Coppock Curve

1. Introduction & Hook

The Coppock Curve is a powerful momentum indicator that has stood the test of time. Developed in the 1960s, it remains a favorite among traders seeking to identify long-term buying opportunities. But what makes the Coppock Curve unique? How can you leverage it in Pine Script, Python, Node.js, and more? This comprehensive guide will take you from the basics to advanced automation, ensuring you master every aspect of the Coppock Curve strategy.

2. What is Coppock Curve?

The Coppock Curve is a momentum oscillator designed to identify major market bottoms. Unlike many oscillators, it was originally intended for monthly charts, helping investors spot long-term buying opportunities. The indicator combines rate-of-change calculations with a weighted moving average, smoothing out noise and highlighting significant shifts in market sentiment.

3. Market Logic Behind the Strategy

The Coppock Curve is rooted in the belief that emotional market extremes—especially after prolonged declines—create the best buying opportunities. By measuring the speed and direction of price changes over specific periods, the indicator aims to capture the moment when bearish momentum fades and bullish sentiment emerges. This logic makes it particularly effective for timing entries after deep corrections or bear markets.

4. Mathematical Foundation & Formula

The Coppock Curve formula is straightforward yet elegant. It involves three main steps:

• Calculate the 14-period Rate of Change (ROC) of closing prices.
• Calculate the 11-period ROC of closing prices.
• Add the two ROCs together and smooth the result with a 10-period Weighted Moving Average (WMA).

The general formula is:

Coppock Curve = WMA(ROC(close, 14) + ROC(close, 11), 10)

Where:

• ROC(close, n): Rate of Change over n periods.
• WMA: Weighted Moving Average.

5. Step-by-Step Calculation Example

Let’s walk through a simplified example using daily closing prices:

1. Suppose today’s close is 110, 14 days ago it was 100, and 11 days ago it was 105.
2. Calculate 14-period ROC: ((110 - 100) / 100) * 100 = 10%
3. Calculate 11-period ROC: ((110 - 105) / 105) * 100 ≈ 4.76%
4. Add the two: 10% + 4.76% = 14.76%
5. Apply a 10-period WMA to the running sum of these values to get the Coppock Curve value for today.

This process repeats for each new bar, creating a smooth oscillator that highlights major trend reversals.

6. Pine Script Implementation

Pine Script makes it easy to implement the Coppock Curve. Below is a well-commented example:

//@version=6
indicator("Coppock Curve", overlay=false)
// Parameters
rocLength1 = input.int(14, title="ROC Length 1")
rocLength2 = input.int(11, title="ROC Length 2")
wmaLength = input.int(10, title="WMA Length")
// Calculate Rate of Change
roc1 = ta.roc(close, rocLength1)
roc2 = ta.roc(close, rocLength2)
// Sum the ROCs
rocSum = roc1 + roc2
// Weighted Moving Average
coppock = ta.wma(rocSum, wmaLength)
// Plot
plot(coppock, color=color.blue, linewidth=2, title="Coppock Curve")
hline(0, "Zero Line", color=color.gray)
// Signal: Cross above zero = Buy
buySignal = ta.crossover(coppock, 0)
plotshape(buySignal, style=shape.triangleup, location=location.belowbar, color=color.green, size=size.small, title="Buy Signal")

This script calculates the Coppock Curve and plots buy signals when the curve crosses above zero.

7. Parameters & Customization in Pine Script

Customization is key to adapting the Coppock Curve to different markets and timeframes. In Pine Script, you can expose parameters for ROC lengths and WMA smoothing. For example:

// Customizable parameters
rocLength1 = input.int(14, minval=1, title="ROC Length 1")
rocLength2 = input.int(11, minval=1, title="ROC Length 2")
wmaLength = input.int(10, minval=1, title="WMA Length")

Experiment with these values to optimize the indicator for your trading style. Shorter periods increase sensitivity, while longer periods smooth out noise.

