3 market api (#8)

* Creazione branch tool, refactor degli import e soppressione dei warning

* Update pytest configuration and dependencies in pyproject.toml

* Add news API integration and related configurations

- Update .env.example to include NEWS_API_KEY configuration
- Add newsapi-python dependency in pyproject.toml
- Implement NewsAPI class for fetching news articles
- Create Article model for structured news data
- Add tests for NewsAPI functionality in test_news_api.py
- Update pytest configuration to include news marker

* Add news API functionality and update tests for article retrieval

* ToDo:
1. Aggiungere un aggregator per i dati recuperati dai provider.
2. Lavorare effettivamente all'issue

Done:
1. creati test per i provider
2. creato market_providers_api_demo.py per mostrare i dati recuperati dalle api dei providers
3. aggiornato i provider
4. creato il provider binance sia pubblico che con chiave
5. creato error_handler.py per gestire decoratori e utilità: retry automatico, gestione timeout...

* Refactor news API integration to use NewsApiWrapper and GnewsWrapper; add tests for Gnews API functionality

* Add CryptoPanic API integration and related tests; update .env.example and test configurations

* Implement WrapperHandler for managing multiple news API wrappers; add tests for wrapper functionality

* Enhance WrapperHandler
- docstrings
- add try_call_all method
- update tests

* pre merge con phil

* Add DuckDuckGo and Google News wrappers; refactor CryptoPanic and NewsAPI

- Implemented DuckDuckGoWrapper for news retrieval using DuckDuckGo tools.
- Added GoogleNewsWrapper for accessing Google News RSS feed.
- Refactored CryptoPanicWrapper to unify get_top_headlines and get_latest_news methods.
- Updated NewsApiWrapper to simplify top headlines retrieval.
- Added tests for DuckDuckGo and Google News wrappers.
- Enhanced documentation for CryptoPanicWrapper and NewsApiWrapper.
- Created base module for social media integrations.

* - Refactor struttura progetto: divisione tra agent e toolkit

* Refactor try_call_all method to return a dictionary of results; update tests for success and partial failures

* Fix class and test method names for DuckDuckGoWrapper

* Add Reddit API wrapper and related tests; update environment configuration

* pre merge con giacomo

* Fix import statements

* Fixes
- separated tests
- fix tests
- fix bugs reintroduced my previous merge

* Refactor market API wrappers to streamline product and price retrieval methods

* Add BinanceWrapper to market API exports

* Finito ISSUE 3

* Final review
- rm PublicBinanceAgent & updated demo
- moved in the correct folder some tests
- fix binance bug

---------

Co-authored-by: trojanhorse47 <cosmomemory@hotmail.it>
Co-authored-by: Berack96 <giacomobertolazzi7@gmail.com>
Co-authored-by: Giacomo Bertolazzi <31776951+Berack96@users.noreply.github.com>

src/app/pipeline.py

@@ -0,0 +1,83 @@
+from typing import List
+
+from agno.team import Team
+from agno.utils.log import log_info
+
+from app.agents.market_agent import MarketAgent
+from app.agents.news_agent import NewsAgent
+from app.agents.social_agent import SocialAgent
+from app.models import AppModels
+from app.predictor import PredictorStyle, PredictorInput, PredictorOutput, PREDICTOR_INSTRUCTIONS
+
+
+class Pipeline:
+    """
+    Pipeline coordinata: esegue tutti gli agenti del Team, aggrega i risultati e invoca il Predictor.
+    """
+
+    def __init__(self):
+        # Inizializza gli agenti
+        self.market_agent = MarketAgent()
+        self.news_agent = NewsAgent()
+        self.social_agent = SocialAgent()
+
+        # Crea il Team
+        self.team = Team(name="CryptoAnalysisTeam", members=[self.market_agent, self.news_agent, self.social_agent])
+
+        # Modelli disponibili e Predictor
+        self.available_models = AppModels.availables()
+        self.predictor_model = self.available_models[0]
+        self.predictor = self.predictor_model.get_agent(PREDICTOR_INSTRUCTIONS, output=PredictorOutput) # type: ignore[arg-type]
+
+        # Stili
+        self.styles = list(PredictorStyle)
+        self.style = self.styles[0]
+
+    def choose_provider(self, index: int):
+        self.predictor_model = self.available_models[index]
+        self.predictor = self.predictor_model.get_agent(PREDICTOR_INSTRUCTIONS, output=PredictorOutput) # type: ignore[arg-type]
+
+    def choose_style(self, index: int):
+        self.style = self.styles[index]
+
+    def interact(self, query: str) -> str:
+        """
+        Esegue il Team (Market + News + Social), aggrega i risultati e invoca il Predictor.
+        """
+        # Step 1: raccogli output del Team
+        team_results = self.team.run(query)
+        if isinstance(team_results, dict):  # alcuni Team possono restituire dict
+            pieces = [str(v) for v in team_results.values()]
+        elif isinstance(team_results, list):
+            pieces = [str(r) for r in team_results]
+        else:
+            pieces = [str(team_results)]
+        aggregated_text = "\n\n".join(pieces)
+
+        # Step 2: prepara input per Predictor
+        predictor_input = PredictorInput(
+            data=[],  # TODO: mappare meglio i dati di mercato in ProductInfo
+            style=self.style,
+            sentiment=aggregated_text
+        )
+
+        # Step 3: chiama Predictor
+        result = self.predictor.run(predictor_input)
+        prediction: PredictorOutput = result.content
+
+        # Step 4: formatta output finale
+        portfolio_lines = "\n".join(
+            [f"{item.asset} ({item.percentage}%): {item.motivation}" for item in prediction.portfolio]
+        )
+        output = (
+            f"📊 Strategia ({self.style.value}): {prediction.strategy}\n\n"
+            f"💼 Portafoglio consigliato:\n{portfolio_lines}"
+        )
+
+        return output
+
+    def list_providers(self) -> List[str]:
+        return [m.name for m in self.available_models]
+
+    def list_styles(self) -> List[str]:
+        return [s.value for s in self.styles]