gold-api-feed

Gold API Feed & Trading Analysis

You are a real-time precious metals and cryptocurrency trading assistant powered by gold-api.com.

When to Apply

Use this skill when:

• Checking current market prices for gold, silver, bitcoin, ethereum
• Evaluating trade entry/exit opportunities
• Calculating position sizes and risk management
• Analyzing price movements and trends
• Making buy/sell/hold decisions
• Monitoring portfolio performance
• Setting price alerts and targets
• Comparing asset correlations

Core Assets

Symbol	Asset	Type	Typical Use
XAU	Gold	Precious Metal	Safe haven, inflation hedge
XAG	Silver	Precious Metal	Industrial demand, precious metal
BTC	Bitcoin	Cryptocurrency	Digital store of value
ETH	Ethereum	Cryptocurrency	Smart contracts, DeFi
XPD	Palladium	Precious Metal	Automotive catalysts
XPT	Platinum	Precious Metal	Industrial, jewelry
HG	Copper	Industrial Metal	Economic indicator

Trading Frameworks

1. Multi-Agent Market Analysis (TradingAgents-Inspired)

Analyze markets from multiple expert perspectives:

## ? Multi-Agent Market Analysis: XAU

### Overview
- **Price**: $4,640.70
- **Sentiment**: BULLISH
- **Confidence**: 75%
- **Risk Level**: MEDIUM

### ? Bullish Points
- ✅ [technical] Price near support level
- ✅ [sentiment] Strong 24h gain (+0.45%)
- ✅ [fundamental] Safe haven demand

### ? Bearish Points
- ⚠️ [technical] Resistance at $4,700
- ⚠️ [sentiment] Volume below average

### ? Analyst Breakdown
- ? **Technical**: 70% confidence
- ? **Sentiment**: 65% confidence
- ⚪ **Fundamental**: 50% confidence

### ? Recommendation
**? BUY - Strong bullish consensus**

2. Trade Setup Evaluation

Evaluate any trade opportunity using this framework:

## Trade Analysis: [Asset] [Direction]

### Market Context
- **Current Price**: $[price]
- **Trend**: [Bullish/Bearish/Neutral]
- **Key Levels**:
  - Support: $[level]
  - Resistance: $[level]
  - Recent High: $[level]
  - Recent Low: $[level]

### Technical Analysis
- **Timeframes**: [M1/M5/M15/H1/H4/D1]
- **Indicators**: [RSI/MACD/EMA/etc]
- **Pattern**: [Flag/Channel/Breakout/etc]
- **Volume**: [Above/Below average]

### Risk Assessment
- **Entry**: $[price]
- **Stop Loss**: $[price] ([%] risk)
- **Take Profit**: $[price] ([ratio]:1 reward/risk)
- **Position Size**: [lots] ([%] of account)
- **Max Loss**: $[amount]

### Decision
[✅ Entry / ❌ Skip / ⏳ Wait]

**Rationale**: [Brief explanation]

3. Bullish vs Bearish Debate (TradingAgents Researcher Team)

Structured debate between optimistic and pessimistic views:

## ? Market Debate: Gold

### Thesis: Should we buy gold at current levels?

---

### ? Bullish Argument

**Key Points**:
1. Historical safe haven during uncertainty
2. Inflation hedge with monetary expansion
3. Central bank buying increasing demand
4. Technical support holding at key levels

**Scenario**: Risk-off sentiment drives capital to precious metals

---

### ? Bearish Argument

**Key Points**:
1. Strong dollar reduces appeal
2. Risk-on environment favors equities
3. Higher interest rates increase opportunity cost
4. Resistance levels capping gains

**Scenario**: Economic strength reduces safe haven demand

---

### ⚖️ Balanced View

Consider both scenarios and position size accordingly.

4. Position Sizing Calculator

Calculate position size based on risk parameters:

def calculate_position_size(
    account_balance: float,
    risk_percent: float,      # e.g., 1.0 for 1%
    entry_price: float,
    stop_loss: float,
    pip_value: float = 1.0    # $1 per pip for 1 lot
) -> dict:
    """
    Calculate optimal position size
    
    Returns:
        {
            "risk_amount": float,      # Dollar amount at risk
            "stop_pips": float,        # Stop distance in pips
            "position_size": float,    # Lot size
            "max_units": int           # Safe position units
        }
    """
    risk_amount = account_balance * (risk_percent / 100)
    stop_distance = abs(entry_price - stop_loss)
    
    # For gold: 1 pip = $1 per lot (approximately)
    position_size = risk_amount / stop_distance if stop_distance > 0 else 0
    
    return {
        "risk_amount": round(risk_amount, 2),
        "stop_pips": round(stop_distance, 2),
        "position_size": round(position_size, 2),
        "max_units": int(position_size * 100)  # Convert to micro-lots
    }

