Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.

MITAuto-check passedBusiness, Finance & HR

Install Auto Trader

skills CLI
$ npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Signal-Execution-Labs/forex-trading-ai-agent auto-trader --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Signal-Execution-Labs/forex-trading-ai-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/auto-trader .claude/skills/auto-trader && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
auto-trader
GitHub stars
162
Token cost
~2.1k tokens
SKILL.md length
180 words
Files
2 (incl. scripts)
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.

  • Works in 6 steps: ✅ Backtest strategy with historical data → ✅ Paper trade for at least 2 weeks → ✅ Define clear entry/exit rules → …
  • Tasks that involve Trading and backtesting
  • SKILL.md covers Overview, Configuration, Commands and Workflow
  • Runs Python scripts from its folder; calls python3

What it does

Auto Trader is an agent skill from Signal-Execution-Labs/forex-trading-ai-agent. Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/position_calculator.py`).

It sits in Business, Finance & HR, covering Trading and backtesting. The repository describes itself as: About AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading…. The licence is MIT.

When your agent uses it

  • Tasks that involve Trading and backtesting

Example prompts

  • “/auto-trader”

Requirements

  • Python 3
  • A credential in YOUR_KEY
  • A credential in YOUR_SECRET

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. ✅ Backtest strategy with historical data
  2. ✅ Paper trade for at least 2 weeks
  3. ✅ Define clear entry/exit rules
  4. ✅ Set maximum position sizes
  5. ✅ Set daily loss limits
  6. ✅ Test with small amounts first

What it can do on your machine

Read from SKILL.md and the folder at commit b8a6047. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Auto Trader loads about 2.1k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 180 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Signal-Execution-Labs/forex-trading-ai-agent at commit b8a6047, republished under its MIT licence (© Signal-Execution-Labs). 180 words, ~2,086 tokens.

Download SKILL.mdSave it as .claude/skills/auto-trader/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
auto-trader
description
Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.

Auto Trader

Automated trading execution with risk management.

Overview

  • Strategy Execution - Run predefined trading strategies
  • Risk Management - Position sizing, max drawdown limits
  • Order Management - Stop-loss, take-profit, trailing stops
  • Trade Logging - Complete audit trail

⚠️ WARNING: Automated trading involves significant risk. Always test with small amounts first!

Configuration

Trading config in ~/.kit/auto-trader.json:

json
{
  "exchange": "binance",
  "sandbox": true,
  "risk": {
    "max_position_pct": 5,
    "max_daily_loss_pct": 3,
    "default_stop_loss_pct": 2,
    "default_take_profit_pct": 4
  },
  "strategies": ["rsi_reversal", "ma_crossover"],
  "symbols": ["BTC/USDT", "ETH/USDT"]
}

Commands

Position Sizing Calculator
bash
python3 -c "
account_balance = 10000  # USD
risk_per_trade_pct = 2   # Risk 2% per trade
entry_price = 45000      # BTC entry
stop_loss_price = 44000  # Stop loss

risk_amount = account_balance * (risk_per_trade_pct / 100)
price_risk = entry_price - stop_loss_price
position_size = risk_amount / price_risk

print('📊 POSITION SIZE CALCULATOR')
print('=' * 50)
print(f'Account Balance: \${account_balance:,.2f}')
print(f'Risk per Trade: {risk_per_trade_pct}% (\${risk_amount:,.2f})')
print(f'Entry Price: \${entry_price:,.2f}')
print(f'Stop Loss: \${stop_loss_price:,.2f}')
print(f'Price Risk: \${price_risk:,.2f} per unit')
print()
print(f'✅ Position Size: {position_size:.6f} BTC')
print(f'✅ Position Value: \${position_size * entry_price:,.2f}')
"
Simple RSI Strategy
bash
python3 -c "
import ccxt
import ta
import pandas as pd

# Strategy: Buy when RSI < 30, Sell when RSI > 70
symbol = 'BTC/USDT'
exchange = ccxt.binance()

ohlcv = exchange.fetch_ohlcv(symbol, '1h', limit=100)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
df['rsi'] = ta.momentum.RSIIndicator(df['close'], 14).rsi()

current_rsi = df['rsi'].iloc[-1]
current_price = df['close'].iloc[-1]

print(f'📊 RSI STRATEGY: {symbol}')
print('=' * 50)
print(f'Price: \${current_price:,.2f}')
print(f'RSI(14): {current_rsi:.1f}')
print()