8. Python & FastAPI + NoSQL Implementation

Python is ideal for backtesting and deploying the Coppock Curve in production systems. Here’s a Python implementation using Pandas, plus a FastAPI endpoint for real-time calculation. Assume you store OHLCV data in a NoSql Database like MongoDB.

# Python Coppock Curve calculation
import pandas as pd

def coppock_curve(df, roc1=14, roc2=11, wma=10):
    roc_1 = df['close'].pct_change(roc1) * 100
    roc_2 = df['close'].pct_change(roc2) * 100
    roc_sum = roc_1 + roc_2
    coppock = roc_sum.rolling(window=wma).apply(lambda x: ((wma * x).sum()) / (wma * (wma + 1) / 2), raw=True)
    return coppock

# FastAPI endpoint
from fastapi import FastAPI, Query
from pymongo import MongoClient

app = FastAPI()
client = MongoClient('mongodb://localhost:27017/')
db = client['market_data']

@app.get("/coppock")
def get_coppock(symbol: str, roc1: int = 14, roc2: int = 11, wma: int = 10):
    data = list(db.ohlcv.find({"symbol": symbol}))
    df = pd.DataFrame(data)
    df['coppock'] = coppock_curve(df, roc1, roc2, wma)
    return df[['date', 'coppock']].tail(1).to_dict(orient='records')[0]

This setup allows you to calculate the Coppock Curve on demand, using live or historical data from a NoSql Database.

9. Node.js / JavaScript Implementation

JavaScript is popular for web-based trading dashboards and bots. Here’s a Node.js example using arrays:

// Node.js Coppock Curve calculation
function rateOfChange(arr, period) {
  let result = [];
  for (let i = 0; i < arr.length; i++) {
    if (i < period) {
      result.push(null);
    } else {
      result.push(((arr[i] - arr[i - period]) / arr[i - period]) * 100);
    }
  }
  return result;
}

function weightedMA(arr, period) {
  let result = [];
  let denom = (period * (period + 1)) / 2;
  for (let i = 0; i < arr.length; i++) {
    if (i < period - 1) {
      result.push(null);
    } else {
      let sum = 0;
      for (let j = 0; j < period; j++) {
        sum += arr[i - j] * (period - j);
      }
      result.push(sum / denom);
    }
  }
  return result;
}

function coppockCurve(closes, roc1 = 14, roc2 = 11, wma = 10) {
  const rocA = rateOfChange(closes, roc1);
  const rocB = rateOfChange(closes, roc2);
  const rocSum = rocA.map((val, idx) => val !== null && rocB[idx] !== null ? val + rocB[idx] : null);
  return weightedMA(rocSum, wma);
}

// Example usage:
const closes = [100, 102, 105, 107, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185];
const coppock = coppockCurve(closes);
console.log(coppock);

This code can be integrated into trading bots or browser-based charting tools.

10. Backtesting & Performance Insights

Backtesting is essential to validate the Coppock Curve’s effectiveness. In Pine Script, you can convert the indicator into a strategy:

//@version=6
strategy("Coppock Curve Strategy", overlay=false)
roc1 = ta.roc(close, 14)
roc2 = ta.roc(close, 11)
coppock = ta.wma(roc1 + roc2, 10)
if ta.crossover(coppock, 0)
    strategy.entry("Long", strategy.long)
if ta.crossunder(coppock, 0)
    strategy.close("Long")

Analyze win rate, drawdown, and profit factor. The Coppock Curve often excels in trending markets but may underperform during sideways conditions. Always test across multiple assets and timeframes.

11. Risk Management Integration

Effective risk management is crucial. Combine the Coppock Curve with position sizing, stop-loss, and take-profit rules. Here’s an example in Pine Script:

// Risk management example
riskPct = input.float(1, title="Risk % per Trade")
stopLoss = input.float(2, title="Stop Loss %")
takeProfit = input.float(4, title="Take Profit %")
if ta.crossover(coppock, 0)
    strategy.entry("Long", strategy.long, qty_percent=riskPct)
    strategy.exit("TP/SL", from_entry="Long", stop=close * (1 - stopLoss / 100), limit=close * (1 + takeProfit / 100))

This ensures each trade is controlled and automated exits are enforced.

