = {0: 'Positive', 1: 'Neutral', 2: 'Negative'} # This order might be different + try: + session = get_session() + + # Try multiple query variations (your test showed this works) + queries = [ + f'"{company_name}" stock', + f'{company_name} share price', + company_name + ] + + for query in queries: + try: + encoded_query = urllib.parse.quote(query) + url = f"https://news.google.com/rss/search?q={encoded_query}&hl=en&gl=US&ceid=US:en" + + response = session.get(url, timeout=15) + if response.status_code == 200: + soup = BeautifulSoup(response.content, 'xml') + items = soup.find_all('item') + + for item in items[:10]: # Top 10 articles + title_elem = item.find('title') + if title_elem and title_elem.text: + articles_data.append({ + "title": title_elem.text.strip(), + "url": item.find('link').text if item.find('link') else '', + "source": item.find('source').text if item.find('source') else 'Google News' + }) + + if articles_data: + break # Stop if we found articles + + except Exception as e: + logger.error(f"Google News query '{query}' failed: {e}") + continue + + except Exception as e: + logger.error(f"Google News scraping failed: {e}") - # Let's verify the model's expected labels - model_labels = sentiment_model.config.id2label - if model_labels: - # e.g., {0: 'positive', 1: 'neutral', 2: 'negative'} - sentiment_map = {int(k): v.capitalize() for k, v in model_labels.items()} - - best_index = scores.index(max(scores)) - return sentiment_map.get(best_index, "Unknown") - + logger.info(f"-> Google News returned {len(articles_data)} articles.") + return articles_data -def get_news_and_sentiment(ticker: str, company_name: str) -> List[Dict[str, Any]]: - load_sentiment_model() - - print(f"Fetching news for {company_name}...") - search_url = f"https://news.google.com/rss/search?q={company_name.replace(' ', '+')}+stock&hl=en-IN&gl=IN&ceid=IN:en" - news_source = newspaper.build(search_url, memoize_articles=False, language='en') +def scrape_yahoo_finance_news(ticker: str) -> List[Dict[str, Any]]: + """Yahoo Finance news scraper""" + logger.info(f"Fetching Yahoo Finance News for {ticker}...") articles_data = [] - for article in news_source.articles[:5]: - try: - article.download(); article.parse(); article.nlp() - if not article.text or len(article.text) < 150: continue + + try: + # Try yfinance first + stock = yf.Ticker(ticker) + news = stock.news + + if news: + for article in news[:10]: # Top 10 + if article.get('title'): + articles_data.append({ + "title": article['title'].strip(), + "url": article.get('link', ''), + "source": article.get('publisher', 'Yahoo Finance'), + }) + + except Exception as e: + logger.error(f"Yahoo Finance scraping failed: {e}") + + logger.info(f"-> Yahoo Finance returned {len(articles_data)} articles.") + return articles_data - sentiment = analyze_sentiment_with_model(article.summary) +def scrape_reddit_mentions(company_name: str) -> List[Dict[str, Any]]: + """Reddit mentions scraper - working well based on your test""" + logger.info(f"Fetching Reddit mentions for {company_name}...") + mentions_data = [] + + try: + session = get_session() + subreddits = ['stocks', 'investing', 'IndiaInvestments', 'SecurityAnalysis', 'ValueInvesting'] + + for subreddit in subreddits: + try: + # Search queries that worked in your test + search_queries = [ + f'"{company_name}"', + company_name.split()[0] if ' ' in company_name else company_name + ] + + for query in search_queries: + search_url = f"https://www.reddit.com/r/{subreddit}/search.json" + params = { + 'q': query, + 'sort': 'new', + 'limit': 10, + 'restrict_sr': 'true', + 't': 'month' + } + + response = session.get(search_url, params=params, timeout=10) + if response.status_code == 200: + data = response.json() + posts = data.get('data', {}).get('children', []) + + for post in posts: + post_data = post.get('data', {}) + if post_data.get('title'): + mentions_data.append({ + "title": post_data['title'].strip(), + "url": f"https://reddit.com{post_data.get('permalink', '')}", + "source": f"r/{subreddit}" + }) + + if posts: + break # Found posts with this query + + time.sleep(0.5) # Rate limiting + + except Exception as e: + logger.error(f"Reddit r/{subreddit} failed: {e}") + + time.sleep(1) # Rate limiting between subreddits + + except Exception as e: + logger.error(f"Reddit scraping failed: {e}") + + logger.info(f"-> Reddit returned {len(mentions_data)} mentions.") + return mentions_data - articles_data.append({ - "title": article.title, - "summary": article.summary, - "url": article.url, - "sentiment": sentiment - }) - except Exception as e: - print(f"Could not process article {article.url}: {e}") - return articles_data +# --- THE MAIN TOOL FUNCTION --- +def get_combined_news_and_sentiment(ticker: str, company_name: str) -> Dict[str, Any]: + """Main function that combines all news sources and analyzes sentiment""" + logger.info(f"Starting news analysis for {ticker} ({company_name})") + + # Load sentiment pipeline + load_sentiment_pipeline() + + all_sources = [] + + # Collect from all sources (based on your successful test) + try: + google_articles = scrape_google_news(company_name) + all_sources.extend(google_articles) + except Exception as e: + logger.error(f"Google News failed: {e}") + + try: + yahoo_articles = scrape_yahoo_finance_news(ticker) + all_sources.extend(yahoo_articles) + except Exception as e: + logger.error(f"Yahoo Finance failed: {e}") + + try: + reddit_mentions = scrape_reddit_mentions(company_name) + all_sources.extend(reddit_mentions) + except Exception as e: + logger.error(f"Reddit failed: {e}") -def get_twitter_sentiment(search_query: str) -> Dict[str, Any]: - load_sentiment_model() + logger.info(f"Total items collected from all sources: {len(all_sources)}") + + if not all_sources: + return { + "articles": [], + "sentiment_summary": { + "total_items": 0, + "positive": 0, + "negative": 0, + "neutral": 0, + "error": "Could not fetch any news from any source." + } + } + + # Perform sentiment analysis + try: + titles = [item['title'] for item in all_sources if item.get('title')] + results = sentiment_pipeline(titles, truncation=True, max_length=512) - print(f"Fetching Twitter sentiment for '{search_query}'...") - tweets = [tweet.rawContent for i, tweet in enumerate(sntwitter.TwitterSearchScraper(f"{search_query} lang:en").get_items()) if i < 50] - if not tweets: return {"error": "No recent tweets found."} + # Map sentiment results back to articles + for i, item in enumerate(all_sources): + if i < len(results): + label = results[i]['label'] + + # Normalize different label formats + if label.upper() in ['POSITIVE', 'POS', 'LABEL_2']: + sentiment = 'Positive' + elif label.upper() in ['NEGATIVE', 'NEG', 'LABEL_0']: + sentiment = 'Negative' + else: + sentiment = 'Neutral' + + item['sentiment'] = sentiment + item['sentiment_score'] = round(results[i]['score'], 2) + else: + # Fallback: simple keyword-based sentiment + title_lower = item['title'].lower() + if any(word in title_lower for word in ['gain', 'rise', 'growth', 'profit', 'strong', 'bullish']): + item['sentiment'] = 'Positive' + item['sentiment_score'] = 0.7 + elif any(word in title_lower for word in ['fall', 'decline', 'loss', 'weak', 'bearish', 'drop']): + item['sentiment'] = 'Negative' + item['sentiment_score'] = 0.7 + else: + item['sentiment'] = 'Neutral' + item['sentiment_score'] = 0.5 - counts = {'Positive': 0, 'Negative': 0, 'Neutral': 0, 'Unknown': 0} - for text in tweets: - sentiment = analyze_sentiment_with_model(text) - counts[sentiment] += 1 + # Count sentiments + counts = {'Positive': 0, 'Negative': 0, 'Neutral': 0} + for item in all_sources: + counts[item.get('sentiment', 'Neutral')] += 1 + + except Exception as e: + logger.error(f"Sentiment analysis failed: {e}") + # Fallback to neutral sentiment for all articles + for item in all_sources: + item['sentiment'] = 'Neutral' + item['sentiment_score'] = 0.5 + counts = {'Positive': 0, 'Negative': 0, 'Neutral': len(all_sources)} + + result = { + "articles": all_sources, + "sentiment_summary": { + "total_items": len(all_sources), + "positive": counts['Positive'], + "negative": counts['Negative'], + "neutral": counts['Neutral'] + } + } - return { - "search_query": search_query, - "total_tweets": len(tweets), - "positive": counts['Positive'], - "negative": counts['Negative'], - "neutral": counts['Neutral'] - } \ No newline at end of file + logger.info(f"News analysis completed: {len(all_sources)} articles, {counts}") + return result \ No newline at end of file diff --git a/backend/tools/prediction_tools.py b/backend/tools/prediction_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..00e8452631f3479034058662430c858501fb3820 --- /dev/null +++ b/backend/tools/prediction_tools.py @@ -0,0 +1,61 @@ +import yfinance as yf +from prophet import Prophet +import pandas as pd +from typing import Dict, Any + +def generate_forecast(ticker: str) -> Dict[str, Any]: + print(f"Generating forecast for ticker {ticker}...") + + stock_data = yf.download(ticker, period="2y", progress=False) + + if stock_data.empty: + return {"error": f"Could not download historical data for {ticker}."