Metadata-Version: 2.4
Name: fba_finance
Version: 0.1.4
Summary: Multi-source financial data library with anti-rate-limiting strategies
Home-page: https://github.com/faux62/fba_finance
Author: Fausto Bandini
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests>=2.31.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: yfinance>=0.2.32
Requires-Dist: yahooquery>=2.3.0
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# FBA Finance 📈

[![PyPI version](https://badge.fury.io/py/fba-finance.svg)](https://badge.fury.io/py/fba-finance)
[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://pepy.tech/badge/fba-finance)](https://pepy.tech/project/fba-finance)

**Libreria Python multi-source per acquisizione dati finanziari con strategie anti-rate-limiting**

FBA Finance è una libreria completa che aggrega **multiple fonti di dati finanziari gratuite** con fallback automatico, caching intelligente e rate limiting distribuito, permettendoti di superare i limiti di utilizzo delle singole API.

## 🎯 Caratteristiche Principali

- ✅ **Multi-Provider con Fallback Automatico**: Se una fonte fallisce o è rate-limited, passa automaticamente alla successiva
- ✅ **Dati Real-Time e Storici**: Quote in tempo quasi reale e dati storici OHLCV
- ✅ **Tutti i Mercati Mondiali**: Azioni, ETF, Indici, Forex, Crypto, Futures
- ✅ **Anti-Rate-Limiting**: Rate limiter intelligente per ogni provider
- ✅ **Caching Multi-Backend**: Memory, Disk (DiskCache), Redis
- ✅ **Web Scraping Resiliente**: Rotating user agents, proxy support
- ✅ **100% Gratuito e Open Source**: Nessun costo, tutte fonti free tier

## 🔌 Provider Supportati

| Provider | Quote Real-Time | Dati Storici | Rate Limit (Free) | API Key Richiesta |
|----------|----------------|--------------|-------------------|-------------------|
| **yfinance** | ✅ | ✅ | ~60 req/min | ❌ No |
| **yahooquery** | ✅ | ✅ | ~60 req/min | ❌ No |
| **Yahoo Scraper** | ✅ | ⚠️ Limitato | ~30 req/min | ❌ No |
| **Twelve Data** | ✅ | ✅ | 800 req/giorno | ✅ Sì (gratis) |
| **Alpha Vantage** | ✅ | ✅ | 500 req/giorno | ✅ Sì (gratis) |

**Capacità teorica combinata**: Migliaia di richieste al giorno senza pagare!

## 📦 Installazione

### Installazione da PyPI (Consigliata)

```bash
# Installazione base (Yahoo Finance + fallback)
pip install fba-finance

# Installazione completa (tutti i provider)
pip install fba-finance[full]
```

### Installazione da Sorgente (per sviluppatori)

```bash
# Clona il repository
git clone https://github.com/faux62/fba_finance.git
cd fba_finance

# Installazione base
pip install -e .

# Installazione completa
pip install -e ".[full]"
```

## 🚀 Quick Start

### Esempio Base - Quote in Real-Time

```python
from fba_finance import FinanceClient

# Inizializza il client
client = FinanceClient()

# Ottieni una quote
quote = client.get_quote("AAPL")
print(f"{quote.symbol}: ${quote.price}")
print(f"Change: {quote.change_percent:.2f}%")

# Ottieni multiple quote (batch)
quotes = client.get_quotes(["AAPL", "MSFT", "GOOGL", "TSLA"])
for symbol, quote in quotes.items():
    print(f"{symbol}: ${quote.price}")
```

### Dati Storici

```python
from fba_finance import FinanceClient
from datetime import datetime, timedelta

client = FinanceClient()

# Ottieni 1 anno di dati giornalieri
data = client.get_historical("AAPL", period="1y", interval="1d")
print(data.data.head())

# Ottieni dati per un periodo specifico
end = datetime.now()
start = end - timedelta(days=30)
data = client.get_historical("AAPL", start=start, end=end, interval="1h")

# Il DataFrame ha le colonne: date, open, high, low, close, volume
df = data.data
print(df[['date', 'close', 'volume']])
```

### Ticker Internazionali

```python
# Mercati globali
stocks = [
    "AAPL",           # USA - NASDAQ
    "TSLA",           # USA - NASDAQ  
    "0700.HK",        # Hong Kong - Tencent
    "BMW.DE",         # Germania - BMW
    "MC.PA",          # Francia - LVMH
    "FTSEMIB.MI",     # Italia - FTSE MIB Index
    "UCG.MI",         # Italia - UniCredit
    "ENI.MI",         # Italia - ENI
]

quotes = client.get_quotes(stocks)
for symbol, quote in quotes.items():
    print(f"{symbol}: {quote.currency} {quote.price}")
```

