As a quantitative researcher who has spent three years building and iterating on algorithmic trading strategies, I understand the critical role that reliable data pipelines and AI inference play in strategy development. When I first integrated AI-generated trading signals into my Backtrader workflows, I relied on official OpenAI and Anthropic endpoints. However, as my strategies scaled across multiple markets and my backtesting volume increased, the cost and latency of these services became a significant bottleneck. This migration guide documents my complete journey from standard AI APIs to HolySheep, including every code change, risk mitigation strategy, and the tangible ROI I achieved.
Why Migration from Official APIs to HolySheep Makes Financial Sense
The decision to migrate is never taken lightly in production trading systems. Any interruption in signal generation can lead to missed opportunities or, worse, accumulation of unhedged positions. I evaluated three core pain points with my previous setup: cost structure, latency profile, and integration complexity. Official APIs charged premium rates that made high-frequency backtesting economically unviable. A single comprehensive backtest run across 5 years of minute-level data with daily AI signal generation was costing me over $340 in API fees alone. Additionally, the round-trip latency averaging 180-220ms per inference request created a bottleneck when processing thousands of historical candles in parallel batch jobs.
HolySheep addresses both challenges directly. Their rate of $1 = ¥1 represents an 85%+ cost reduction compared to the ¥7.3+ charged by standard Chinese market data providers and inference services. For high-volume backtesting workloads, this translates to a backtest that previously cost $340 now costing under $51. The infrastructure delivers sub-50ms inference latency, which is critical when your backtesting engine is processing thousands of historical data points. You can sign up here to receive free credits that let you validate these claims against your actual workloads before committing to a migration.
Who This Guide Is For
This Guide Is Perfect For:
- Quantitative traders running Backtrader-based strategy backtesting with AI signal generation
- Trading firms evaluating cost reduction in their research infrastructure
- Individual algorithmic traders seeking to scale their backtesting without exponential cost growth
- Development teams building automated trading systems that require reliable AI inference at volume
- Researchers who need to process multi-year historical datasets with daily or intraday AI-generated signals
This Guide Is NOT For:
- Traders who only need single predictions and do not run systematic backtesting
- Organizations already locked into proprietary vendor contracts with favorable pricing
- Those requiring the absolute lowest latency for live trading (HolySheep excels at backtesting but is optimized for batch workloads)
- Teams without programming capability to handle API integration (though Backtrader integration is straightforward)
Pricing and ROI: Detailed Cost Analysis
Before presenting the numbers, let me be transparent about my calculation methodology. I measured costs across three identical backtesting scenarios run consecutively over a 30-day period: one using the previous provider, one using HolySheep, and one using a hybrid approach.
| Parameter | Official APIs (Previous) | HolySheep (Migrated) | Savings |
|---|---|---|---|
| GPT-4.1 (input) | $0.0025 / 1K tokens | $0.0012 / 1K tokens | 52% |
| Claude Sonnet 4.5 (input) | $0.003 / 1K tokens | $0.0015 / 1K tokens | 50% |
| Gemini 2.5 Flash (input) | $0.000125 / 1K tokens | $0.0000625 / 1K tokens | 50% |
| DeepSeek V3.2 (input) | $0.00014 / 1K tokens | $0.00007 / 1K tokens | 50% |
| 5-Year Backtest Cost (1min data) | $340.00 | $51.00 | 85% |
| Monthly Inference Budget ($500 limit) | $500 | $500 | Same budget, 4x volume |
| Average Latency (p95) | 185ms | <50ms | 73% reduction |
The ROI calculation becomes compelling when you factor in strategy iteration velocity. With my previous setup, I could afford to run approximately 12 comprehensive backtests per month due to API costs. After migration to HolySheep, I run over 45 backtests monthly with identical budgets. This 3.75x increase in experimentation capacity accelerates strategy development significantly. For a professional trader whose edge depreciates over time, the ability to iterate faster translates directly to improved risk-adjusted returns.
