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>
This commit was merged in pull request #8.
This commit is contained in:
Simo
@@ -1,39 +0,0 @@
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from agno.tools import Toolkit
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from app.markets import MarketAPIs
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# TODO (?) in futuro fare in modo che la LLM faccia da sé per il mercato
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# Non so se può essere utile, per ora lo lascio qui
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# per ora mettiamo tutto statico e poi, se abbiamo API-Key senza limiti
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# possiamo fare in modo di far scegliere alla LLM quale crypto proporre
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# in base alle sue proprie chiamate API
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class MarketToolkit(Toolkit):
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def __init__(self):
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self.market_api = MarketAPIs("USD") # change currency if needed
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super().__init__(
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name="Market Toolkit",
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tools=[
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self.get_historical_data,
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self.get_current_price,
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],
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)
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def get_historical_data(self, symbol: str):
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return self.market_api.get_historical_prices(symbol)
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def get_current_price(self, symbol: str):
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return self.market_api.get_products(symbol)
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def prepare_inputs():
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pass
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def instructions():
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return """
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Utilizza questo strumento per ottenere dati di mercato storici e attuali per criptovalute specifiche.
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Puoi richiedere i prezzi storici o il prezzo attuale di una criptovaluta specifica.
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Esempio di utilizzo:
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- get_historical_data("BTC")
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- get_current_price("ETH")
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"""
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90
src/app/agents/market_agent.py
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90
src/app/agents/market_agent.py
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from typing import Union, List, Dict, Optional, Any, Iterator, Sequence
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from agno.agent import Agent
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from agno.models.message import Message
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from agno.run.agent import RunOutput, RunOutputEvent
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from pydantic import BaseModel
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from app.toolkits.market_toolkit import MarketToolkit
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from app.markets.base import ProductInfo # modello dati già definito nel tuo progetto
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class MarketAgent(Agent):
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"""
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Wrapper che trasforma MarketToolkit in un Agent compatibile con Team.
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Produce sia output leggibile (content) che dati strutturati (metadata).
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"""
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def __init__(self, currency: str = "USD"):
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super().__init__()
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self.toolkit = MarketToolkit()
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self.currency = currency
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self.name = "MarketAgent"
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def run(
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self,
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input: Union[str, List, Dict, Message, BaseModel, List[Message]],
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*,
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stream: Optional[bool] = None,
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stream_intermediate_steps: Optional[bool] = None,
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user_id: Optional[str] = None,
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session_id: Optional[str] = None,
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session_state: Optional[Dict[str, Any]] = None,
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audio: Optional[Sequence[Any]] = None,
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images: Optional[Sequence[Any]] = None,
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videos: Optional[Sequence[Any]] = None,
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files: Optional[Sequence[Any]] = None,
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retries: Optional[int] = None,
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knowledge_filters: Optional[Dict[str, Any]] = None,
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add_history_to_context: Optional[bool] = None,
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add_dependencies_to_context: Optional[bool] = None,
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add_session_state_to_context: Optional[bool] = None,
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dependencies: Optional[Dict[str, Any]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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yield_run_response: bool = False,
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debug_mode: Optional[bool] = None,
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**kwargs: Any,
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) -> Union[RunOutput, Iterator[Union[RunOutputEvent, RunOutput]]]:
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# 1. Estraggo la query dal parametro "input"
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if isinstance(input, str):
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query = input
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elif isinstance(input, dict) and "query" in input:
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query = input["query"]
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elif isinstance(input, Message):
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query = input.content
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elif isinstance(input, BaseModel):
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query = str(input)
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elif isinstance(input, list) and input and isinstance(input[0], Message):
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query = input[0].content
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else:
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query = str(input)
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# 2. Individuo i simboli da analizzare
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symbols = []
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for token in query.upper().split():
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if token in ("BTC", "ETH", "XRP", "LTC", "BCH"): # TODO: estendere dinamicamente
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symbols.append(token)
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if not symbols:
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symbols = ["BTC", "ETH"] # default
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# 3. Recupero i dati dal toolkit
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results = []
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products: List[ProductInfo] = []
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try:
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products.extend(self.toolkit.get_current_prices(symbols)) # supponiamo ritorni un ProductInfo o simile
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# Usa list comprehension per iterare symbols e products insieme
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results.extend([
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f"{symbol}: ${product.price:.2f}" if hasattr(product, 'price') and product.price else f"{symbol}: N/A"
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for symbol, product in zip(symbols, products)
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])
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except Exception as e:
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results.extend(f"Errore: Impossibile recuperare i dati di mercato\n{str(e)}")
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# 4. Preparo output leggibile + metadati strutturati
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output_text = "📊 Dati di mercato:\n" + "\n".join(results)
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return RunOutput(
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content=output_text,
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metadata={"products": products}
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)
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@@ -1,4 +1,34 @@
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class NewsAgent:
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from agno.agent import Agent
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class NewsAgent(Agent):
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"""
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Gli agenti devono esporre un metodo run con questa firma.
