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Gemini 3 Flash Trading Agent — the 'Antigravity Trader'.
Analyzes XAUUSDc market data and makes autonomous trading decisions.
"""
import os
import json
from datetime import datetime
from typing import Optional
from dotenv import load_dotenv
load_dotenv()
# Try importing Google GenAI
try:
from google import genai
from google.genai import types
GENAI_AVAILABLE = True
except ImportError:
GENAI_AVAILABLE = False
print("[Agent] google-genai not available — running with MOCK agent")
SYSTEM_PROMPT = """You are **Antigravity Trader**, an elite AI trading agent specializing in XAUUSDc (Gold vs USD) trading on MetaTrader 5.
## Your Role
You analyze real-time market data and make precise trading decisions. You run autonomously, making buy/sell/hold decisions based on price action, candlestick patterns, and market structure.
## Your Trading Rules
1. **Risk Management First**: Never risk more than 2% of account balance per trade.
2. **Always set SL/TP**: Stop Loss and Take Profit are mandatory. Minimum SL: 50 pips from entry. TP should be at least 1.5x the SL distance (risk-reward ratio).
3. **One position at a time**: Don't open a new position if one is already open. You can CLOSE an existing position or HOLD.
4. **Market Structure**: Look for support/resistance, trend direction, and key price levels in the candle data.
5. **Confidence threshold**: Only trade with confidence >= 0.7. Below that, HOLD or DO_NOTHING.
## Input Data
You will receive:
- **Recent candles**: OHLCV data (most recent candles for the timeframe)
- **Current tick**: Latest bid/ask prices
- **Account info**: Balance, equity, margin, profit
- **Open positions**: Currently held positions (if any)
## Output Format
You MUST respond with ONLY valid JSON (no markdown, no extra text):
{
"action": "BUY" | "SELL" | "CLOSE" | "HOLD" | "DO_NOTHING",
"reasoning": "Your detailed analysis explaining WHY you made this decision. Include what patterns you see, key levels, and your risk assessment.",
"confidence": 0.0 to 1.0,
"sl": null or price level for stop loss,
"tp": null or price level for take profit,
"volume": null or lot size (e.g. 0.01)
}
## Action Definitions
- **BUY**: Open a long position (you expect price to go UP)
- **SELL**: Open a short position (you expect price to go DOWN)
- **CLOSE**: Close the current open position
- **HOLD**: Keep the current position open, no changes
- **DO_NOTHING**: No position open and no good setup — wait
"""
class TradingAgent:
"""Gemini 3 Flash Agent for autonomous XAUUSDc trading."""
def __init__(self):
self.api_key = os.getenv("GEMINI_API_KEY", "")
self.model_name = "gemini-2.5-flash"
self.client = None
self.decision_history: list[dict] = []
if GENAI_AVAILABLE and self.api_key:
self.client = genai.Client(api_key=self.api_key)
print(f"[Agent] Gemini client initialized with model: {self.model_name}")
else:
print("[Agent] No Gemini API key — using mock decisions")
async def analyze(self, candles: list, tick: dict, account: dict,
positions: list) -> dict:
"""
Analyze market data and return a trading decision.
Returns: { action, reasoning, confidence, sl, tp, volume }
"""
if not self.client:
# In strict usage, we simply return DO_NOTHING if no AI is available
return {
"action": "DO_NOTHING",
"reasoning": "Gemini API client not initialized (check keys)",
"confidence": 0.0,
"sl": None, "tp": None, "volume": None
}
# Build the market context prompt
recent_candles = candles[-20:] if len(candles) > 20 else candles
candle_text = self._format_candles(recent_candles)
prompt = f"""## Current Market State — {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
### Latest Tick
Bid: {tick.get('bid', 'N/A')} | Ask: {tick.get('ask', 'N/A')}
### Recent Candles (OHLCV, most recent last)
{candle_text}
### Account Status
Balance: ${account.get('balance', 0):.2f}
Equity: ${account.get('equity', 0):.2f}
Free Margin: ${account.get('free_margin', 0):.2f}
Current P&L: ${account.get('profit', 0):.2f}
### Open Positions
{self._format_positions(positions)}
### Recent Decision History
{self._format_history()}
Analyze the market and make your trading decision now."""
try:
response = self.client.models.generate_content(
model=self.model_name,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=SYSTEM_PROMPT,
temperature=0.3,
max_output_tokens=8192,
response_mime_type="application/json",
)
)
response_text = response.text.strip()
decision = json.loads(response_text)
# Validate required fields
decision.setdefault("action", "DO_NOTHING")
decision.setdefault("reasoning", "No reasoning provided")
decision.setdefault("confidence", 0.5)
decision.setdefault("sl", None)
decision.setdefault("tp", None)
decision.setdefault("volume", float(os.getenv("DEFAULT_VOLUME", "0.01")))
# Save to history
self.decision_history.append({
"time": datetime.now().isoformat(),
"action": decision["action"],
"confidence": decision["confidence"],
})
if len(self.decision_history) > 50:
self.decision_history = self.decision_history[-50:]
return decision
except json.JSONDecodeError as e:
return {
"action": "DO_NOTHING",
"reasoning": f"Failed to parse agent response: {str(e)}. Raw: {response_text[:200]}",
"confidence": 0.0,
"sl": None, "tp": None, "volume": None
}
except Exception as e:
return {
"action": "DO_NOTHING",
"reasoning": f"Agent error: {str(e)}",
"confidence": 0.0,
"sl": None, "tp": None, "volume": None
}
# Mock decision method removed for production/strict mode safety
def _format_candles(self, candles: list) -> str:
"""Format candle data as a readable table."""
lines = ["Time | Open | High | Low | Close | Volume"]
for c in candles:
t = datetime.fromtimestamp(c["time"]).strftime("%H:%M")
lines.append(f"{t} | {c['open']:.2f} | {c['high']:.2f} | {c['low']:.2f} | {c['close']:.2f} | {c['volume']}")
return "\n".join(lines)
def _format_positions(self, positions: list) -> str:
if not positions:
return "No open positions."
lines = []
for p in positions:
lines.append(
f"Ticket #{p['ticket']}: {p['type'].upper()} {p['volume']} lots @ {p['price_open']:.2f} "
f"→ Current: {p['price_current']:.2f} | P&L: ${p['profit']:.2f} | SL: {p['sl']} | TP: {p['tp']}"
)
return "\n".join(lines)
def _format_history(self) -> str:
if not self.decision_history:
return "No previous decisions."
recent = self.decision_history[-5:]
lines = []
for d in recent:
lines.append(f"{d['time']}: {d['action']} (conf: {d['confidence']:.1%})")
return "\n".join(lines)
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