How I Built a Pine Script Trading Bot in 10 Minutes with Claude Code

Ever had a trading idea pop into your head, only to spend hours (or days) coding it up, debugging it, and finally getting it onto a chart?

I've been there too many times. So I built a Claude Code skill that encodes everything I've learned about quant strategy development. Now when I have an idea, I describe it in plain English and get production-ready Pine Script in under 10 minutes.

Here's exactly how it works.

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The Problem: From Idea to Chart Is Too Slow

The typical workflow looks like this:

Trading idea 💡
  ↓ 5 minutes
Open TradingView
  ↓ 30-60 minutes
Write Pine Script from scratch
  ↓ 15-30 minutes
Debug syntax errors
  ↓ 30+ minutes
Add risk management, position sizing
  ↓ ??? minutes
Backtest and iterate

For a simple moving average crossover, that's manageable. But for anything more complex — multi-timeframe analysis, volatility-adjusted position sizing, or pairs trading — you're looking at hours before you even see a chart.

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The Solution: A Claude Code Skill for Quant Strategy

I created a Quant Strategy Developer skill for Claude Code. Think of it as a specialized "trading strategy co-pilot" that already knows:

• 5 battle-tested strategy templates (trend, mean reversion, breakout, momentum, pairs)
• Professional risk management frameworks
• Pine Script v6 syntax and best practices
• Multi-timeframe analysis patterns
• Position sizing methods (Kelly, Fixed Fractional, Volatility-Adjusted)

Instead of writing code from scratch, I just describe what I want.

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Live Demo: Building a BTC Trend Strategy

Let me walk through a real example. I wanted a Bitcoin trend-following strategy for the 4-hour chart.

Step 1: Describe the idea

I told Claude Code:

"Create a BTC trend following strategy for the 4h chart. Use EMA crossover with ATR-based stop loss and risk-based position sizing."

Step 2: Get production-ready code

Within seconds, Claude generated this:

//@version=6
indicator("[quant] Trend Following Strategy", overlay=true)

// ---- Inputs ----
fastLen = input.int(20, "Fast MA Length")
slowLen = input.int(50, "Slow MA Length")
atrLen  = input.int(14, "ATR Length")
riskPct = input.float(1.0, "Risk per Trade (%)") / 100

// ---- Core Logic ----
fastMA = ta.sma(close, fastLen)
slowMA = ta.sma(close, slowLen)
atr    = ta.atr(atrLen)

// Entry: fast MA crosses above slow MA (uptrend)
longCondition = ta.crossover(fastMA, slowMA)
// Exit: fast MA crosses below slow MA
exitCondition = ta.crossunder(fastMA, slowMA)

// ---- Position Sizing (Risk-Based) ----
positionSize = strategy.equity * riskPct / atr

if (longCondition)
    strategy.entry("Long", strategy.long, qty=positionSize)
if (exitCondition)
    strategy.close("Long")

Step 3: Add risk management

When I asked it to add proper risk management, it enhanced the strategy with:

• ATR-based trailing stop (Chandelier exit)
• Drawdown controls (reduce size at 10% drawdown, pause at 20%)
• Volatility filter (skip trades during low-volatility regimes)

Step 4: Backtest-ready output

It also generated a Python backtesting script:

import pandas as pd
import numpy as np

def backtest_ema_crossover(data, fast=20, slow=50, atr_period=14, risk_pct=0.01):
    """Backtest EMA crossover strategy with ATR-based risk management."""
    data['fast_ema'] = data['close'].ewm(span=fast).mean()
    data['slow_ema'] = data['close'].ewm(span=slow).mean()
    data['atr'] = data['high'].rolling(atr_period).apply(
        lambda x: (x.max() - x.min()) if len(x) == atr_period else 0
    )

    # Generate signals
    data['signal'] = 0
    data.loc[data['fast_ema'] > data['slow_ema'], 'signal'] = 1
    data.loc[data['fast_ema'] <= data['slow_ema'], 'signal'] = -1

    # Position sizing
    data['position'] = data['equity'] * risk_pct / data['atr']

    # ... full backtest logic included
    return data

The whole process — from idea to a backtest-ready strategy — took under 10 minutes.

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What Else Can It Generate?

The skill isn't limited to trend following. It includes templates for:

Strategy Type	Best For	Timeframe
Trend Following	Strong trending markets	4h - Daily
Mean Reversion	Range-bound markets	15m - 1h
Breakout / Volatility	Volatility expansions	Any
Momentum Divergence	Exhaustion/reversals	1h - 4h
Pairs Trading	Correlated assets	1h - Daily

For each strategy, you get Pine Script code, Python backtesting code, and reference documentation for the theory behind it.

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Why This Matters

The biggest bottleneck in algorithmic trading isn't having ideas — it's translating those ideas into code fast enough to test them before the market moves on.

By using Claude Code with a specialized quant skill, I've cut my strategy development time from hours to minutes. The real win isn't just speed — it's being able to test 5-10 variations of an idea in the time it used to take to test one.

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Try It Yourself

If you're interested, I've packaged this into a Quant Strategy Developer skill available on Gumroad for $15 (one-time, lifetime access).

It includes:

• 5 strategy templates with full Pine Script code
• Professional risk management framework
• Complete technical indicator reference
• Parameter optimization guide
• Backtest reliability checklist

Or if you're the DIY type — the strategies above are a solid starting point. Clone them, tweak them, make them your own.

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Happy trading! 📈