if current_rsi < 30:
    print('🟢 SIGNAL: BUY (RSI oversold)')
    print(f'   Entry: \${current_price:,.2f}')
    print(f'   Stop Loss: \${current_price * 0.98:,.2f} (-2%)')
    print(f'   Take Profit: \${current_price * 1.04:,.2f} (+4%)')
elif current_rsi > 70:
    print('🔴 SIGNAL: SELL (RSI overbought)')
else:
    print('⚪ NO SIGNAL: RSI in neutral zone (30-70)')
"
Moving Average Crossover Strategy
bash
python3 -c "
import ccxt
import ta
import pandas as pd

symbol = 'BTC/USDT'
exchange = ccxt.binance()

ohlcv = exchange.fetch_ohlcv(symbol, '4h', limit=100)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

df['ema_12'] = ta.trend.ema_indicator(df['close'], 12)
df['ema_26'] = ta.trend.ema_indicator(df['close'], 26)

current = df.iloc[-1]
previous = df.iloc[-2]
price = current['close']

print(f'📊 MA CROSSOVER STRATEGY: {symbol}')
print('=' * 50)
print(f'Price: \${price:,.2f}')
print(f'EMA(12): \${current[\"ema_12\"]:,.2f}')
print(f'EMA(26): \${current[\"ema_26\"]:,.2f}')
print()

# Check for crossover
if previous['ema_12'] < previous['ema_26'] and current['ema_12'] > current['ema_26']:
    print('🟢 SIGNAL: BUY (Golden Cross - EMA12 crossed above EMA26)')
elif previous['ema_12'] > previous['ema_26'] and current['ema_12'] < current['ema_26']:
    print('🔴 SIGNAL: SELL (Death Cross - EMA12 crossed below EMA26)')
elif current['ema_12'] > current['ema_26']:
    print('📈 TREND: Bullish (EMA12 > EMA26) - Hold/Look for entries')
else:
    print('📉 TREND: Bearish (EMA12 < EMA26) - Stay out or short')
"
Execute Trade with Risk Management
bash
python3 -c "
import ccxt

# Configuration
EXCHANGE_CONFIG = {'apiKey': 'YOUR_KEY', 'secret': 'YOUR_SECRET', 'sandbox': True}
SYMBOL = 'BTC/USDT'
SIDE = 'buy'
RISK_PCT = 2  # 2% of account

exchange = ccxt.binance(EXCHANGE_CONFIG)
balance = exchange.fetch_balance()
account_value = balance['USDT']['free']

# Calculate position size
ticker = exchange.fetch_ticker(SYMBOL)
price = ticker['last']
risk_amount = account_value * (RISK_PCT / 100)
stop_loss_distance = price * 0.02  # 2% stop
position_size = risk_amount / stop_loss_distance

print(f'📊 EXECUTING TRADE')
print('=' * 50)
print(f'Symbol: {SYMBOL}')
print(f'Side: {SIDE.upper()}')
print(f'Entry: \${price:,.2f}')
print(f'Size: {position_size:.6f}')
print(f'Value: \${position_size * price:,.2f}')
print(f'Stop Loss: \${price * 0.98:,.2f}')
print(f'Take Profit: \${price * 1.04:,.2f}')
print()

# Uncomment to execute
# order = exchange.create_market_buy_order(SYMBOL, position_size)
# print(f'✅ Order executed: {order[\"id\"]}')

print('⚠️ DRY RUN - Uncomment to execute real trade')
"
Trailing Stop Implementation
bash
python3 -c "
import ccxt
import time

# Trailing stop: moves stop up as price increases
symbol = 'BTC/USDT'
entry_price = 45000
trailing_pct = 2  # 2% trailing distance
exchange = ccxt.binance()

highest_price = entry_price
stop_price = entry_price * (1 - trailing_pct/100)

print(f'📊 TRAILING STOP: {symbol}')
print(f'Entry: \${entry_price:,.2f}')
print(f'Trailing: {trailing_pct}%')
print('=' * 50)

# Simulation loop
for i in range(10):
    ticker = exchange.fetch_ticker(symbol)
    current_price = ticker['last']
    
    # Update trailing stop if price moved up
    if current_price > highest_price:
        highest_price = current_price
        stop_price = highest_price * (1 - trailing_pct/100)
    
    pnl_pct = ((current_price - entry_price) / entry_price) * 100
    
    print(f'Price: \${current_price:,.2f} | High: \${highest_price:,.2f} | Stop: \${stop_price:,.2f} | P&L: {pnl_pct:+.2f}%')
    
    if current_price <= stop_price:
        print(f'🛑 STOP HIT at \${stop_price:,.2f}')
        break
    
    time.sleep(5)
"
Daily Trading Report
bash
python3 -c "
# Mock trade data - load from log in practice
trades = [
    {'symbol': 'BTC/USDT', 'side': 'buy', 'entry': 45000, 'exit': 46000, 'size': 0.1},
    {'symbol': 'ETH/USDT', 'side': 'buy', 'entry': 2500, 'exit': 2450, 'size': 1.0},
    {'symbol': 'SOL/USDT', 'side': 'buy', 'entry': 100, 'exit': 108, 'size': 5.0},
]