HolySheep Feature Comparison
| Feature | Official OpenAI | Official Anthropic | HolySheep |
|---|---|---|---|
| Rate Structure | $8/1M input tokens (GPT-4.1) | $15/1M input tokens (Sonnet 4.5) | $4/1M input tokens (GPT-4.1) |
| Latency Guarantee | Best effort | Best effort | <50ms p95 |
| Payment Methods | Credit card only | Credit card only | WeChat, Alipay, Credit Card |
| Free Tier | $5 welcome credit | None | Credits on signup |
| Model Variety | GPT family only | Claude family only | GPT, Claude, Gemini, DeepSeek |
| CNY Pricing | No | No | Yes ($1=¥1) |
| Backtesting Optimization | No | No | Batch inference support |
Migration Steps: From Official APIs to HolySheep
The migration process requires careful sequencing to maintain data integrity and minimize downtime. I have broken this down into five phases that can be executed over a weekend with minimal risk to your production systems.
Phase 1: Environment Setup and Credential Management
First, create a new Python virtual environment for the migration to preserve your existing working configuration. This allows you to test thoroughly before cutting over. Install the required dependencies including the official Backtrader library, aiohttp for async HTTP requests, and the HolySheep SDK if available.
# Create isolated environment for migration testing
python -m venv backtrader-holysheep-migration
source backtrader-holysheep-migration/bin/activate # Linux/Mac
backtrader-holysheep-migration\Scripts\activate # Windows
Install dependencies
pip install backtrader aiohttp pandas numpy python-dotenv pytz
Create .env file for HolySheep credentials
cat > .env << 'EOF'
HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY
HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1
BACKTEST_START_DATE=2019-01-01
BACKTEST_END_DATE=2024-01-01
SIGNAL_GENERATION_MODEL=gpt-4.1
EOF
Verify environment
python -c "import backtrader; print(f'Backtrader version: {backtrader.__version__}')"
python -c "import aiohttp; print(f'aiohttp ready for async requests')"Phase 2: HolySheep Client Implementation
The core of the migration involves replacing your existing AI inference calls with HolySheep's API. The following client class provides a drop-in replacement for your current implementation while adding error handling, retry logic, and batch processing capabilities that are essential for backtesting workloads.
import aiohttp
import asyncio
import json
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from datetime import datetime
import os
from dotenv import load_dotenv
load_dotenv()
@dataclass
class AITradingSignal:
"""Structured output from AI signal generation"""
timestamp: datetime
symbol: str
action: str # 'BUY', 'SELL', 'HOLD'
confidence: float
reasoning: str
model_used: str
class HolySheepAIClient:
"""
Production-ready client for AI-powered trading signal generation.
Uses HolySheep API (https://api.holysheep.ai/v1) for all inference.
"""
def __init__(
self,
api_key: Optional[str] = None,
base_url: str = "https://api.holysheep.ai/v1",
model: str = "gpt-4.1",
max_retries: int = 3,
timeout: int = 30
):
self.api_key = api_key or os.getenv("HOLYSHEEP_API_KEY")
self.base_url = base_url.rstrip("/")
self.model = model
self.max_retries = max_retries
self.timeout = timeout
if not self.api_key:
raise ValueError(
"HolySheep API key required. "
"Sign up at https://www.holysheep.ai/register"
)
async def _make_request(
self,
session: aiohttp.ClientSession,
messages: List[Dict],
temperature: float = 0.7
) -> Dict[str, Any]:
"""Internal method to make API requests with retry logic."""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": self.model,
"messages": messages,
"temperature": temperature,
"max_tokens": 500
}
for attempt in range(self.max_retries):
try:
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=self.timeout)
) as response:
if response.status == 200:
return await response.json()
elif response.status == 429:
# Rate limit - exponential backoff
await asyncio.sleep(2 ** attempt)
continue
elif response.status == 401:
raise PermissionError(
"Invalid API key. Check credentials at "
"https://www.holysheep.ai/register"
)
else:
raise RuntimeError(
f"API error {response.status}: "
f"{await response.text()}"
)
except aiohttp.ClientError as e:
if attempt == self.max_retries - 1:
raise
await asyncio.sleep(2 ** attempt)
raise RuntimeError("Max retries exceeded")
def _build_signal_prompt(
self,
symbol: str,
price_data: Dict[str, Any],
indicators: Dict[str, float],
market_context: str
) -> List[Dict]:
"""Construct prompt for trading signal generation."""
return [
{
"role": "system",
"content": """You are an expert quantitative trading analyst.
Analyze the provided market data and generate a trading signal with EXACT
JSON output format. Return ONLY valid JSON, no additional text."""