} + + # --- THE FINAL, MOST ROBUST FIX FOR THE DATAFRAME --- + # 1. Create a new DataFrame with only the columns we need. + df_prophet = stock_data[['Close']].copy() + # 2. Reset the index to turn the 'Date' index into a column. + df_prophet.reset_index(inplace=True) + # 3. Rename the columns to what Prophet expects. + df_prophet.columns = ['ds', 'y'] + # --- END OF FIX --- + + model = Prophet( + daily_seasonality=False, + weekly_seasonality=True, + yearly_seasonality=True, + changepoint_prior_scale=0.05 + ) + model.fit(df_prophet) + + future = model.make_future_dataframe(periods=30) + forecast = model.predict(future) + + current_price = df_prophet['y'].iloc[-1] + predicted_price_30_days = forecast['yhat'].iloc[-1] + trend = "upward" if predicted_price_30_days > current_price else "downward" + change_percent = ((predicted_price_30_days - current_price) / current_price) * 100 + + forecast_data = { + "summary": ( + f"The model predicts a {trend} trend over the next 30 days. " + f"Current price: {current_price:.2f}, " + f"predicted price in 30 days: {predicted_price_30_days:.2f} " + f"({change_percent:+.2f}% change)." + ), + # Convert datetime objects to strings for JSON compatibility + "history_plot_data": [ + {'ds': r['ds'].isoformat(), 'y': r['y']} for r in df_prophet.tail(90).to_dict('records') + ], + "forecast_plot_data": [ + { + 'ds': r['ds'].isoformat(), + 'yhat': r['yhat'], + 'yhat_lower': r['yhat_lower'], + 'yhat_upper': r['yhat_upper'] + } for r in forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(120).to_dict('records') + ] + } + + print(f"Forecast for {ticker} generated successfully.") + return forecast_data \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index aa3c716be6b4db1a7a307d7a197ccdfe82b16025..6a2b4ddce379b4288194d4c4096e6d6041674d7b 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,12 +1,14 @@ services: + # --- Application Services --- redis: image: redis:7-alpine ports: - "6379:6379" + restart: always backend: build: - context: . # <-- Context is ROOT + context: . dockerfile: ./backend/Dockerfile ports: - "8000:8000" @@ -14,19 +16,21 @@ services: - ./backend:/code/app env_file: - .env - command: python -m uvicorn main:app --host 0.0.0.0 --port 8000 + command: python -m uvicorn main:app --host 0.0.0.0 --port 8000 --reload + restart: always depends_on: - redis worker: build: - context: . # <-- Context is ROOT + context: . dockerfile: ./backend/Dockerfile volumes: - ./backend:/code/app env_file: - .env command: python -m celery -A celery_worker.celery worker --loglevel=info + restart: always depends_on: - redis - backend @@ -40,5 +44,9 @@ services: volumes: - ./frontend:/app - /app/node_modules + restart: always depends_on: - - backend \ No newline at end of file + - backend + +# No other services or volumes are needed. +# The 'mlflow' service and its 'mlflow_server_data' volume have been completely removed. \ No newline at end of file diff --git a/frontend/package-lock.json b/frontend/package-lock.json index b0ffd89f269c13915d5c8c8a65fa95b96e7fd597..9300892b5cc171ace73a3452a7192e0a1aafa927 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -8,21 +8,43 @@ "name": "frontend", "version": "0.0.0", "dependencies": { + "axios": "^1.11.0", + "lucide-react": "^0.542.0", "react": "^19.1.1", - "react-dom": "^19.1.1" + "react-dom": "^19.1.1", + "react-markdown": "^10.1.0", + "recharts": "^3.1.2" }, "devDependencies": { "@eslint/js": "^9.33.0", + "@tailwindcss/line-clamp": "^0.4.4", + "@tailwindcss/typography": "^0.5.16", "@types/react": "^19.1.10", "@types/react-dom": "^19.1.7", "@vitejs/plugin-react": "^5.0.0", + "autoprefixer": "^10.4.21", "eslint": "^9.33.0", "eslint-plugin-react-hooks": "^5.2.0", "eslint-plugin-react-refresh": "^0.4.20", "globals": "^16.3.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.0", "vite": "^7.1.2" } }, + "node_modules/@alloc/quick-lru": { + "version": "5.2.0", + "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", + "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/@ampproject/remapping": { "version": "2.3.0", "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz", @@ -762,9 +784,9 @@ } }, "node_modules/@eslint-community/eslint-utils": { - "version": "4.7.0", - "resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.7.0.tgz", - "integrity": "sha512-dyybb3AcajC7uha6CvhdVRJqaKyn7w2YKqKyAN37NKYgZT36w+iRb0Dymmc5qEJ549c/S31cMMSFd75bteCpCw==", + "version": "4.8.0", + "resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.8.0.tgz", + "integrity": 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"sha512-MR04uvD+07cwl/yhVuVWAtw+3GOR/knlL55Nd/wAdblk27GCVt3lqpTivy/tkJcZoNPzTwS1Y+KMojlLDhoTzg==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-is": "^6.0.0", + "unist-util-visit-parents": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/unist-util-visit-parents": { + "version": "6.0.1", + "resolved": "https://registry.npmjs.org/unist-util-visit-parents/-/unist-util-visit-parents-6.0.1.tgz", + "integrity": "sha512-L/PqWzfTP9lzzEa6CKs0k2nARxTdZduw3zyh8d2NVBnsyvHjSX4TWse388YrrQKbvI8w20fGjGlhgT96WwKykw==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-is": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/update-browserslist-db": { "version": "1.1.3", "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.1.3.tgz", @@ -2689,6 +5655,72 @@ "punycode": "^2.1.0" } }, + "node_modules/use-sync-external-store": { + "version": "1.5.0", + "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.5.0.tgz", + "integrity": "sha512-Rb46I4cGGVBmjamjphe8L/UnvJD+uPPtTkNvX5mZgqdbavhI4EbgIWJiIHXJ8bc/i9EQGPRh4DwEURJ552Do0A==", + "license": "MIT", + "peerDependencies": { + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" + } + }, + "node_modules/util-deprecate": { + "version": "1.0.2", + "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", + "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", + "dev": true, + "license": "MIT" + }, + "node_modules/vfile": { + "version": "6.0.3", + "resolved": "https://registry.npmjs.org/vfile/-/vfile-6.0.3.tgz", + "integrity": "sha512-KzIbH/9tXat2u30jf+smMwFCsno4wHVdNmzFyL+T/L3UGqqk6JKfVqOFOZEpZSHADH1k40ab6NUIXZq422ov3Q==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "vfile-message": "^4.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/vfile-message": { + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/vfile-message/-/vfile-message-4.0.3.tgz", + "integrity": "sha512-QTHzsGd1EhbZs4AsQ20JX1rC3cOlt/IWJruk893DfLRr57lcnOeMaWG4K0JrRta4mIJZKth2Au3mM3u03/JWKw==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-stringify-position": "^4.