* 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>
51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
import pytest
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from app.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
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from app.markets.base import ProductInfo
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from app.models import AppModels
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def unified_checks(model: AppModels, input):
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llm = model.get_agent(PREDICTOR_INSTRUCTIONS, output=PredictorOutput)
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result = llm.run(input)
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content = result.content
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assert isinstance(content, PredictorOutput)
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assert content.strategy not in (None, "", "null")
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assert isinstance(content.strategy, str)
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assert isinstance(content.portfolio, list)
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assert len(content.portfolio) > 0
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for item in content.portfolio:
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assert item.asset not in (None, "", "null")
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assert isinstance(item.asset, str)
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assert item.percentage >= 0.0
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assert item.percentage <= 100.0
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assert isinstance(item.percentage, (int, float))
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assert item.motivation not in (None, "", "null")
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assert isinstance(item.motivation, str)
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# La somma delle percentuali deve essere esattamente 100
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total_percentage = sum(item.percentage for item in content.portfolio)
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assert abs(total_percentage - 100) < 0.01 # Permette una piccola tolleranza per errori di arrotondamento
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class TestPredictor:
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@pytest.fixture(scope="class")
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def inputs(self):
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data = []
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for symbol, price in [("BTC", 60000.00), ("ETH", 3500.00), ("SOL", 150.00)]:
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product_info = ProductInfo()
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product_info.symbol = symbol
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product_info.price = price
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data.append(product_info)
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return PredictorInput(data=data, style=PredictorStyle.AGGRESSIVE, sentiment="positivo")
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def test_gemini_model_output(self, inputs):
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unified_checks(AppModels.GEMINI, inputs)
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@pytest.mark.slow
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def test_ollama_qwen_model_output(self, inputs):
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unified_checks(AppModels.OLLAMA_QWEN, inputs)
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@pytest.mark.slow
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def test_ollama_gpt_oss_model_output(self, inputs):
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unified_checks(AppModels.OLLAMA_GPT, inputs)
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