12. Combining with Other Indicators

The Coppock Curve works best when combined with other tools. For example, use a moving average to confirm trend direction or RSI to filter overbought/oversold conditions. Here’s a Pine Script snippet:

// Combine Coppock with 200-period SMA
sma200 = ta.sma(close, 200)
longCondition = ta.crossover(coppock, 0) and close > sma200
if longCondition
    strategy.entry("Long", strategy.long)

This approach reduces false signals and improves reliability.

13. Multi-Timeframe & Multi-Asset Usage

The Coppock Curve can be applied to any timeframe or asset. For multi-timeframe analysis in Pine Script:

// Multi-timeframe Coppock Curve
coppockHigher = request.security(syminfo.tickerid, "D", ta.wma(ta.roc(close, 14) + ta.roc(close, 11), 10))
if ta.crossover(coppock, 0) and coppockHigher > 0
    strategy.entry("Long", strategy.long)

Use it for equities, forex, crypto, or options. Adjust parameters for each market’s volatility and trading style.

14. AI/ML Enhancements

Machine learning can optimize Coppock Curve parameters or use its signals as features. For example, a reinforcement learning (RL) agent can adjust ROC and WMA lengths to maximize returns. Here’s a pseudocode outline:

# RL agent pseudocode
for episode in range(num_episodes):
    state = get_market_state()
    action = agent.select_parameters(state)
    reward = backtest_coppock(action)
    agent.learn(state, action, reward)

Feature engineering: Use Coppock values, zero-crossings, and slopes as ML features for predictive models.

15. Automation with Playwright/Jest

Automated testing ensures your Coppock Curve scripts remain robust. Use Jest for unit tests or playwright for end-to-end checks. Example Jest test:

// Jest unit test for Coppock calculation
const { coppockCurve } = require('./coppock');
test('Coppock Curve basic calculation', () => {
  const closes = [100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170];
  const result = coppockCurve(closes, 14, 11, 10);
  expect(result[result.length - 1]).not.toBeNull();
});

Playwright can automate browser-based strategy deployment and validation.

16. Advanced Variations

Advanced traders may experiment with:

• Alternative smoothing methods (EMA, SMA)
• Dynamic ROC periods based on volatility
• Combining with volume or volatility filters
• Adaptive thresholds for zero-cross signals

These tweaks can enhance performance in specific markets.

17. Common Pitfalls & Misconceptions

• Not just for monthly charts: The Coppock Curve can be adapted to any timeframe.
• False signals in sideways markets: Always confirm with trend filters.
• Lagging indicator: It’s designed for major reversals, not quick scalps.
• Parameter sensitivity: Test and optimize for your asset and timeframe.

18. Conclusion & Key Takeaways

The Coppock Curve is a robust, time-tested momentum indicator. Its simplicity and adaptability make it suitable for a wide range of markets and strategies. By understanding its logic, customizing parameters, and integrating with automation and risk management, you can harness its full potential. Always backtest and combine with other tools for best results.

Glossary of Key Terms

• ROC (Rate of Change): Measures the percentage change in price over a specified period.
• WMA (Weighted Moving Average): A moving average that gives more weight to recent data.
• Momentum Oscillator: An indicator that measures the speed of price movements.
• Zero Line: The baseline in oscillators; crossings often signal trend changes.
• Backtesting: Testing a strategy on historical data to evaluate performance.
• Risk Management: Techniques to control losses and protect capital.
• Multi-Timeframe Analysis: Using indicators across different chart intervals.
• Reinforcement Learning: A type of machine learning focused on maximizing rewards through trial and error.

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