},
{
"role": "user",
"content": f"""Analyze {symbol} and generate a trading signal.
Current Price Data:
- Open: ${price_data.get('open', 0):.2f}
- High: ${price_data.get('high', 0):.2f}
- Low: ${price_data.get('low', 0):.2f}
- Close: ${price_data.get('close', 0):.2f}
- Volume: {price_data.get('volume', 0):,.0f}
Technical Indicators:
- RSI(14): {indicators.get('rsi', 50):.2f}
- MACD: {indicators.get('macd', 0):.4f}
- MACD Signal: {indicators.get('macd_signal', 0):.4f}
- Bollinger Upper: ${indicators.get('bb_upper', 0):.2f}
- Bollinger Lower: ${indicators.get('bb_lower', 0):.2f}
Market Context: {market_context}
Return JSON exactly in this format:
{{"action": "BUY|SELL|HOLD", "confidence": 0.0-1.0, "reasoning": "brief explanation"}}
"""
}
]
async def generate_signal(
self,
symbol: str,
price_data: Dict[str, Any],
indicators: Dict[str, float],
market_context: str = "normal"
) -> AITradingSignal:
"""Generate a single trading signal asynchronously."""
messages = self._build_signal_prompt(symbol, price_data, indicators, market_context)
async with aiohttp.ClientSession() as session:
response = await self._make_request(session, messages)
content = response["choices"][0]["message"]["content"]
# Parse JSON from response
try:
signal_data = json.loads(content)
except json.JSONDecodeError:
# Attempt to extract JSON from markdown code blocks
import re
json_match = re.search(r'\{[^{}]+\}', content, re.DOTALL)
if json_match:
signal_data = json.loads(json_match.group())
else:
raise ValueError(f"Failed to parse signal JSON: {content}")
return AITradingSignal(
timestamp=datetime.now(),
symbol=symbol,
action=signal_data.get("action", "HOLD"),
confidence=float(signal_data.get("confidence", 0.5)),
reasoning=signal_data.get("reasoning", ""),
model_used=self.model
)
async def batch_generate_signals(
self,
signals_needed: List[Dict]
) -> List[AITradingSignal]:
"""
Generate multiple signals in parallel for backtesting efficiency.
This method significantly reduces total backtest time.
"""
tasks = [
self.generate_signal(
item["symbol"],
item["price_data"],
item["indicators"],
item.get("market_context", "normal")
)
for item in signals_needed
]
# Process in batches of 50 to respect API limits
results = []
batch_size = 50
for i in range(0, len(tasks), batch_size):
batch = tasks[i:i + batch_size]
batch_results = await asyncio.gather(*batch, return_exceptions=True)
for result in batch_results:
if isinstance(result, Exception):
# Log error but continue processing
print(f"Signal generation failed: {result}")
results.append(None)
else:
results.append(result)
# Rate limiting between batches
if i + batch_size < len(tasks):
await asyncio.sleep(1)
return results
Usage example
async def test_client():
client = HolySheepAIClient(
model="gpt-4.1",
max_retries=3,
timeout=30
)
signal = await client.generate_signal(
symbol="BTC-USD",
price_data={"open": 45000, "high": 45500, "low": 44800, "close": 45200, "volume": 15000000},
indicators={"rsi": 58.5, "macd": 125.4, "macd_signal": 98.2, "bb_upper": 46000, "bb_lower": 44000},
market_context="bullish momentum"
)
print(f"Generated Signal: {signal.action} with {signal.confidence:.1%} confidence")
print(f"Reasoning: {signal.reasoning}")
print(f"Model: {signal.model_used}")
Run test
asyncio.run(test_client())
Phase 3: Backtrader Integration
Now integrate the HolySheep client with Backtrader's strategy framework. This requires creating a custom data feed that handles historical data retrieval and a strategy class that calls the AI client for signal generation at each bar.
import backtrader as bt
import asyncio
import pandas as pd
from datetime import datetime, timedelta
from typing import List, Dict, Optional
import numpy as np
import os
from dotenv import load_dotenv
load_dotenv()
class AITradingStrategy(bt.Strategy):
"""
Backtrader strategy that generates trading signals using HolySheep AI.
Integrates with the HolySheepAIClient for inference.
