Termos de Referência
import json
import os
import time
# Install via: pip install google-genai
from google import genai
from google.genai import types
class GeminiProTradingBot:
"""
Automated Pro Trading Bot using Gemini API for AI-driven technical and trend analysis.
"""
def __init__(self, symbol: str = "BTC/USDT", min_confidence: float = 0.75):
self.symbol = symbol
self.min_confidence = min_confidence
# Initializes client using the standard GEMINI_API_KEY environment variable
self.client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
def _build_system_instruction(self) -> str:
return (
"You are an expert quantitative trading algorithm. Analyze the provided OHLCV candle data "
"and technical indicators. Output a strict JSON signal evaluating market state, "
"recommendation (BUY, SELL, HOLD), confidence score (0.0 to 1.0), and risk management parameters."
)
def analyze_market(self, market_payload: dict) -> dict:
"""
Sends formatted candle data to Gemini and returns structured trading signals.
"""
prompt = f"""
Analyze the following market state for asset: {self.symbol}
Recent Candles (OHLCV) & Indicators:
{json.dumps(market_payload, indent=2)}
Determine the immediate market direction, entry price target, stop loss, and take profit levels.
"""
# Define JSON Schema enforce exact AI response parameters
response_schema = {
"type": "OBJECT",
"properties": {
"action": {"type": "STRING", "enum": ["BUY", "SELL", "HOLD"]},
"confidence": {"type": "NUMBER"},
"reasoning": {"type": "STRING"},
"target_entry": {"type": "NUMBER"},
"stop_loss": {"type": "NUMBER"},
"take_profit": {"type": "NUMBER"},
"risk_reward_ratio": {"type": "NUMBER"}
},
"required": ["action", "confidence", "reasoning", "stop_loss", "take_profit"]
}
try:
response = self.client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=self._build_system_instruction(),
response_mime_type="application/json",
response_schema=response_schema,
temperature=0.2, # Low temperature for deterministic risk evaluation
),
)
return json.loads(response.text)
except Exception as e:
print(f"[Error] Gemini AI API query failed: {e}")
return {"action": "HOLD", "confidence": 0.0, "reasoning": "API execution error."}
def execute_signal(self, signal: dict):
"""
Validates confidence thresholds and triggers simulated or real execution.
"""
action = signal.get("action")
confidence = signal.get("confidence", 0.0)
print("\n--- [AI SIGNAL RECEIVED] ---")
print(f"Action: {action} | Confidence: {confidence * 100:.1f}%")
print(f"Reasoning: {signal.get('reasoning')}")
if confidence < self.min_confidence or action == "HOLD":
print("Status: Execution skipped (Insufficient confidence or HOLD signal).")
return
print(f"Status: EXECUTING {action} ORDER")
print(f"Entry Price Target : {signal.get('target_entry')}")
print(f"Stop Loss Target : {signal.get('stop_loss')}")
print(f"Take Profit Target : {signal.get('take_profit')}")
# Place broker API execution call here (e.g., CCXT, MetaTrader, or exchange REST APIs)
# --- Example Run ---
if __name__ == "__main__":
# Mock OHLCV candle feed with RSI & MACD indicators
sample_market_data = {
"timeframe": "5m",
"current_price": 64250.00,
"rsi_14": 28.5, # Oversold
"macd": {"macd_line": -120.4, "signal_line": -145.2, "histogram": 24.8},
"recent_candles": [
{"close": 64500, "volume": 12.4},
{"close": 64350, "volume": 18.1},
{"close": 64100, "volume": 25.6},
{"close": 64250, "volume": 32.0}
]
}
bot = GeminiProTradingBot(symbol="BTC/USDT", min_confidence=0.70)
signal = bot.analyze_market(sample_market_data)
bot.execute_signal(signal)
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