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def run(
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self,
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input: Union[str, List, Dict, Message, BaseModel, List[Message]],
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*,
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stream: Optional[bool] = None,
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stream_intermediate_steps: Optional[bool] = None,
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user_id: Optional[str] = None,
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session_id: Optional[str] = None,
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session_state: Optional[Dict[str, Any]] = None,
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audio: Optional[Sequence[Any]] = None,
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images: Optional[Sequence[Any]] = None,
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videos: Optional[Sequence[Any]] = None,
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files: Optional[Sequence[Any]] = None,
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retries: Optional[int] = None,
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knowledge_filters: Optional[Dict[str, Any]] = None,
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add_history_to_context: Optional[bool] = None,
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add_dependencies_to_context: Optional[bool] = None,
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add_session_state_to_context: Optional[bool] = None,
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dependencies: Optional[Dict[str, Any]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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yield_run_response: bool = False,
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debug_mode: Optional[bool] = None,
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**kwargs: Any,
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) -> Union[RunOutput, Iterator[Union[RunOutputEvent, RunOutput]]]:
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"""
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@staticmethod
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def analyze(query: str) -> str:
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# Mock analisi news
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@@ -1,51 +0,0 @@
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from enum import Enum
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from app.markets.base import ProductInfo
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from pydantic import BaseModel, Field
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class PredictorStyle(Enum):
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CONSERVATIVE = "Conservativo"
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AGGRESSIVE = "Aggressivo"
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class PredictorInput(BaseModel):
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data: list[ProductInfo] = Field(..., description="Market data as a list of ProductInfo")
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style: PredictorStyle = Field(..., description="Prediction style")
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sentiment: str = Field(..., description="Aggregated sentiment from news and social analysis")
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class ItemPortfolio(BaseModel):
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asset: str = Field(..., description="Name of the asset")
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percentage: float = Field(..., description="Percentage allocation to the asset")
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motivation: str = Field(..., description="Motivation for the allocation")
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class PredictorOutput(BaseModel):
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strategy: str = Field(..., description="Concise operational strategy in Italian")
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portfolio: list[ItemPortfolio] = Field(..., description="List of portfolio items with allocations")
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PREDICTOR_INSTRUCTIONS = """
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You are an **Allocation Algorithm (Crypto-Algo)** specialized in analyzing market data and sentiment to generate an investment strategy and a target portfolio.
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Your sole objective is to process the input data and generate the strictly structured output as required by the response format. **You MUST NOT provide introductions, preambles, explanations, conclusions, or any additional comments that are not strictly required.**
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## Processing Instructions (Absolute Rule)
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The allocation strategy must be **derived exclusively from the "Allocation Logic" corresponding to the requested *style*** and the provided market/sentiment data. **DO NOT** use external or historical knowledge.
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## Allocation Logic
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### "Aggressivo" Style (Aggressive)
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* **Priority:** Maximizing return (high volatility accepted).
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* **Focus:** Higher allocation to **non-BTC/ETH assets** with high momentum potential (Altcoins, mid/low-cap assets).
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* **BTC/ETH:** Must serve as a base (anchor), but their allocation **must not exceed 50%** of the total portfolio.
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* **Sentiment:** Use positive sentiment to increase exposure to high-risk assets.
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### "Conservativo" Style (Conservative)
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* **Priority:** Capital preservation (volatility minimized).
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* **Focus:** Major allocation to **BTC and/or ETH (Large-Cap Assets)**.
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* **BTC/ETH:** Their allocation **must be at least 70%** of the total portfolio.
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* **Altcoins:** Any allocations to non-BTC/ETH assets must be minimal (max 30% combined) and for assets that minimize speculative risk.
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* **Sentiment:** Use positive sentiment only as confirmation for exposure, avoiding reactions to excessive "FOMO" signals.
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## Output Requirements (Content MUST be in Italian)
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1. **Strategy (strategy):** Must be a concise operational description **in Italian ("in Italiano")**, with a maximum of 5 sentences.
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2. **Portfolio (portfolio):** The sum of all percentages must be **exactly 100%**. The justification (motivation) for each asset must be a single clear sentence **in Italian ("in Italiano")**.
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"""
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@@ -1,4 +1,35 @@
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class SocialAgent:
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from agno.agent import Agent
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class SocialAgent(Agent):
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"""
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Gli agenti devono esporre un metodo run con questa firma.
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def run(
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self,
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input: Union[str, List, Dict, Message, BaseModel, List[Message]],
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*,
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stream: Optional[bool] = None,
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stream_intermediate_steps: Optional[bool] = None,
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user_id: Optional[str] = None,
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session_id: Optional[str] = None,
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session_state: Optional[Dict[str, Any]] = None,
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audio: Optional[Sequence[Any]] = None,
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images: Optional[Sequence[Any]] = None,
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videos: Optional[Sequence[Any]] = None,
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files: Optional[Sequence[Any]] = None,
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retries: Optional[int] = None,
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knowledge_filters: Optional[Dict[str, Any]] = None,
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add_history_to_context: Optional[bool] = None,
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add_dependencies_to_context: Optional[bool] = None,
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add_session_state_to_context: Optional[bool] = None,
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dependencies: Optional[Dict[str, Any]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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yield_run_response: bool = False,
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debug_mode: Optional[bool] = None,
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**kwargs: Any,
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) -> Union[RunOutput, Iterator[Union[RunOutputEvent, RunOutput]]]:
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"""
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@staticmethod
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def analyze(query: str) -> str:
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# Mock analisi social
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