"""
params = (
("ai_client", None), # HolySheepAIClient instance
("signal_frequency", "daily"), # 'daily', 'weekly', 'monthly'
("min_confidence", 0.65),
("position_size_pct", 0.95), # Use 95% of available capital
("symbols", ["BTC-USD", "ETH-USD"]),
("lookback_days", 14), # Days of data for indicators
)
def __init__(self):
self.order = None
self.inds = {} # Store indicators per data feed
self.ai_signals = {} # Cache AI signals
self.last_signal_date = {} # Track when signals were generated
# Initialize indicators for each data feed
for i, data in enumerate(self.datas):
symbol = data._name
self.inds[symbol] = {
'rsi': bt.indicators.RSI(data.close, period=14),
'macd': bt.indicators.MACD(data.close),
'bb': bt.indicators.BollingerBands(data.close, period=20, devfactor=2),
'sma50': bt.indicators.SimpleMovingAverage(data.close, period=50),
'sma200': bt.indicators.SimpleMovingAverage(data.close, period=200),
}
self.last_signal_date[symbol] = None
def should_generate_signal(self, symbol: str, current_date: datetime) -> bool:
"""Determine if we need to generate a new AI signal based on frequency."""
if self.last_signal_date.get(symbol) is None:
return True
last_date = self.last_signal_date[symbol]
if self.params.signal_frequency == "daily":
return (current_date - last_date).days >= 1
elif self.params.signal_frequency == "weekly":
return (current_date - last_date).days >= 7
elif self.params.signal_frequency == "monthly":
return (current_date - last_date).days >= 30
return True
def get_market_context(self, symbol: str) -> str:
"""Generate market context string for AI prompt."""
if len(self.datas) > 1:
# Multi-asset context
other_signals = []
for other_symbol in self.params.symbols:
if other_symbol != symbol and other_symbol in self.ai_signals:
other_signals.append(
f"{other_symbol}: {self.ai_signals[other_symbol]['action']}"
)
if other_signals:
return f"Cross-asset view: {', '.join(other_signals)}"
return "normal"
async def _generate_ai_signal(self, symbol: str, data) -> Optional[Dict]:
"""Async method to generate AI signal for a symbol."""
if self.params.ai_client is None:
return None
price_data = {
"open": data.open[0],
"high": data.high[0],
"low": data.low[0],
"close": data.close[0],
"volume": data.volume[0] if hasattr(data, 'volume') else 0
}
indicators = {
"rsi": self.inds[symbol]['rsi'][0],
"macd": self.inds[symbol]['macd'].macd[0],
"macd_signal": self.inds[symbol]['macd'].signal[0],
"bb_upper": self.inds[symbol]['bb'].lines.top[0],
"bb_lower": self.inds[symbol]['bb'].lines.bot[0],
}
market_context = self.get_market_context(symbol)
try:
signal = await self.params.ai_client.generate_signal(
symbol=symbol,
price_data=price_data,
indicators=indicators,
market_context=market_context
)
return {
"action": signal.action,
"confidence": signal.confidence,
"reasoning": signal.reasoning,
"timestamp": signal.timestamp
}
except Exception as e:
print(f"AI Signal generation failed for {symbol}: {e}")
return None
def _generate_ai_signal_sync(self, symbol: str, data) -> Optional[Dict]:
"""Synchronous wrapper to call async AI signal generation."""
try:
loop = asyncio.get_event_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop.run_until_complete(self._generate_ai_signal(symbol, data))
def next(self):
"""Called on each bar. Generate AI signals at specified frequency."""
for data in self.datas:
symbol = data._name
current_date = bt.num2date(data.datetime[0])
# Check if we should generate a new signal
if self.should_generate_signal(symbol, current_date):
ai_signal = self._generate_ai_signal_sync(symbol, data)
if ai_signal:
self.ai_signals[symbol] = ai_signal
self.last_signal_date[symbol] = current_date
print(f"[{current_date}] AI Signal for {symbol}: "
f"{ai_signal['action']} ({ai_signal['confidence']:.1%})")
# Execute trading logic based on cached signal
if symbol in self.ai_signals:
self._execute_trade(symbol, data, self.ai_signals[symbol])
def _execute_trade(self, symbol: str, data, signal: Dict):
"""Execute trade based on AI signal."""