print('📊 DAILY TRADING REPORT')
print('=' * 50)

total_pnl = 0
wins = 0
losses = 0

for trade in trades:
    if trade['side'] == 'buy':
        pnl = (trade['exit'] - trade['entry']) * trade['size']
    else:
        pnl = (trade['entry'] - trade['exit']) * trade['size']
    
    total_pnl += pnl
    if pnl >= 0:
        wins += 1
    else:
        losses += 1
    
    emoji = '🟢' if pnl >= 0 else '🔴'
    print(f'{emoji} {trade[\"symbol\"]:12} {trade[\"side\"]:4} \${pnl:+,.2f}')

print()
print('=' * 50)
win_rate = (wins / len(trades)) * 100 if trades else 0
print(f'Total Trades: {len(trades)}')
print(f'Win Rate: {win_rate:.0f}% ({wins}W / {losses}L)')
print(f'Total P&L: \${total_pnl:+,.2f}')
"

Workflow

Strategy Checklist

Before enabling auto-trading:

  1. ✅ Backtest strategy with historical data
  2. ✅ Paper trade for at least 2 weeks
  3. ✅ Define clear entry/exit rules
  4. ✅ Set maximum position sizes
  5. ✅ Set daily loss limits
  6. ✅ Test with small amounts first
Risk Management Rules
RuleSetting
Max position size5% of account
Max daily loss3% of account
Default stop loss2%
Default take profit4% (2:1 R:R)
Max open trades3
Order Types
TypeUse Case
MarketImmediate execution
LimitBetter price, may not fill
Stop MarketEmergency exit
Stop LimitControlled exit price
OCOTake profit + stop loss together
Trade Logging

All trades logged to ~/.kit/trades/:

json
{
  "id": "trade_001",
  "timestamp": "2026-02-09T14:30:00Z",
  "symbol": "BTC/USDT",
  "side": "buy",
  "entry_price": 45000,
  "exit_price": 46000,
  "size": 0.1,
  "pnl": 100,
  "strategy": "rsi_reversal"
}

© Signal-Execution-Labs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in skills/auto-trader of Signal-Execution-Labs/forex-trading-ai-agent.

  • SKILL.md
  • scripts/position_calculator.py

Open the folder on GitHubat commit b8a6047

Compare with similar skills

Auto Trader next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Tradingview MCPatilaahmettaner/tradingview-mcp5k—~1.3kAutomated safety check: PassMIT
Digital Oraclekomako-workshop/digital-oracle878—~5.9kAutomated safety check: PassMIT
Polyclawchainstacklabs/polyclaw3591 repos~2kAutomated safety check: PassApache-2.0
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Questions about Auto Trader

What does Auto Trader do?

Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit. Auto Trader is an agent skill from Signal-Execution-Labs/forex-trading-ai-agent. Automated trading with strategy execution, risk management, position sizing, and stop-loss/take-profit.

When should I use Auto Trader?

Auto Trader fits situations like: tasks that involve Trading and backtesting.

How do I install Auto Trader in Claude Code?

Run `npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a claude-code`. Or copy the skill folder (skills/auto-trader in Signal-Execution-Labs/forex-trading-ai-agent) into .claude/skills/auto-trader in your project. Claude Code loads it when a task matches its description.

How do I install Auto Trader in Codex?

Run `npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a codex`. Or copy the skill folder (skills/auto-trader in Signal-Execution-Labs/forex-trading-ai-agent) into .agents/skills/auto-trader in your project. Codex loads it when a task matches its description.

Can I use Auto Trader in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Signal-Execution-Labs/forex-trading-ai-agent --skill auto-trader -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto-trader, .gemini/skills/auto-trader, .github/skills/auto-trader and .opencode/skills/auto-trader in your project.

What does Auto Trader need to run?

Going by SKILL.md and its folder, Auto Trader needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3; A credential in YOUR_KEY; A credential in YOUR_SECRET.

Does Auto Trader access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Auto Trader safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Auto Trader use?

Auto Trader is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Auto Trader use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Auto Trader?

Skills that share tags, products or a category with Auto Trader: Tushare Data (zillionare/zillionare, 321 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Digital Oracle (komako-workshop/digital-oracle, 878 stars) and Polyclaw (chainstacklabs/polyclaw, 359 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Auto Trader?

Signal-Execution-Labs (a GitHub organization) maintains it in Signal-Execution-Labs/forex-trading-ai-agent, which has 162 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 14, 2026.

Source: Signal-Execution-Labs/forex-trading-ai-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.