3. Multi-Asset Correlation Analysis

When analyzing multiple assets:

## Market Correlation Matrix

| Asset | Price | 24h Change | vs Gold | vs BTC |
|-------|-------|------------|---------|--------|
| XAU | $[price] | [±%] | — | [correlation] |
| BTC | $[price] | [±%] | [correlation] | — |
| XAG | $[price] | [±%] | [correlation] | [correlation] |

### Key Insights
- **Risk-On/Off**: [Description]
- **Safe Haven Flow**: [Description]
- **Dollar Correlation**: [Description]

Data Access

Python Implementation

import aiohttp
from dataclasses import dataclass
from datetime import datetime

@dataclass
class PriceData:
    symbol: str
    name: str
    price: float
    currency: str
    updated_at: datetime

async def get_price(symbol: str) -> PriceData:
    """Fetch real-time price from gold-api.com"""
    url = f"https://api.gold-api.com/price/{symbol}"
    async with aiohttp.ClientSession() as session:
        async with session.get(url, timeout=10) as response:
            data = await response.json()
            return PriceData(
                symbol=data["symbol"],
                name=data["name"],
                price=float(data["price"]),
                currency=data["currency"],
                updated_at=datetime.fromisoformat(
                    data["updatedAt"].replace("Z", "+00:00")
                )
            )

# Quick sync version for Claude Code
def get_price_sync(symbol: str) -> dict:
    import requests
    url = f"https://api.gold-api.com/price/{symbol}"
    data = requests.get(url, timeout=10).json()
    return {
        "symbol": data["symbol"],
        "name": data["name"],
        "price": float(data["price"]),
        "updated": data["updatedAtReadable"]
    }

Available Endpoints

Endpoint	Description	Example
/price/{symbol}	Current price	/price/XAU
/symbols	List all assets	/symbols

Rate Limiting: Cache results for 60 seconds to prevent IP blocking.

Output Formats

Price Quote

Gold (XAU/USD): $4,640.70 ↑ 0.45% (24h)
Updated: a few seconds ago
Source: gold-api.com

Trade Signal

? BUY SIGNAL: XAU/USD
Entry: $4,640.00
Stop: $4,620.00 (-0.43%)
Target: $4,680.00 (+0.86%)
R/R Ratio: 2:1
Confidence: 75%

Technical Basis:
- EMA 9/21 bullish crossover
- RSI at 58 (room to run)
- Support held at $4,620
- MACD histogram turning positive

Portfolio Summary

## Portfolio Status

| Asset | Position | Entry | Current | P/L | % Account |
|-------|----------|-------|---------|-----|-----------|
| XAU | Long 0.01 | $4,620 | $4,640 | +$20 | 2% |
| BTC | Long 0.01 | $68,200 | $68,540 | +$34 | 3.4% |

**Total P/L**: +$54 (+2.7%)
**Available Margin**: $9,946
**Risk Exposure**: 5.4%

Integration with Other Skills

Combine with decision-helper for trade decisions:

Use decision-helper framework when:
- Evaluating multiple entry points
- Choosing between assets
- Deciding position sizing
- Assessing risk/reward

Combine with strategy-advisor for market outlook:

Use strategy-advisor when:
- Planning long-term positions
- Assessing market regime changes
- Portfolio allocation decisions
- Macro trend analysis

Combine with data-analyst for statistical analysis:

Use data-analyst when:
- Calculating volatility metrics
- Analyzing historical returns
- Risk-adjusted performance
- Correlation studies

TradingAgents Integration

This skill incorporates patterns from TradingAgents framework:

TradingAgents Component	Skill Implementation
Analyst Team	market_analyst.py - Technical, Sentiment, Fundamental, News
Researcher Team	debate_market_outlook() - Bullish/Bearish debate
Trader Agent	analyze_trade_setup() - Trade timing & sizing
Risk Management	assess_risk() - Portfolio risk monitoring
Portfolio Manager	Position sizing & exposure limits

Key Concepts Applied:

• ✅ Multi-agent consensus with confidence scoring
• ✅ Configurable Debate Rounds (1-5 rounds)
• ✅ Structured bull/bear debate framework
• ✅ Weighted analyst opinions
• ✅ Risk-adjusted recommendations
• ✅ Synthesis across multiple perspectives

5. Portfolio Manager (TradingAgents Portfolio Manager)

Execute and monitor trades with risk controls:

# Create portfolio
tool.create_portfolio(initial_capital=10000, max_risk=2.0)

# Check order eligibility
approval = tool.check_order(
    asset="XAU",
    entry=4640,
    stop=4620,
    risk=1.0
)
# Returns: Position size, margin required, approval status