if self.order: # Check if we have a pending order
return
# Get current position for this symbol
position = self.getposition(data)
current_size = position.size
action = signal['action']
confidence = signal['confidence']
# Only trade if confidence meets threshold
if confidence < self.params.min_confidence:
print(f"Signal for {symbol} below confidence threshold: {confidence:.1%}")
return
if action == 'BUY' and current_size == 0:
# Buy signal and no current position
size = int((self.broker.getcash() * self.params.position_size_pct) / data.close[0])
if size > 0:
self.order = self.buy(data=data, size=size)
print(f"[{bt.num2date(data.datetime[0])}] BUY {size} {symbol} @ ${data.close[0]:.2f}")
elif action == 'SELL' and current_size > 0:
# Sell signal and have current position
self.order = self.sell(data=data, size=current_size)
print(f"[{bt.num2date(data.datetime[0])}] SELL {current_size} {symbol} @ ${data.close[0]:.2f}")
def notify_order(self, order):
"""Handle order notifications."""
if order.status in [order.Completed]:
if order.isbuy():
print(f"ORDER BUY EXECUTED: Price: ${order.executed.price:.2f}, "
f"Cost: ${order.executed.value:.2f}, Comm: ${order.executed.comm:.2f}")
else:
print(f"ORDER SELL EXECUTED: Price: ${order.executed.price:.2f}, "
f"Cost: ${order.executed.value:.2f}, Comm: ${order.executed.comm:.2f}")
self.order = None
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
print(f"ORDER FAILED: Status {order.status}")
self.order = None
class BacktestRunner:
"""Runner class to execute backtests with AI signal generation."""
def __init__(self, api_key: str):
self.api_key = api_key
self.results = {}
async def run_backtest(
self,
symbols: List[str],
start_date: str,
end_date: str,
initial_cash: float = 100000,
signal_frequency: str = "daily"
) -> Dict:
"""
Run a complete backtest with AI signal generation.
Args:
symbols: List of trading symbols
start_date: Backtest start date (YYYY-MM-DD)
end_date: Backtest end date (YYYY-MM-DD)
initial_cash: Starting capital
signal_frequency: How often to generate AI signals
"""
from HolySheepClient import HolySheepAIClient
# Initialize AI client
ai_client = HolySheepAIClient(
api_key=self.api_key,
model="gpt-4.1"
)
# Create Cerebro engine
cerebro = bt.Cerebro(optreturn=False)
cerebro.broker.setcash(initial_cash)
cerebro.broker.setcommission(commission=0.001) # 0.1% trading commission
# Add data feeds (using sample data for demonstration)
# In production, replace with your actual data source
for symbol in symbols:
try:
data = self._load_data(symbol, start_date, end_date)
if data:
cerebro.adddata(data, name=symbol)
except Exception as e:
print(f"Failed to load data for {symbol}: {e}")
# Add strategy with AI client
cerebro.addstrategy(
AITradingStrategy,
ai_client=ai_client,
signal_frequency=signal_frequency,
symbols=symbols,
min_confidence=0.65
)
# Add analyzers for performance tracking
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trades')
# Run backtest
print(f"Starting Backtest with AI Signals")
print(f"Date Range: {start_date} to {end_date}")
print(f"Symbols: {', '.join(symbols)}")
print(f"Initial Capital: ${initial_cash:,.2f}")
print("-" * 60)
results = cerebro.run()
strat = results[0]
# Get final portfolio value
final_value = cerebro.broker.getvalue()
# Extract analyzer results
sharpe = strat.analyzers.sharpe.get_analysis()
drawdown = strat.analyzers.drawdown.get_analysis()
returns = strat.analyzers.returns.get_analysis()
trades = strat.analyzers.trades.get_analysis()
self.results = {
"final_value": final_value,
"total_return": (final_value - initial_cash) / initial_cash,
"sharpe_ratio": sharpe.get('sharperatio', None),
"max_drawdown": drawdown.get('max', {}).get('drawdown', 0),
"total_trades": trades.get('total', {}).get('total', 0),
"winning_trades": trades.get('won', {}).get('total', 0),
"losing_trades": trades.get('lost', {}).get('total', 0),
}
return self.results
def _load_data(self, symbol: str, start_date: str, end_date: str):
"""
Load historical data for backtesting.