### Forex e Crypto

```python
# Forex pairs
forex = client.get_quote("EURUSD=X")
print(f"EUR/USD: {forex.price}")

# Cryptocurrencies
btc = client.get_quote("BTC-USD")
eth = client.get_quote("ETH-USD")
print(f"Bitcoin: ${btc.price}")
print(f"Ethereum: ${eth.price}")
```

## 🔧 Configurazione Avanzata

### Setup API Keys (Opzionale ma Raccomandato)

Crea un file `.env` nella root del progetto:

```bash
# Copia il template
cp .env.example .env
```

Modifica `.env` con le tue API keys (tutte gratuite!):

```env
# Alpha Vantage - https://www.alphavantage.co/support/#api-key
ALPHA_VANTAGE_API_KEY=your_key_here

# Twelve Data - https://twelvedata.com/
TWELVE_DATA_API_KEY=your_key_here

# Cache settings
CACHE_ENABLED=true
CACHE_BACKEND=disk  # Options: disk, redis, memory
CACHE_TTL_REALTIME=60
CACHE_TTL_HISTORICAL=3600
```

### Personalizza Provider Priority

```python
from fba_finance import FinanceClient

# Definisci l'ordine di fallback
custom_priority = [
    "twelvedata",      # Prova prima Twelve Data
    "yahooquery",      # Poi yahooquery
    "yfinance",        # Poi yfinance standard
    "yahoo_scraper",   # Infine scraper
]

client = FinanceClient(provider_priority=custom_priority)
quote = client.get_quote("AAPL")
```

### Forza Provider Specifico

```python
# Usa solo un provider specifico
quote = client.get_quote("AAPL", provider="twelvedata")
data = client.get_historical("AAPL", period="1mo", provider="yfinance")
```

### Gestione Cache

```python
# Disabilita cache
client = FinanceClient(use_cache=False)

# Cancella cache
client.clear_cache()

# Usa Redis invece di disk cache
# Nel file .env:
# CACHE_BACKEND=redis
# REDIS_HOST=localhost
# REDIS_PORT=6379
```

## 📊 Esempi Avanzati

### Esempio 1: Portfolio Tracker

```python
from fba_finance import FinanceClient

client = FinanceClient()

portfolio = {
    "AAPL": 10,   # 10 shares
    "MSFT": 5,
    "GOOGL": 3,
    "TSLA": 2,
}

quotes = client.get_quotes(list(portfolio.keys()))

total_value = 0
for symbol, shares in portfolio.items():
    if symbol in quotes:
        price = quotes[symbol].price
        value = price * shares
        total_value += value
        print(f"{symbol}: {shares} shares @ ${price} = ${value:.2f}")

print(f"\nTotal Portfolio Value: ${total_value:.2f}")
```

### Esempio 2: Download Massivo Dati Storici

```python
from fba_finance import FinanceClient
from datetime import datetime, timedelta
import pandas as pd

client = FinanceClient()

# Lista di 100+ ticker
tickers = ["AAPL", "MSFT", "GOOGL", "TSLA", "AMZN", ...]  # aggiungi più ticker

all_data = {}

for ticker in tickers:
    print(f"Downloading {ticker}...")
    data = client.get_historical(ticker, period="1y", interval="1d")
    
    if data:
        all_data[ticker] = data.data
        print(f"✓ {ticker}: {len(data.data)} rows")
    else:
        print(f"✗ {ticker}: failed")

# Combina tutti in un singolo DataFrame
combined = pd.concat(all_data, names=['ticker', 'idx'])
combined.to_csv("all_stocks_1y.csv")
```

### Esempio 3: Real-Time Monitoring

```python
import time
from fba_finance import FinanceClient

client = FinanceClient(use_cache=False)  # No cache for real-time

watchlist = ["AAPL", "TSLA", "BTC-USD"]

while True:
    quotes = client.get_quotes(watchlist)
    
    print("\n" + "="*50)
    print(f"Update: {datetime.now().strftime('%H:%M:%S')}")
    print("="*50)
    
    for symbol, quote in quotes.items():
        change_symbol = "🔴" if quote.change < 0 else "🟢"
        print(f"{change_symbol} {symbol}: ${quote.price:.2f} ({quote.change_percent:+.2f}%)")
    
    time.sleep(10)  # Aggiorna ogni 10 secondi
```