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/victory-vendor": { + "version": "37.3.6", + "resolved": "https://registry.npmjs.org/victory-vendor/-/victory-vendor-37.3.6.tgz", + "integrity": "sha512-SbPDPdDBYp+5MJHhBCAyI7wKM3d5ivekigc2Dk2s7pgbZ9wIgIBYGVw4zGHBml/qTFbexrofXW6Gu4noGxrOwQ==", + "license": "MIT AND ISC", + "dependencies": { + "@types/d3-array": "^3.0.3", + "@types/d3-ease": "^3.0.0", + "@types/d3-interpolate": "^3.0.1", + "@types/d3-scale": "^4.0.2", + "@types/d3-shape": "^3.1.0", + "@types/d3-time": "^3.0.0", + "@types/d3-timer": "^3.0.0", + "d3-array": "^3.1.6", + "d3-ease": "^3.0.1", + "d3-interpolate": "^3.0.1", + "d3-scale": "^4.0.2", + "d3-shape": "^3.1.0", + "d3-time": "^3.0.0", + "d3-timer": "^3.0.1" + } + }, "node_modules/vite": { "version": "7.1.4", "resolved": "https://registry.npmjs.org/vite/-/vite-7.1.4.tgz", @@ -2764,6 +5796,37 @@ } } }, + "node_modules/vite/node_modules/fdir": { + "version": "6.5.0", + "resolved": "https://registry.npmjs.org/fdir/-/fdir-6.5.0.tgz", + "integrity": "sha512-tIbYtZbucOs0BRGqPJkshJUYdL+SDH7dVM8gjy+ERp3WAUjLEFJE+02kanyHtwjWOnwrKYBiwAmM0p4kLJAnXg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12.0.0" + }, + "peerDependencies": { + "picomatch": "^3 || ^4" + }, + "peerDependenciesMeta": { + "picomatch": { + "optional": true + } + } + }, + "node_modules/vite/node_modules/picomatch": { + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz", + "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12" + }, + "funding": { + "url": "https://github.com/sponsors/jonschlinkert" + } + }, "node_modules/which": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", @@ -2790,6 +5853,101 @@ "node": ">=0.10.0" } }, + "node_modules/wrap-ansi": { + "version": "8.1.0", + "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz", + "integrity": "sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "ansi-styles": "^6.1.0", + "string-width": "^5.0.1", + "strip-ansi": "^7.0.1" + }, + "engines": { + "node": ">=12" + }, + "funding": { + "url": "https://github.com/chalk/wrap-ansi?sponsor=1" + } + }, + "node_modules/wrap-ansi-cjs": { + "name": "wrap-ansi", + "version": "7.0.0", + "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz", + "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==", + "dev": true, + "license": "MIT", + "dependencies": { + "ansi-styles": "^4.0.0", + "string-width": "^4.1.0", + "strip-ansi": "^6.0.0" + }, + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/chalk/wrap-ansi?sponsor=1" + } + }, + "node_modules/wrap-ansi-cjs/node_modules/ansi-regex": { + "version": "5.0.1", + "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz", + "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=8" + } + }, + "node_modules/wrap-ansi-cjs/node_modules/emoji-regex": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", + "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", + "dev": true, + "license": "MIT" + }, + "node_modules/wrap-ansi-cjs/node_modules/string-width": { + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", + "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", + "dev": true, + "license": "MIT", + "dependencies": { + "emoji-regex": "^8.0.0", + "is-fullwidth-code-point": "^3.0.0", + "strip-ansi": "^6.0.1" + }, + "engines": { + "node": ">=8" + } + }, + "node_modules/wrap-ansi-cjs/node_modules/strip-ansi": { + "version": "6.0.1", + "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz", + "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==", + "dev": true, + "license": "MIT", + "dependencies": { + "ansi-regex": "^5.0.1" + }, + "engines": { + "node": ">=8" + } + }, + "node_modules/wrap-ansi/node_modules/ansi-styles": { + "version": "6.2.1", + "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-6.2.1.tgz", + "integrity": "sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12" + }, + "funding": { + "url": "https://github.com/chalk/ansi-styles?sponsor=1" + } + }, "node_modules/yallist": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", @@ -2797,6 +5955,19 @@ "dev": true, "license": "ISC" }, + "node_modules/yaml": { + "version": "2.8.1", + "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.8.1.tgz", + "integrity": "sha512-lcYcMxX2PO9XMGvAJkJ3OsNMw+/7FKes7/hgerGUYWIoWu5j/+YQqcZr5JnPZWzOsEBgMbSbiSTn/dv/69Mkpw==", + "dev": true, + "license": "ISC", + "bin": { + "yaml": "bin.mjs" + }, + "engines": { + "node": ">= 14.6" + } + }, "node_modules/yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", @@ -2809,6 +5980,16 @@ "funding": { "url": "https://github.com/sponsors/sindresorhus" } + }, + "node_modules/zwitch": { + "version": "2.0.4", + "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", + "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", + "license": "MIT", + "funding": { + "type": "github", + "url": "https://github.com/sponsors/wooorm" + } } } } diff --git a/frontend/package.json b/frontend/package.json index c3894aef466357ae7db9afde67aef660d00e9250..b11f860725af059d3ace0f35bf08ddae77b145f3 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -10,18 +10,27 @@ "preview": "vite preview" }, "dependencies": { + "axios": "^1.11.0", + "lucide-react": "^0.542.0", "react": "^19.1.1", - "react-dom": "^19.1.1" + "react-dom": "^19.1.1", + "react-markdown": "^10.1.0", + "recharts": "^3.1.2" }, "devDependencies": { "@eslint/js": "^9.33.0", + "@tailwindcss/line-clamp": "^0.4.4", + "@tailwindcss/typography": "^0.5.16", "@types/react": "^19.1.10", "@types/react-dom": "^19.1.7", "@vitejs/plugin-react": "^5.0.0", + "autoprefixer": "^10.4.21", "eslint": "^9.33.0", "eslint-plugin-react-hooks": "^5.2.0", "eslint-plugin-react-refresh": "^0.4.20", "globals": "^16.3.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.0", "vite": "^7.1.2" } } diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..e99ebc2c0e00cc37de4cefee2bf2a332abb73d8d --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} \ No newline at end of file diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx index f67355ae04c4000eef464262481f6b02daa50789..15c06c2b0430179e4dab49946364ab4189a9eaca 100644 --- a/frontend/src/App.jsx +++ b/frontend/src/App.jsx @@ -1,35 +1,113 @@ -import { useState } from 'react' -import reactLogo from './assets/react.svg' -import viteLogo from '/vite.svg' -import './App.css' +import React, { useState, useEffect } from 'react'; +import Header from './components/Header'; +import JobForm from './components/JobForm'; +import JobStatusCard from './components/JobStatusCard'; +import ResultsDisplay from './components/ResultsDisplay'; +import LoadingSkeleton from './components/LoadingSkeleton'; +import HistoryPanel from './components/HistoryPanel'; +import { createJob, getJob } from './services/api'; +import { XCircle } from 'lucide-react'; function App() { - const [count, setCount] = useState(0) + const [job, setJob] = useState(null); + const [isLoading, setIsLoading] = useState(false); + const [isPolling, setIsPolling] = useState(false); + const [error, setError] = useState(null); + + const handleAnalysisRequest = async (ticker) => { + setIsLoading(true); + setIsPolling(true); + setError(null); + setJob(null); + try { + const response = await createJob(ticker); + setJob(response.data); + } catch (err) { + setError('Failed to create job. Please check the API server and try again.'); + setIsLoading(false); + setIsPolling(false); + } + }; + + const handleSelectHistoryJob = (historyJob) => { + setIsLoading(false); + setIsPolling(false); + setError(null); + setJob(historyJob); + } + + useEffect(() => { + if (!job?.id || !isPolling) return; + + if (job.status !== 'PENDING') { + setIsLoading(false); + } + + const intervalId = setInterval(async () => { + try { + const response = await getJob(job.id); + const updatedJob = response.data; + setJob(updatedJob); + + if (updatedJob.status === 'SUCCESS' || updatedJob.status === 'FAILED') { + clearInterval(intervalId); + setIsPolling(false); + } + } catch (err) { + setError('Failed to poll job status.'); + clearInterval(intervalId); + setIsPolling(false); + } + }, 3000); + + return () => clearInterval(intervalId); + }, [job, isPolling]); return ( - <> -
- - Vite logo - - - React logo - -
-