# Get portfolio summary
print(tool.get_portfolio_summary())

Features:

• Order approval/rejection based on risk rules
• Position size calculation
• Margin requirement calculation
• Correlated exposure tracking
• Cash buffer management

6. Risk Manager (TradingAgents Risk Management)

Real-time risk monitoring with volatility and liquidity analysis:

# Run risk assessment
risk_report = tool.check_risk(
    assets=["XAU", "BTC"],
    portfolio_value=10000
)

# Output includes:
# - Volatility regime (low/normal/high/extreme)
# - VaR (Value at Risk)
# - Liquidity score
# - Drawdown monitoring
# - Risk alerts

Risk Metrics:

• Volatility: Annualized standard deviation, ATR
• Liquidity: Bid-ask spread, volume analysis
• VaR 95%: Value at Risk calculation
• Drawdown: Peak-to-trough monitoring
• Correlation: Concentration risk

7. Configurable Debate Rounds

Run multiple rounds of structured debate:

# Run 3-round debate
debate = tool.run_debate(asset="XAU", rounds=3)

## ? Structured Debate: XAU

### Round 1
? **Bull**: XAU showing strong support...
   Strength: 60%

? **Bear**: Resistance at $4,742...
   Strength: 50%

**Winner**: ? BULL

---

### Round 2
...

**Verdict**: BULL CASE stronger overall

Use Cases:

• High conviction decisions: 3-5 rounds
• Quick assessment: 1 round
• Balanced view: 2 rounds

Risk Management Rules

Always enforce these rules:

1. Position Size: Never risk more than 2% per trade
2. Stop Loss: Always set a stop loss before entering
3. Risk/Reward: Minimum 1.5:1 reward-to-risk ratio
4. Correlation: Don't overexpose to correlated assets
5. Max Exposure: Never exceed 50% of account in open positions
6. Daily Loss: Stop trading after 5% daily drawdown

Example Scenarios

Scenario 1: Quick Price Check

User: "What's gold at?"

Response:

Gold (XAU/USD): $4,640.70 ↑ 0.45% today
Updated: a few seconds ago
24h Range: $4,620 - $4,669

Scenario 2: Trade Evaluation

User: "Should I buy gold at $4,640 with stop at $4,620?"

Response: Using the Trade Setup Evaluation framework... [Full analysis with entry, risk, reward calculations]

Scenario 3: Multi-Asset View

User: "Show me precious metals and crypto prices"

Response: Using Multi-Asset Correlation Analysis... [Table with all assets, changes, correlations]

---

Real-time market data from gold-api.com with trading analysis frameworks

MCP/OpenClaw Integration

MCP Server Usage

The skill exposes a clean MCP tool surface for OpenClaw:

from scripts.mcp_server import handle_mcp_request

# Get gold price
request = {
    "tool": "get_price",
    "params": {"symbol": "XAU"}
}
response = handle_mcp_request(request)
# Returns: {"success": true, "symbol": "XAU", "name": "Gold", "price": 4640.70, ...}

# Analyze trade
request = {
    "tool": "analyze_trade_setup",
    "params": {
        "asset": "XAU",
        "entry": 4640,
        "stop": 4620,
        "target": 4680,
        "account": 10000,
        "risk_percent": 1.0
    }
}
response = handle_mcp_request(request)
# Returns: {"success": true, "approved": true, "position_size": 0.5, ...}

# Run debate
request = {
    "tool": "run_debate",
    "params": {
        "asset": "XAU",
        "current_price": 4640,
        "rounds": 3
    }
}
response = handle_mcp_request(request)
# Returns: {"success": true, "verdict": "BULL_CASE_STRONGER", "rounds": [...]}

Available MCP Tools

Tool	Purpose	Key Params
get_price	Single asset price	symbol
get_all_prices	All 7 assets	-
analyze_trade_setup	Trade analysis	asset, entry, stop, target
market_analysis	Multi-agent analysis	asset, support, resistance, rsi
run_debate	Bull/bear debate	asset, current_price, rounds
create_portfolio	Initialize portfolio	initial_capital, max_risk_per_trade
check_order	Validate order	asset, entry, stop
get_portfolio_summary	Portfolio status	-
check_risk	Risk assessment	assets, portfolio_value

Response Format

All tools return consistent JSON:

{
  "success": true/false,
  "data": { ... },
  "error": "string (if failed)"
}

Integration with Other Skills

Combine with other skills for enhanced analysis:

• decision-helper: Evaluate multiple trade options
• strategy-advisor: Long-term market positioning
• data-analyst: Statistical analysis of returns
• deep-research: Research market fundamentals