### Esempio 4: Analisi Tecnica

```python
import pandas as pd
from fba_finance import FinanceClient

client = FinanceClient()

# Ottieni dati storici
data = client.get_historical("AAPL", period="3mo", interval="1d")
df = data.data

# Calcola medie mobili
df['SMA_20'] = df['close'].rolling(window=20).mean()
df['SMA_50'] = df['close'].rolling(window=50).mean()

# RSI (Relative Strength Index)
delta = df['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
rs = gain / loss
df['RSI'] = 100 - (100 / (1 + rs))

# Volatilità
df['volatility'] = df['close'].pct_change().rolling(window=20).std()

print(df[['date', 'close', 'SMA_20', 'SMA_50', 'RSI', 'volatility']].tail())
```

## 🛠️ Strategie Anti-Rate-Limiting

La libreria implementa diverse strategie per massimizzare il throughput:

### 1. **Fallback Multi-Provider**
Se un provider è rate-limited, prova automaticamente il prossimo nella catena.

### 2. **Rate Limiter Intelligente**
Tiene traccia delle richieste per secondo/minuto/giorno per ogni provider.

```python
# Configurabile in .env
MAX_REQUESTS_PER_SECOND=2
MAX_REQUESTS_PER_MINUTE=50
```

### 3. **Caching Aggressivo**
Cachea le risposte per ridurre richieste duplicate.

```python
# TTL configurabile per tipo di dato
CACHE_TTL_REALTIME=60      # 1 minuto per quote
CACHE_TTL_HISTORICAL=3600  # 1 ora per dati storici
```

### 4. **Batch Requests**
Usa batch API quando disponibili (yahooquery, twelvedata).

### 5. **Web Scraping di Fallback**
Se le API falliscono, usa web scraping con:
- Rotating User Agents
- Request throttling
- Error handling robusto

## 📈 Performance Tips

### Massimizza Throughput

```python
# 1. Usa batch requests quando possibile
quotes = client.get_quotes(["AAPL", "MSFT", "GOOGL"])  # 1 request
# invece di
quote1 = client.get_quote("AAPL")  # 3 requests
quote2 = client.get_quote("MSFT")
quote3 = client.get_quote("GOOGL")

# 2. Abilita cache per dati ripetuti
client = FinanceClient(use_cache=True)

# 3. Usa provider con limiti più alti per batch
client = FinanceClient(provider_priority=["twelvedata", "yahooquery"])

# 4. Per download massivi, distribuisci su più provider
providers = ["yfinance", "twelvedata", "alphavantage"]
for i, ticker in enumerate(large_ticker_list):
    provider = providers[i % len(providers)]
    data = client.get_historical(ticker, period="1y", provider=provider)
```

## 🐛 Debugging

```python
# Verifica provider disponibili
statuses = client.get_provider_status()
for status in statuses:
    symbol = "✓" if status.available else "✗"
    print(f"{symbol} {status.name}")
    if not status.available:
        print(f"  Error: {status.last_error}")

# Verifica quali provider hanno funzionato
client.get_available_providers()
# Output: ['yfinance', 'yahooquery', 'yahoo_scraper']
```

## 🤝 Contribuire

Contributi sono benvenuti! Per aggiungere nuovi provider:

1. Crea una nuova classe in `fba_finance/providers/`
2. Eredita da `BaseProvider`
3. Implementa `get_quote()`, `get_historical()`, `get_multiple_quotes()`
4. Aggiungi al `ProviderAggregator`

## 📝 Limiti Noti

- **Dati Intraday**: Alcuni provider free hanno limiti sui dati intraday (< 1d interval)
- **Dati Storici**: Twelvedata free tier limita a 8 anni di storia
- **Mercati Esotici**: Alcuni mercati minori potrebbero non essere disponibili su tutti i provider

## ⚖️ Disclaimer

Questa libreria è per scopi educativi e di ricerca. Rispetta sempre i termini di servizio dei provider:
- Yahoo Finance: Solo uso personale, non commerciale
- API Provider: Rispetta i rate limits dei tier gratuiti
- Web Scraping: Usa responsabilmente

**NON usare per trading automatico ad alta frequenza.**

## 📜 License

MIT License - Vedi `LICENSE` file

## 🙏 Crediti

Questa libreria si basa su:
- [yfinance](https://github.com/ranaroussi/yfinance)
- [yahooquery](https://github.com/dpguthrie/yahooquery)
- [Alpha Vantage](https://www.alphavantage.co/)
- [Twelve Data](https://twelvedata.com/)

---

**Made with ❤️ for the financial data community**

Per domande o supporto, apri una issue su GitHub!