Vite + React

-
- -

- Edit src/App.jsx and save to test HMR -

-
-

- Click on the Vite and React logos to learn more -

- - ) +
+
+ + +
+
+

+ Enter an Indian stock ticker to receive a comprehensive, AI-powered analysis. +

+ + + + {error &&
{error}
} + + {isLoading && !job && } + + {job && !isLoading && } + + {job?.status === 'SUCCESS' && job.result && ( + + )} + + {job?.status === 'FAILED' && job.result?.error && ( +
+ +

Analysis Failed

+

+ We couldn't complete the analysis for {job.ticker}. + This usually means the stock symbol is incorrect or not listed. +

+

Please double-check the ticker (e.g., RELIANCE.NS) and try again.

+ +
+ Show technical details + 
+                   {job.result.error}
+
+
+
+ )} +
+
+
+ ); } -export default App +export default App; \ No newline at end of file diff --git a/frontend/src/components/Header.jsx b/frontend/src/components/Header.jsx new file mode 100644 index 0000000000000000000000000000000000000000..d06209005eca05c3531c553c2e85dda3584f5d17 --- /dev/null +++ b/frontend/src/components/Header.jsx @@ -0,0 +1,17 @@ +import React from 'react'; +import { Bot } from 'lucide-react'; // Using a cool icon + +function Header() { + return ( +
+
+ +

+ Quantitative Analysis Platform +

+
+
+ ); +} + +export default Header; \ No newline at end of file diff --git a/frontend/src/components/HistoricalChart.jsx b/frontend/src/components/HistoricalChart.jsx new file mode 100644 index 0000000000000000000000000000000000000000..b46a6478edbdea556e48d74b449ea8e613133a8f --- /dev/null +++ b/frontend/src/components/HistoricalChart.jsx @@ -0,0 +1,67 @@ +import React, { useState, useEffect } from 'react'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer } from 'recharts'; +import axios from 'axios'; + +// A more reliable public CORS proxy +const PROXY_URL = 'https://api.allorigins.win/raw?url='; + +const fetchHistoricalData = async (ticker) => { + const yahooUrl = `https://query1.finance.yahoo.com/v8/finance/chart/${ticker}?range=100d&interval=1d`; + const encodedUrl = encodeURIComponent(yahooUrl); + + try { + const response = await axios.get(PROXY_URL + encodedUrl); + const data = response.data.chart.result[0]; + const timestamps = data.timestamp; + const prices = data.indicators.quote[0].close; + + // Filter out any null price points which can crash the chart + return timestamps + .map((ts, i) => ({ + date: new Date(ts * 1000).toLocaleDateString('en-IN', {day: 'numeric', month: 'short'}), + price: prices[i] ? prices[i].toFixed(2) : null, + })) + .filter(point => point.price !== null); + + } catch (error) { + console.error("Failed to fetch historical data for chart:", error); + return []; + } +}; + +function HistoricalChart({ ticker }) { + const [data, setData] = useState([]); + const [loading, setLoading] = useState(true); + + useEffect(() => { + if (ticker) { + setLoading(true); + fetchHistoricalData(ticker).then(chartData => { + setData(chartData); + setLoading(false); + }); + } + }, [ticker]); + + if (loading) return

Loading Chart Data...

; + if (data.length === 0) return

Could not load chart data.

; + + return ( +
+ + + + + + + + + +
+ ); +} + +export default HistoricalChart; \ No newline at end of file diff --git a/frontend/src/components/HistoryPanel.jsx b/frontend/src/components/HistoryPanel.jsx new file mode 100644 index 0000000000000000000000000000000000000000..6a1de958b20315bfa4eeb68a509e9daee05de534 --- /dev/null +++ b/frontend/src/components/HistoryPanel.jsx @@ -0,0 +1,86 @@ +import React, { useState, useEffect } from 'react'; +import { getJobsHistory } from '../services/api'; +import { History, LoaderCircle } from 'lucide-react'; + +function HistoryPanel({ onSelectJob }) { + const [history, setHistory] = useState([]); + const [isLoading, setIsLoading] = useState(false); + const [isOpen, setIsOpen] = useState(false); + + const fetchHistory = () => { + setIsLoading(true); + getJobsHistory() + .then(response => { + // Filter for only completed jobs to make the list cleaner + setHistory(response.data.filter(job => job.status === 'SUCCESS' || job.status === 'FAILED')); + }) + .catch(error => console.error("Failed to fetch history:", error)) + .finally(() => setIsLoading(false)); + }; + + // When the panel opens, fetch the history + const togglePanel = () => { + const newIsOpen = !isOpen; + setIsOpen(newIsOpen); + if (newIsOpen) { + fetchHistory(); + } + }; + + const handleSelect = (job) => { + onSelectJob(job); + setIsOpen(false); + } + + return ( + <> + + + {/* Overlay to close panel when clicking outside */} + {isOpen &&
setIsOpen(false)} className="fixed inset-0 bg-black/50 z-30 transition-opacity">
} + +
+
+

Analysis History

+ +
+
+ {isLoading ? ( +
+ +
+ ) : history.length === 0 ? ( +
+

No past analyses found.

+

Complete an analysis to see it here.

+
+ ) : ( + + )} +
+
+ + ); +} + +export default HistoryPanel; \ No newline at end of file diff --git a/frontend/src/components/JobForm.jsx b/frontend/src/components/JobForm.jsx new file mode 100644 index 0000000000000000000000000000000000000000..d6a29e01e4d258c7a63430498c241b234ae88dd0 --- /dev/null +++ b/frontend/src/components/JobForm.jsx @@ -0,0 +1,44 @@ +import React, { useState } from 'react'; +import { Search, LoaderCircle } from 'lucide-react'; + +// The component now receives 'onAnalyze' and 'isLoading' as props +function JobForm({ onAnalyze, isLoading }) { + const [ticker, setTicker] = useState(''); + + const handleSubmit = (e) => { + e.preventDefault(); + if (!ticker.trim() || isLoading) return; + onAnalyze(ticker); // Call the function passed down from App.jsx + }; + + return ( +
+
+ + + + setTicker(e.target.value.toUpperCase())} + placeholder="e.g., RELIANCE.NS" + className="w-full p-4 bg-transparent text-lg text-gray-200 placeholder-gray-500 focus:outline-none" + disabled={isLoading} + /> + +
+
+ ); +} + +export default JobForm; \ No newline at end of file diff --git a/frontend/src/components/JobStatusCard.jsx b/frontend/src/components/JobStatusCard.jsx new file mode 100644 index 0000000000000000000000000000000000000000..f177c01c74cf13dcd1fb6a10aa32719d8a97ab40 --- /dev/null +++ b/frontend/src/components/JobStatusCard.jsx @@ -0,0 +1,66 @@ +import React from 'react'; +import { LoaderCircle, CheckCircle2, XCircle, FileClock, Database, Search, Bot } from 'lucide-react'; + +function JobStatusCard({ job }) { + const getStatusInfo = (status) => { + // This map now perfectly matches the statuses set in your main_task.py + const statusMap = { + 'PENDING': { + icon: , + text: 'Your analysis is in the queue.', + bgColor: 'bg-yellow-900/50 border-yellow-400/30', + }, + 'DATA_FETCHING': { + icon: , + text: 'Agent 1: Data Agent is fetching fundamentals...', + bgColor: 'bg-blue-900/50 border-blue-400/30', + }, + 'INTELLIGENCE_GATHERING': { + icon: , + text: 'Agent 2: Intelligence Agent is scanning news...', + bgColor: 'bg-blue-900/50 border-blue-400/30', + }, + 'ANALYZING': { + icon: , + text: 'Agent 3: Analyst Agent is generating a report with Gemini...', + bgColor: 'bg-blue-900/50 border-blue-400/30', + }, + 'SUCCESS': { + icon: , + text: 'All agents have completed their tasks!', + bgColor: 'bg-green-900/50 border-green-400/30', + }, + 'FAILED': { + icon: , + text: `Analysis failed. See error below.`, + bgColor: 'bg-red-900/50 border-red-400/30', + } + }; + + return statusMap[status] || { + icon: , + text: 'Waiting for status...', + bgColor: 'bg-gray-800 border-gray-600', + }; + }; + + const statusInfo = getStatusInfo(job.status); + + return ( +
+
+
+ {statusInfo.icon} +
+
+

