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WIP: Fix Aggregazione market Product #67

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Simo93-rgb wants to merge 13 commits from 62-aggregazione-market-product-non-corretta into main
pull from: 62-aggregazione-market-product-non-corretta
merge into: Berack:main
Conversation 12 Commits 13 Files Changed 4 +14 -38
1 changed files with 14 additions and 38 deletions
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52
src/app/api/tools/symbols_tool.py
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@@ -13,38 +13,27 @@ logging = logging.getLogger("crypto_symbols")
copilot-pull-request-reviewer[bot] commented 2025-10-30 15:43:55 +01:00 (Migrated from github.com)
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Italian comment in English codebase. Should be translated to 'Search both in name and symbol, ignoring case'.

            # Search both in name and symbol, ignoring case
Italian comment in English codebase. Should be translated to 'Search both in name and symbol, ignoring case'. ```suggestion # Search both in name and symbol, ignoring case ```
copilot-pull-request-reviewer[bot] commented 2025-10-30 15:43:56 +01:00 (Migrated from github.com)
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Italian comment in English codebase. Should be translated to 'Convert the result to a list of tuples'.

            # Convert the result to a list of tuples
Italian comment in English codebase. Should be translated to 'Convert the result to a list of tuples'. ```suggestion # Convert the result to a list of tuples ```
Simo93-rgb commented 2025-10-30 20:32:54 +01:00 (Migrated from github.com)
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Non mi ricordo, non ho memoria di quella cosa

Non mi ricordo, non ho memoria di quella cosa
Simo93-rgb commented 2025-10-30 20:32:58 +01:00 (Migrated from github.com)
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Non mi ricordo, non ho memoria di quella cosa

Non mi ricordo, non ho memoria di quella cosa
BASE_URL = "https://finance.yahoo.com/markets/crypto/all/"
class CryptoSymbolsTools(Toolkit):
"""
Class for obtaining cryptocurrency symbols via Yahoo Finance.
(This class-level docstring is for developers).
"""
def __init__(self, cache_file: str = 'resources/cryptos.csv'):
self.cache_file = cache_file
try:
self.final_table = pd.read_csv(self.cache_file) if os.path.exists(self.cache_file) else pd.DataFrame(
columns=['Symbol', 'Name'])
except Exception:
self.final_table = pd.DataFrame(columns=['Symbol', 'Name'])
self.final_table = pd.read_csv(self.cache_file) if os.path.exists(self.cache_file) else pd.DataFrame() # type: ignore
Toolkit.__init__(self, # type: ignore
name="Crypto Symbols Tool",
instructions="A utility tool to find and verify the correct cryptocurrency symbols (tickers). " \
"Use this to translate a cryptocurrency name (e.g., 'Bitcoin') into its official symbol " \
"(e.g., 'BTC-USD') *before* delegating tasks to the MarketAgent.",
tools=[
self.get_all_symbols,
self.get_symbols_by_name,
],
)
name="Crypto Symbols Tool",
instructions="Tool to get cryptocurrency symbols and search them by name.",
tools=[
self.get_all_symbols,
self.get_symbols_by_name,
],
)
def get_all_symbols(self) -> list[str]:
"""
Returns a complete list of all available cryptocurrency symbols (tickers).
Warning: This list can be very long. Prefer 'get_symbols_by_name'
if you are searching for a specific asset.
The list could be very long, prefer using 'get_symbols_by_name' for specific searches.
Returns:
list[str]: A comprehensive list of all supported crypto symbols (e.g., "BTC-USD", "ETH-USD").
@@ -54,32 +43,19 @@ class CryptoSymbolsTools(Toolkit):
def get_symbols_by_name(self, query: str) -> list[tuple[str, str]]:
"""
Searches the cryptocurrency database for assets matching a name or symbol.
Use this to find the exact, correct symbol for a cryptocurrency name.
(e.g., query="Bitcoin" might return [("BTC-USD", "Bitcoin USD")]).
Args:
query (str): The name, partial name, or symbol to search for (e.g., "Bitcoin", "ETH").
Returns:
list[tuple[str, str]]: A list of tuples, where each tuple contains
the (symbol, full_name) of a matching asset.
Returns an empty list if no matches are found.
"""
if self.final_table.empty or 'Name' not in self.final_table.columns or 'Symbol' not in self.final_table.columns:
return []
try:
# Cerca sia nel nome che nel simbolo, ignorando maiuscole/minuscole
mask = self.final_table['Name'].str.contains(query, case=False, na=False) | \
self.final_table['Symbol'].str.contains(query, case=False, na=False)
filtered_df = self.final_table[mask]
# Converte il risultato in una lista di tuple
return list(zip(filtered_df['Symbol'], filtered_df['Name']))
except Exception:
return []
query_lower = query.lower()
positions = self.final_table['Name'].str.lower().str.contains(query_lower) | \
self.final_table['Symbol'].str.lower().str.contains(query_lower)
filtered_df = self.final_table[positions]
return list(zip(filtered_df['Symbol'], filtered_df['Name']))
async def fetch_crypto_symbols(self, force_refresh: bool = False) -> None:
"""