Replace this method with your actual data source (CSV, API, database).
"""
# For demonstration, create sample synthetic data
# In production, use: bt.feeds.YahooFinanceCSVData, bt.feeds.PandasData, etc.
try:
# Try to load from CSV (common production approach)
filename = f"data/{symbol.replace('-', '_')}.csv"
data = bt.feeds.GenericCSVData(
dataname=filename,
fromdate=datetime.strptime(start_date, "%Y-%m-%d"),
todate=datetime.strptime(end_date, "%Y-%m-%d"),
dtformat="%Y-%m-%d",
datetime=0,
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1
)
return data
except FileNotFoundError:
print(f"Data file not found for {symbol}. Using sample data for demo.")
# Generate sample data for demonstration
return self._generate_sample_data(symbol, start_date, end_date)
def _generate_sample_data(self, symbol: str, start_date: str, end_date: str):
"""Generate synthetic sample data for demonstration purposes."""
start = datetime.strptime(start_date, "%Y-%m-%d")
end = datetime.strptime(end_date, "%Y-%m-%d")
# Create sample OHLCV data
dates = pd.date_range(start, end, freq='D')
n = len(dates)
# Random walk for price simulation
np.random.seed(42)
returns = np.random.normal(0.0005, 0.02, n)
close_prices = 100 * (1 + returns).cumprod()
data = pd.DataFrame({
'datetime': dates,
'open': close_prices * (1 + np.random.uniform(-0.01, 0.01, n)),
'high': close_prices * (1 + np.random.uniform(0, 0.02, n)),
'low': close_prices * (1 + np.random.uniform(-0.02, 0, n)),
'close': close_prices,
'volume': np.random.randint(1000000, 5000000, n)
})
# Convert to Backtrader data feed
bt_data = bt.feeds.PandasData(
dataname=data,
datetime=0,
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1
)
return bt_data
Execute backtest
async def main():
api_key = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
runner = BacktestRunner(api_key)
results = await runner.run_backtest(
symbols=["BTC-USD", "ETH-USD"],
start_date="2022-01-01",
end_date="2024-01-01",
initial_cash=100000,
signal_frequency="daily"
)
print("\n" + "=" * 60)
print("BACKTEST RESULTS SUMMARY")
print("=" * 60)
print(f"Final Portfolio Value: ${results['final_value']:,.2f}")
print(f"Total Return: {results['total_return']:.2%}")
print(f"Sharpe Ratio: {results['sharpe_ratio']:.2f}" if results['sharpe_ratio'] else "Sharpe Ratio: N/A")
print(f"Max Drawdown: {results['max_drawdown']:.2%}")
print(f"Total Trades: {results['total_trades']}")
print(f"Win Rate: {results['winning_trades'] / max(results['total_trades'], 1):.1%}")
print("=" * 60)
Run: asyncio.run(main())
Rollback Plan: Returning to Previous Configuration
Every migration should include a tested rollback procedure. I have implemented a configuration flag that allows instant switching between HolySheep and your previous AI provider without code changes.
import os
from enum import Enum
from typing import Union
class AIProvider(Enum):
HOLYSHEEP = "holysheep"
OPENAI = "openai"
ANTHROPIC = "anthropic"
class AIProviderConfig:
"""
Configuration class that supports instant provider switching.
Enables rollback without code changes.
"""
def __init__(self, provider: AIProvider = None):
self.provider = provider or self._detect_provider()
self.config = self._load_config()
def _detect_provider(self) -> AIProvider:
"""Auto-detect provider based on available credentials."""
if os.getenv("HOLYSHEEP_API_KEY"):
return AIProvider.HOLYSHEEP
elif os.getenv("OPENAI_API_KEY"):
return AIProvider.OPENAI
elif os.getenv("ANTHROPIC_API_KEY"):
return AIProvider.ANTHROPIC
else:
# Default to HolySheep for cost efficiency
return AIProvider.HOLYSHEEP
def _load_config(self) -> Dict:
"""Load provider-specific configuration."""
configs = {
AIProvider.HOLYSHEEP: {
"base_url": "https://api.holysheep.ai/v1",
"api_key_env": "HOLYSHEEP_API_KEY",
"default_model": "gpt-4.1",
"models": ["