+ Analysis for {job.ticker} +

+

{statusInfo.text}

+
+
+
+ ); +} + +export default JobStatusCard; \ No newline at end of file diff --git a/frontend/src/components/LoadingSkeleton.jsx b/frontend/src/components/LoadingSkeleton.jsx new file mode 100644 index 0000000000000000000000000000000000000000..513dbc12e6a4ab170eb3ee6a851478f4860f07ed --- /dev/null +++ b/frontend/src/components/LoadingSkeleton.jsx @@ -0,0 +1,46 @@ +import React from 'react'; + +// A simple helper for the pulsing effect +const SkeletonBlock = ({ className }) => ( +
+); + +function LoadingSkeleton() { + return ( +
+ {/* Header Skeleton */} +
+ + +
+ + {/* Key Metrics Skeleton */} +
+ + + + + +
+ + {/* Chart Skeleton */} +
+ + +
+ + {/* AI Report Skeleton */} +
+ +
+ + + + +
+
+
+ ); +} + +export default LoadingSkeleton; \ No newline at end of file diff --git a/frontend/src/components/PredictionChart.jsx b/frontend/src/components/PredictionChart.jsx new file mode 100644 index 0000000000000000000000000000000000000000..80545614a703e5c987daf5f0d762068d33d496b4 --- /dev/null +++ b/frontend/src/components/PredictionChart.jsx @@ -0,0 +1,36 @@ +import React from 'react'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer, Area } from 'recharts'; + +function PredictionChart({ data }) { + if (!data || data.length === 0) return null; + + // Format the date for the X-axis + const formatXAxis = (tickItem) => { + return new Date(tickItem).toLocaleDateString('en-IN', { month: 'short', day: 'numeric' }); + }; + + return ( +
+ + + + + + new Date(label).toLocaleDateString('en-IN')} + /> + + {/* Confidence Interval Area */} + + + + {/* Forecast Line */} + + + +
+ ); +} + +export default PredictionChart; \ No newline at end of file diff --git a/frontend/src/components/ResultsDisplay.jsx b/frontend/src/components/ResultsDisplay.jsx new file mode 100644 index 0000000000000000000000000000000000000000..64bf392f45df200079dbab2bd79f92bb4474b032 --- /dev/null +++ b/frontend/src/components/ResultsDisplay.jsx @@ -0,0 +1,291 @@ +import React from 'react'; +import { Bot, Newspaper, LineChart, Share2, TrendingUp, TrendingDown, ChevronsUp, ChevronsDown, Landmark, Building2, User, ArrowUp, ArrowDown, Minus } from 'lucide-react'; +import ReactMarkdown from 'react-markdown'; +import HistoricalChart from './HistoricalChart'; + +const SectionTitle = ({ icon, title, subtitle }) => ( +
+
+
+ {icon} +
+

+ {title} +

+
+ {subtitle && ( +

{subtitle}

+ )} +
+); + +const MetricCard = ({ title, value, subtitle, trend }) => { + const getTrendIcon = () => { + if (trend > 0) return ; + if (trend < 0) return ; + return ; + }; + + const getTrendColor = () => { + if (trend > 0) return 'text-green-400'; + if (trend < 0) return 'text-red-400'; + return 'text-gray-400'; + }; + + return ( +
+
+
+
+

{title}

+ {trend !== undefined && getTrendIcon()} +
+

{value}

+ {subtitle && ( +

+ {subtitle} +

+ )} +
+
+ ); +}; + +const NewsCard = ({ item, index }) => ( + +
+
+
+ + {item.sentiment} + +
+
+ Live +
+
+ +

+ {item.title} +

+ +
+ {item.source} + Click to read +
+
+ +); + +function ResultsDisplay({ result }) { + if (!result) return null; + + const { + company_name, + current_price, + previous_close, + day_high, + day_low, + fifty_two_week_high, + fifty_two_week_low, + market_cap, + pe_ratio, + sector, + industry, + ceo, + intelligence_briefing, + llm_analysis + } = result; + + const price_change = current_price - previous_close; + const price_change_percent = ((price_change / previous_close) * 100); + const advisorReportText = llm_analysis?.llm_report; + + const handleShare = () => { + if (navigator.share) { + navigator.share({ + title: `${company_name} Analysis`, + text: `Check out this comprehensive analysis of ${company_name}`, + url: window.location.href, + }); + } else { + navigator.clipboard.writeText(window.location.href); + alert('Analysis link copied to clipboard!'); + } + }; + + return ( +
+ +
+
+
+
+
+
+ Live Analysis +
+ +

+ {company_name} +

+ +
+ + + {sector} + + + + {industry} + + + + CEO: {ceo} + +
+
+ + +
+
+ +
+ } + title="Key Metrics" + subtitle="Real-time financial indicators and performance metrics" + /> + +
+ = 0 ? '+' : ''}₹${price_change.toFixed(2)} (${price_change_percent >= 0 ? '+' : ''}${price_change_percent.toFixed(2)}%)`} + trend={price_change} + /> + + + + +
+
+ +
+ } + title="Price Movement" + subtitle="Historical price action over the last 100 trading days" + /> + +
+
+
+ +
+
+
+ + {advisorReportText && ( +
+ } + title="AI Analyst Report" + subtitle="Comprehensive analysis powered by advanced AI models and real-time market data" + /> + +
+
+
+
+
+ AI Generated + • + Powered by AI +
+ +
+

, + h2: ({node, ...props}) =>

, + h3: ({node, ...props}) =>

, + ul: ({node, ...props}) =>
    , + ol: ({node, ...props}) =>
      , + li: ({node, ...props}) =>
    1. , + p: ({node, ...props}) =>

      , + strong: ({node, ...props}) => , + blockquote: ({node, ...props}) => ( +

      + ), + }} + > + {advisorReportText} + +

+
+
+
+ )} + +
+ } + title="Market Intelligence" + subtitle="Latest news and sentiment analysis from multiple sources" + /> + + {intelligence_briefing?.articles?.length > 0 ? ( +
+ {intelligence_briefing.articles.slice(0, 9).map((item, index) => ( + + ))} +
+ ) : ( +
+ +

No news articles found for this symbol.

+

Try checking back later or verify the ticker symbol.

+
+ )} +
+
+ ); +} + +export default ResultsDisplay; \ No newline at end of file diff --git a/frontend/src/index.css b/frontend/src/index.css index 08a3ac9e1e5c44ce374f782d7c4fa3aa70e4c1ff..bd6213e1dfe6b0a79ce7d8b37d0d2dc70f0250bb 100644 --- a/frontend/src/index.css +++ b/frontend/src/index.css @@ -1,68 +1,3 @@ -:root { - font-family: system-ui, Avenir, Helvetica, Arial, sans-serif; - line-height: 1.5; - font-weight: 400; - - color-scheme: light dark; - color: rgba(255, 255, 255, 0.87); - background-color: #242424; - - font-synthesis: none; - text-rendering: optimizeLegibility; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -a { - font-weight: 500; - color: #646cff; - text-decoration: inherit; -} -a:hover { - color: #535bf2; -} - -body { - margin: 0; - display: flex; - place-items: center; - min-width: 320px; - min-height: 100vh; -} - -h1 { - font-size: 3.2em; - line-height: 1.1; -} - -button { - border-radius: 8px; - border: 1px solid transparent; - padding: 0.6em 1.2em; - font-size: 1em; - font-weight: 500; - font-family: inherit; - background-color: #1a1a1a; - cursor: pointer; - transition: border-color 0.25s; -} -button:hover { - border-color: #646cff; -} -button:focus, -button:focus-visible { - outline: 4px auto -webkit-focus-ring-color; -} - -@media (prefers-color-scheme: light) { - :root { - color: #213547; - background-color: #ffffff; - } - a:hover { - color: #747bff; - } - button { - background-color: #f9f9f9; - } -} +@tailwind base; +@tailwind components; +@tailwind utilities; \ No newline at end of file diff --git a/frontend/src/services/api.js b/frontend/src/services/api.js new file mode 100644 index 0000000000000000000000000000000000000000..9bb401f4861db06073620ac3c123f35465d8c00d --- /dev/null +++ b/frontend/src/services/api.js @@ -0,0 +1,21 @@ +import axios from 'axios'; + +const apiClient = axios.create({ + baseURL: 'http://localhost:8000', + headers: { + 'Content-Type': 'application/json', + }, +}); + +export const createJob = (ticker) => { + return apiClient.post('/jobs', { ticker }); +}; + +export const getJob = (jobId) => { + return apiClient.get(`/jobs/${jobId}`); +}; + + +export const getJobsHistory = () => { + return apiClient.get('/jobs'); +}; \ No newline at end of file diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..6223ff99ba2deff5c8498deadf110f465c95f6cf --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,25 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: { + // Add custom animations + animation: { + 'fade-in': 'fadeIn 0.5s ease-in-out', + }, + keyframes: { + fadeIn: { + '0%': { opacity: '0' }, + '100%': { opacity: '1' }, + }, + }, + }, + }, + plugins: [ + require('@tailwindcss/typography'), + require('@tailwindcss/line-clamp'), + ], +} \ No newline at end of file diff --git a/ml_models/.DS_Store b/ml_models/.DS_Store deleted file mode 100644 index 0dc24460f28918210a8c836716ccabf1d1a22656..0000000000000000000000000000000000000000 Binary files a/ml_models/.DS_Store and /dev/null differ diff --git a/ml_models/README.md b/ml_models/README.md deleted file mode 120000 index 90b6f55e1a665b270ef5201f35586e52213f65bf..0000000000000000000000000000000000000000 --- a/ml_models/README.md +++ /dev/null @@ -1 +0,0 @@ -../../blobs/368ed67c15df571f78fa692ab0e20262d0cbe8cc \ No newline at end of file diff --git a/ml_models/config.json b/ml_models/config.json deleted file mode 120000 index 76efb0c20076cce6be8637017e800482650db8e1..0000000000000000000000000000000000000000 --- a/ml_models/config.json +++ /dev/null @@ -1 +0,0 @@ -../../blobs/bedcf6808384112f9da4ecc439f2addbdac3785b \ No newline at end of file diff --git a/ml_models/config.json b/ml_models/config.json new file mode 100644 index 0000000000000000000000000000000000000000..e3d6323dc93e170f3eef66cebb32dbaf4589aee0 --- /dev/null +++ b/ml_models/config.json @@ -0,0 +1,32 @@ +{ + "_name_or_path": "/home/ubuntu/finbert/models/language_model/finbertTRC2", + "architectures": [ + "BertForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": "positive", + "1": "negative", + "2": "neutral" + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "positive": 0, + "negative": 1, + "neutral": 2 + }, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "position_embedding_type": "absolute", + "type_vocab_size": 2, + "vocab_size": 30522 +} diff --git a/ml_models/merges.txt b/ml_models/merges.txt deleted file mode 120000 index a9c624307f42e4ce9e448c8cea9f59d837f833b9..0000000000000000000000000000000000000000 --- a/ml_models/merges.txt +++ /dev/null @@ -1 +0,0 @@ -../../blobs/226b0752cac7789c48f0cb3ec53eda48b7be36cc \ No newline at end of file diff --git a/ml_models/special_tokens_map.json b/ml_models/special_tokens_map.json deleted file mode 120000 index bc467358b0798f807d8eafcd26d236fc78dd7753..0000000000000000000000000000000000000000 --- a/ml_models/special_tokens_map.json +++ /dev/null @@ -1 +0,0 @@ -../../blobs/a7816a407990c2e8d254b10d36beeb972016086e \ No newline at end of file diff --git a/ml_models/special_tokens_map.json b/ml_models/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..e7b0375001f109a6b8873d756ad4f7bbb15fbaa5 --- /dev/null +++ b/ml_models/special_tokens_map.json @@ -0,0 +1 @@ +{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"} \ No newline at end of file diff --git a/ml_models/tokenizer.json b/ml_models/tokenizer.json deleted file mode 120000 index 2c0caea87536175db3f2543707d2c481c3c63df7..0000000000000000000000000000000000000000 --- a/ml_models/tokenizer.json +++ /dev/null @@ -1 +0,0 @@ -../../blobs/d8059234da10f3ae6513891308e25f256dc1db6d \ No newline at end of file diff --git a/ml_models/tokenizer_config.json b/ml_models/tokenizer_config.json deleted file mode 120000 index e6b7dd441948ae1ca4780809a3f4a57ed9449eea..0000000000000000000000000000000000000000 --- a/ml_models/tokenizer_config.json +++ /dev/null @@ -1 +0,0 @@ -../../blobs/45af26cf13c9666e90a0891616e8cf48b0404aed \ No newline at end of file diff --git a/ml_models/tokenizer_config.json b/ml_models/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..cd79adc84d3c96daa03456853766c63141aef734 --- /dev/null +++ b/ml_models/tokenizer_config.json @@ -0,0 +1 @@ +{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "name_or_path": "bert-base-uncased"} \ No newline at end of file diff --git a/ml_models/vocab.json b/ml_models/vocab.json deleted file mode 120000 index c5d1a367b5f8e1efc16d4c0100b61adbfef49a37..0000000000000000000000000000000000000000 --- a/ml_models/vocab.json +++ /dev/null @@ -1 +0,0 @@ -../../blobs/4ebe4bb3f3114daf2e4cc349f24873a1175a35d7 \ No newline at end of file diff --git a/ml_models/vocab.txt b/ml_models/vocab.txt new file mode 100644 index 0000000000000000000000000000000000000000..fb140275c155a9c7c5a3b3e0e77a9e839594a938 --- /dev/null +++ b/ml_models/vocab.txt @@ -0,0 +1,30522 @@ +[PAD] +[unused0] +[unused1] +[unused2] +[unused3] +[unused4] +[unused5] +[unused6] +[unused7] +[unused8] +[unused9] +[unused10] +[unused11] +[unused12] +[unused13] +[unused14] +[unused15] +[unused16] +[unused17] +[unused18] +[unused19] +[unused20] +[unused21] +[unused22] +[unused23] +[unused24] +[unused25] +[unused26] +[unused27] +[unused28] +[unused29] +[unused30] +[unused31] +[unused32] +[unused33] +[unused34] +[unused35] +[unused36] +[unused37] +[unused38] +[unused39] +[unused40] +[unused41] +[unused42] +[unused43] +[unused44] +[unused45] +[unused46] +[unused47] +[unused48] +[unused49] +[unused50] +[unused51] +[unused52] +[unused53] +[unused54] +[unused55] +[unused56] +[unused57] +[unused58] +[unused59] +[unused60] +[unused61] +[unused62] +[unused63] +[unused64] +[unused65] +[unused66] +[unused67] +[unused68] +[unused69] +[unused70] +[unused71] +[unused72] +[unused73] +[unused74] +[unused75] +[unused76] +[unused77] +[unused78] +[unused79] +[unused80] +[unused81] +[unused82] +[unused83] +[unused84] +[unused85] +[unused86] +[unused87] +[unused88] +[unused89] +[unused90] +[unused91] +[unused92] +[unused93] +[unused94] +[unused95] +[unused96] +[unused97] +[unused98] +[UNK] +[CLS] +[SEP] +[MASK] +[unused99] +[unused100] +[unused101] +[unused102] +[unused103] +[unused104] +[unused105] +[unused106] +[unused107] +[unused108] +[unused109] +[unused110] +[unused111] +[unused112] +[unused113] +[unused114] +[unused115] +[unused116] +[unused117] +[unused118] +[unused119] +[unused120] +[unused121] +[unused122] +[unused123] +[unused124] +[unused125] +[unused126] +[unused127] +[unused128] +[unused129] +[unused130] +[unused131] +[unused132] +[unused133] +[unused134] +[unused135] +[unused136] +[unused137] +[unused138] +[unused139] +[unused140] +[unused141] +[unused142] +[unused143] +[unused144] +[unused145] +[unused146] +[unused147] +[unused148] +[unused149] +[unused150] +[unused151] +[unused152] +[unused153] +[unused154] +[unused155] +[unused156] +[unused157] +[unused158] +[unused159] +[unused160] +[unused161] +[unused162] +[unused163] +[unused164] +[unused165] +[unused166] +[unused167] +[unused168] +[unused169] +[unused170] +[unused171] +[unused172] +[unused173] +[unused174] +[unused175] +[unused176] +[unused177] +[unused178] +[unused179] +[unused180] +[unused181] +[unused182] +[unused183] +[unused184] +[unused185] 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+##fi +##fl +##！ +##（ +##） +##， +##－ +##． +##／ +##： +##？ +##～ diff --git a/news_demo.py b/news_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..b6c1b8f58748865c86fc1fd79e58818c43d6a7cc --- /dev/null +++ b/news_demo.py @@ -0,0 +1,490 @@ +import requests +import yfinance as yf +from textblob import TextBlob +import pandas as pd +from datetime import datetime, timedelta +import json +import re +from bs4 import BeautifulSoup +from typing import List, Dict +import time +import urllib.parse + +class FreeStockSentimentAnalyzer: + def __init__(self): + """ + Initialize the Free Stock Sentiment Analyzer + Uses only free APIs and web scraping methods + """ + self.session = requests.Session() + self.session.headers.update({ + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36' + }) + + def get_stock_info(self, symbol: str) -> Dict: + """ + Get basic stock information using yfinance (free) + """ + try: + # Try different suffix combinations for Indian stocks + suffixes_to_try = [ + symbol.upper(), # As is (for US stocks) + f"{symbol.upper()}.NS", # NSE + f"{symbol.upper()}.BO", # BSE + ] + + stock_info = None + working_symbol = None + + for test_symbol in suffixes_to_try: + try: + stock = yf.Ticker(test_symbol) + info = stock.info + hist = stock.history(period="1d") + + # Check if we got valid data + if not hist.empty or 'symbol' in info or 'shortName' in info: + stock_info = info + working_symbol = test_symbol + + # Get current price from history if not in info + if not hist.empty: + current_price = hist['Close'].iloc[-1] + else: + current_price = info.get('currentPrice', info.get('regularMarketPrice', 'N/A')) + + break + except Exception as e: + continue + + if stock_info: + return { + 'symbol': working_symbol, + 'name': stock_info.get('longName', stock_info.get('shortName', symbol)), + 'sector': stock_info.get('sector', 'N/A'), + 'country': stock_info.get('country', 'N/A'), + 'currency': stock_info.get('currency', 'N/A'), + 'market_cap': stock_info.get('marketCap', 'N/A'), + 'current_price': current_price + } + else: + return { + 'symbol': symbol, + 'name': symbol, + 'sector': 'N/A', + 'country': 'N/A', + 'currency': 'N/A', + 'market_cap': 'N/A', + 'current_price': 'N/A' + } + + except Exception as e: + print(f"Error getting stock info: {e}") + return { + 'symbol': symbol, + 'name': symbol, + 'sector': 'N/A', + 'country': 'N/A', + 'currency': 'N/A', + 'market_cap': 'N/A', + 'current_price': 'N/A' + } + + def scrape_google_news(self, stock_name: str, company_name: str) -> List[Dict]: + """ + Scrape news from Google News (free method) + """ + try: + # Create search query for Google News + query = f"{company_name} stock news" + encoded_query = urllib.parse.quote(query) + + url = f"https://news.google.com/rss/search?q={encoded_query}&hl=en-US&gl=US&ceid=US:en" + + response = self.session.get(url, timeout=10) + + if response.status_code == 200: + soup = BeautifulSoup(response.content, 'xml') + items = soup.find_all('item') + + articles = [] + for item in items[:15]: # Limit to 15 articles + try: + title = item.find('title').text if item.find('title') else '' + link = item.find('link').text if item.find('link') else '' + pub_date = item.find('pubDate').text if item.find('pubDate') else '' + description = item.find('description').text if item.find('description') else '' + source = item.find('source').text if item.find('source') else 'Google News' + + articles.append({ + 'title': title, + 'description': BeautifulSoup(description, 'html.parser').get_text()[:200] if description else '', + 'url': link, + 'published_at': pub_date, + 'source': source, + }) + except Exception as e: + continue + + return articles + else: + print(f"Google News scraping failed: {response.status_code}") + return [] + + except Exception as e: + print(f"Error scraping Google News: {e}") + return [] + + def scrape_yahoo_news(self, symbol: str) -> List[Dict]: + """ + Scrape news from Yahoo Finance (free method) + """ + try: + # Try different symbol formats + symbols_to_try = [symbol, f"{symbol}.NS", f"{symbol}.BO"] + + articles = [] + for test_symbol in symbols_to_try: + try: + stock = yf.Ticker(test_symbol) + news = stock.news + + for article in news[:10]: # Limit to 10 articles per symbol + articles.append({ + 'title': article.get('title', ''), + 'description': article.get('summary', ''), + 'url': article.get('link', ''), + 'published_at': datetime.fromtimestamp(article.get('providerPublishTime', 0)).strftime('%Y-%m-%d %H:%M:%S') if article.get('providerPublishTime') else '', + 'source': article.get('publisher', 'Yahoo Finance'), + }) + + if articles: # If we found articles, stop trying other symbols + break + + except Exception as e: + continue + + return articles + + except Exception as e: + print(f"Error scraping Yahoo News: {e}") + return [] + + def scrape_reddit_mentions(self, stock_name: str, company_name: str) -> List[Dict]: + """ + Scrape Reddit mentions using Reddit's JSON API (free) + """ + try: + # Search multiple subreddits + subreddits = ['stocks', 'investing', 'SecurityAnalysis', 'StockMarket', 'ValueInvesting'] + mentions = [] + + for subreddit in subreddits: + try: + # Search for posts mentioning the stock + search_url = f"https://www.reddit.com/r/{subreddit}/search.json" + params = { + 'q': f"{stock_name} OR {company_name}", + 'sort': 'new', + 'limit': 10, + 'restrict_sr': 'true' + } + + response = self.session.get(search_url, params=params, timeout=10) + + if response.status_code == 200: + data = response.json() + posts = data.get('data', {}).get('children', []) + + for post in posts: + post_data = post.get('data', {}) + mentions.append({ + 'title': post_data.get('title', ''), + 'text': post_data.get('selftext', ''), + 'url': f"https://reddit.com{post_data.get('permalink', '')}", + 'score': post_data.get('score', 0), + 'created_at': datetime.fromtimestamp(post_data.get('created_utc', 0)).strftime('%Y-%m-%d %H:%M:%S'), + 'subreddit': subreddit, + 'author': post_data.get('author', 'Unknown'), + 'num_comments': post_data.get('num_comments', 0) + }) + + time.sleep(1) # Be respectful to Reddit's servers + + except Exception as e: + print(f"Error scraping r/{subreddit}: {e}") + continue + + return mentions[:20] # Return top 20 mentions + + except Exception as e: + print(f"Error scraping Reddit: {e}") + return [] + + def get_free_twitter_alternative(self, stock_name: str, company_name: str) -> List[Dict]: + """ + Get social media mentions from free sources (alternative to Twitter API) + This is a placeholder for free social media data sources + """ + try: + # Using Reddit as Twitter alternative since Twitter API is no longer free + print("Note: Using Reddit data as Twitter alternative (Twitter API no longer free)") + return self.scrape_reddit_mentions(stock_name, company_name) + + except Exception as e: + print(f"Error getting social media data: {e}") + return [] + + def analyze_sentiment(self, text: str) -> Dict[str, float]: + """ + Analyze sentiment using TextBlob (free library) + """ + try: + if not text or text.strip() == '': + return {'polarity': 0.0, 'subjectivity': 0.0, 'sentiment_label': 'Neutral'} + + blob = TextBlob(text) + polarity = blob.sentiment.polarity # -1 (negative) to 1 (positive) + subjectivity = blob.sentiment.subjectivity # 0 (objective) to 1 (subjective) + + # Determine sentiment label + if polarity > 0.1: + sentiment_label = 'Positive' + elif polarity < -0.1: + sentiment_label = 'Negative' + else: + sentiment_label = 'Neutral' + + return { + 'polarity': round(polarity, 3), + 'subjectivity': round(subjectivity, 3), + 'sentiment_label': sentiment_label + } + + except Exception as e: + print(f"Error analyzing sentiment: {e}") + return {'polarity': 0.0, 'subjectivity': 0.0, 'sentiment_label': 'Neutral'} + + def calculate_overall_sentiment(self, articles: List[Dict]) -> Dict: + """ + Calculate overall sentiment from all articles/posts + """ + if not articles: + return { + 'overall_sentiment': 'Neutral', + 'average_polarity': 0.0, + 'positive_count': 0, + 'negative_count': 0, + 'neutral_count': 0, + 'total_articles': 0 + } + + polarities = [] + sentiment_counts = {'Positive': 0, 'Negative': 0, 'Neutral': 0} + + for article in articles: + if 'sentiment' in article: + polarity = article['sentiment']['polarity'] + sentiment_label = article['sentiment']['sentiment_label'] + + polarities.append(polarity) + sentiment_counts[sentiment_label] += 1 + + if polarities: + avg_polarity = sum(polarities) / len(polarities) + + if avg_polarity > 0.05: + overall_sentiment = 'Positive' + elif avg_polarity < -0.05: + overall_sentiment = 'Negative' + else: + overall_sentiment = 'Neutral' + else: + avg_polarity = 0.0 + overall_sentiment = 'Neutral' + + return { + 'overall_sentiment': overall_sentiment, + 'average_polarity': round(avg_polarity, 3), + 'positive_count': sentiment_counts['Positive'], + 'negative_count': sentiment_counts['Negative'], + 'neutral_count': sentiment_counts['Neutral'], + 'total_articles': len(articles) + } + + def analyze_stock(self, symbol: str) -> Dict: + """ + Main function to analyze a stock comprehensively + """ + print(f"Analyzing stock: {symbol}") + print("=" * 50) + + # Get stock information + print("Fetching stock information...") + stock_info = self.get_stock_info(symbol) + + company_name = stock_info['name'] + stock_symbol = stock_info['symbol'] + + print(f"Company: {company_name}") + print(f"Symbol: {stock_symbol}") + + # Collect all news and social media data + all_articles = [] + + # Get news from different sources + print("\nFetching news from Google News...") + google_news = self.scrape_google_news(symbol, company_name) + all_articles.extend(google_news) + + print("Fetching news from Yahoo Finance...") + yahoo_news = self.scrape_yahoo_news(symbol) + all_articles.extend(yahoo_news) + + print("Fetching social media mentions...") + social_mentions = self.get_free_twitter_alternative(symbol, company_name) + all_articles.extend(social_mentions) + + # Analyze sentiment for each article + print(f"\nAnalyzing sentiment for {len(all_articles)} items...") + for article in all_articles: + text_to_analyze = "" + + # Combine title and description/text for sentiment analysis + if 'title' in article and article['title']: + text_to_analyze += article['title'] + " " + + if 'description' in article and article['description']: + text_to_analyze += article['description'] + elif 'text' in article and article['text']: + text_to_analyze += article['text'] + + article['sentiment'] = self.analyze_sentiment(text_to_analyze) + + # Calculate overall sentiment + overall_sentiment = self.calculate_overall_sentiment(all_articles) + + # Compile results + results = { + 'stock_info': stock_info, + 'analysis_timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'total_items_analyzed': len(all_articles), + 'news_articles': [article for article in all_articles if 'subreddit' not in article], + 'social_media_mentions': [article for article in all_articles if 'subreddit' in article], + 'sentiment_analysis': overall_sentiment, + 'articles_with_sentiment': all_articles + } + + return results + + def print_results(self, results: Dict): + """ + Print formatted results + """ + print("\n" + "="*80) + print("STOCK SENTIMENT ANALYSIS REPORT") + print("="*80) + + # Stock Information + stock_info = results['stock_info'] + print(f"\n📊 STOCK INFORMATION:") + print(f" Symbol: {stock_info['symbol']}") + print(f" Company: {stock_info['name']}") + print(f" Sector: {stock_info['sector']}") + print(f" Country: {stock_info['country']}") + print(f" Current Price: {stock_info['current_price']} {stock_info['currency']}") + print(f" Market Cap: {stock_info['market_cap']}") + + # Sentiment Summary + sentiment = results['sentiment_analysis'] + print(f"\n🎯 SENTIMENT ANALYSIS SUMMARY:") + print(f" Overall Sentiment: {sentiment['overall_sentiment']}") + print(f" Average Polarity: {sentiment['average_polarity']}") + print(f" Positive Articles: {sentiment['positive_count']}") + print(f" Negative Articles: {sentiment['negative_count']}") + print(f" Neutral Articles: {sentiment['neutral_count']}") + print(f" Total Items Analyzed: {sentiment['total_articles']}") + + # Recent News + news_articles = results['news_articles'] + if news_articles: + print(f"\n📰 LATEST NEWS ({len(news_articles)} articles):") + for i, article in enumerate(news_articles[:5], 1): + sentiment_info = article.get('sentiment', {}) + print(f"\n {i}. {article['title'][:80]}...") + print(f" Source: {article['source']}") + print(f" Sentiment: {sentiment_info.get('sentiment_label', 'N/A')} " + f"(Polarity: {sentiment_info.get('polarity', 'N/A')})") + print(f" URL: {article['url']}") + + # Social Media Mentions + social_mentions = results['social_media_mentions'] + if social_mentions: + print(f"\n💬 SOCIAL MEDIA MENTIONS ({len(social_mentions)} mentions):") + for i, mention in enumerate(social_mentions[:5], 1): + sentiment_info = mention.get('sentiment', {}) + print(f"\n {i}. r/{mention.get('subreddit', 'unknown')}: {mention['title'][:60]}...") + print(f" Score: {mention.get('score', 0)} | Comments: {mention.get('num_comments', 0)}") + print(f" Sentiment: {sentiment_info.get('sentiment_label', 'N/A')} " + f"(Polarity: {sentiment_info.get('polarity', 'N/A')})") + + print(f"\n⏰ Analysis completed at: {results['analysis_timestamp']}") + print("="*80) + +# Example usage and main function +def main(): + """ + Main function to run the stock sentiment analyzer + """ + analyzer = FreeStockSentimentAnalyzer() + + while True: + print("\n🔍 Free Stock Sentiment Analyzer") + print("-" * 40) + stock_symbol = input("Enter stock symbol (e.g., RELIANCE, AAPL, TCS): ").strip() + + if not stock_symbol: + print("Please enter a valid stock symbol.") + continue + + if stock_symbol.lower() in ['quit', 'exit', 'q']: + print("Goodbye!") + break + + try: + # Analyze the stock + results = analyzer.analyze_stock(stock_symbol) + + # Print results + analyzer.print_results(results) + + # Ask if user wants to save results + save_option = input("\nWould you like to save results to JSON file? (y/n): ").strip().lower() + if save_option == 'y': + filename = f"{stock_symbol}_sentiment_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + with open(filename, 'w', encoding='utf-8') as f: + json.dump(results, f, indent=2, ensure_ascii=False) + print(f"Results saved to {filename}") + + except Exception as e: + print(f"Error analyzing stock {stock_symbol}: {e}") + + # Ask if user wants to analyze another stock + continue_option = input("\nAnalyze another stock? (y/n): ").strip().lower() + if continue_option != 'y': + print("Thank you for using Stock Sentiment Analyzer!") + break + +if __name__ == "__main__": + print("Welcome to Free Stock Sentiment Analyzer!") + print("\nRequired Python packages:") + print("pip install yfinance textblob pandas beautifulsoup4 requests lxml") + print("\nNote: This tool uses free APIs and web scraping methods only.") + print("For Twitter data, we use Reddit as an alternative since Twitter API is no longer free.") + + try: + main() + except KeyboardInterrupt: + print("\n\nProgram interrupted. Goodbye!") + except Exception as e: + print(f"An unexpected error occurred: {e}") \ No newline at end of file diff --git a/tmp_down.py b/tmp_down.py index e7af9718fd8cf535fe2cbd805d4c2d87f7c71c1b..c45ec2b69a5a7f694e35095ef5c7ac0e48fd4e14 100644 --- a/tmp_down.py +++ b/tmp_down.py @@ -1,7 +1,6 @@ -from sentence_transformers import CrossEncoder +from transformers import pipeline -print("Starting model download to local cache...") -# This will download the model to a central Hugging Face cache on your Mac -# It might be in ~/.cache/huggingface/hub/ or similar -model = CrossEncoder('cross-encoder/nli-roberta-base') -print("Model download complete!") \ No newline at end of file +print("Downloading NEW model 'ProsusAI/finbert'...") +# Using the pipeline API is the easiest way to download all necessary files +classifier = pipeline('text-classification', model='ProsusAI/finbert') +print("Download complete!") \ No newline at end of file