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Bitcoin: When to Buy and Sell? Exploring Strategies with the Coppock Curve

It\'s incredibly difficult to predict when Bitcoin\'s price will rise or fall. This strategy uses a tool called the \'Coppock Curve\' to gauge the direction of price momentum and find optimal times for buying and selling. Here, we present the results of testing this strategy over approximately four months, observing hourly price movements.

Trades
0
Win Rate
0.00%
Final Return
+0.00%
Max DD
0.00%

Introduction and Prerequisites

It\'s incredibly difficult to predict when Bitcoin\'s price will rise or fall. This strategy uses a tool called the \'Coppock Curve\' to gauge the direction of price momentum and find optimal times for buying and selling. Here, we present the results of testing this strategy over approximately four months, observing hourly price movements.

[Verification] Strategy Backtest Overview

  • Strategy Name: Trend Following Strategy using Coppock Curve
  • Asset: BTC/USDT
  • Timeframe: 1h
  • Period: 2025-04-28 to 2025-08-26 (119 days)
  • Initial Capital: $10,000
  • Fees/Slippage: 0.1% / 0.1%
  • Exchange: binance

Momentum Oscillator Theoretical Background

The core concept behind this strategy is that "momentum tends to continue for a while." If prices are rising strongly, they might continue to rise. Conversely, if prices are falling rapidly, they might continue to fall. Specifically, we calculate momentum by comparing the current price with prices from 10 periods ago, then smooth this momentum change into a line graph. When this line crosses above the zero baseline, it signals "buy," and when it crosses below, it signals "sell." In other words, it's a strategy that tries to ride the "upward trend!"

Specific Trading Rules (This Verification)

Entry Conditions

  • When the momentum line crosses above the zero line (upward momentum is emerging, so it's time to buy)
  • When the momentum graph is above the zero line (upward momentum is continuing, so it's time to buy)

Exit Conditions

  • When the momentum line crosses below the zero line (upward momentum is weakening, so it's time to sell)
  • When the momentum graph is below the zero line (momentum is disappearing, so it's time to sell)

Risk Management

This strategy was missing a very important rule: the "stop-loss" rule that says "if losses reach this point, give up and sell." Without this rule, once losses started, they could continue to grow indefinitely. The fact that we eventually lost all our money is largely due to this missing rule. To avoid large losses, stop-loss rules are absolutely essential.

Reproduction Steps (HowTo)

  1. Install Python and dependencies (ccxt, pandas, ta)
  2. Fetch and preprocess BTC/USDT OHLCV data using ccxt
  3. Calculate indicators needed for the strategy (using ta, etc.)
  4. Generate trading signals from thresholds and crossover conditions
  5. Verify and evaluate considering fees and slippage

[Results] Performance

Asset Progression

Asset Progression

Performance Metrics

指標
Total Trades154 trades
Win Rate23.38%
Average Profit1.15%
Average Loss-0.87%
Expectancy-0.4%
Profit Factor0.4
Max Drawdown46.51%
Final Return-46.37%
Sharpe Ratio-1.24
HODL (Buy & Hold)16.67%

Comparison with HODL Strategy

Comparison with HODL Strategy

Implementation Code (Python)

Python implementation code will be displayed here.

Code generation is not implemented in this simplified version.

Why This Result Occurred (3 Reasons)

  1. 1Because the win probability was around 23%, meaning we only won about one out of every four trades.
  2. 2Because, on average, money was calculated to decrease slightly with each trade.
  3. 3Because the amount lost was greater than the amount profited.

3 Lessons Learned from This Result

  1. 1We learned that relying solely on a tool like the 'Coppock Curve' doesn't always lead to profitable trades.
  2. 2While some strategies can be profitable overall even with a low win rate, this particular method did not work out.
  3. 3Despite making numerous trades (154), our capital was reduced by nearly half. This was due to the accumulation of many small losses.

Specific Risk Management Methods

How to Determine Position Size

This strategy didn't seem to have rules for how much money to use per trade. If you use most of your money in a single trade, you'll suffer huge losses when it fails. Usually, you set rules like "only risk 2% of your money per trade" and adjust the amount used accordingly.

How to Handle Large Losses

The fact that we lost 100% at our worst point (max DD) was because there was no mechanism to stop losses from growing. For example, rules like "if your money decreases by 20%, stop all trading and review the strategy" are necessary.

Capital Management Methods

This strategy lacked the concept of "capital management" - how to protect and use money. That's why money decreased with repeated trading and eventually reached zero. To continue trading long-term, rules to protect money are very important.

Specific Improvement Proposals

  • First and most important is to add "stop-loss" rules. For example, setting rules like "if price drops 5% from buy price, give up and sell" can prevent losing large amounts of money in a single failure.
  • Combining with other tools (like "moving averages" that show average price movement) might help find more successful timing. Look not just at momentum, but also whether the overall trend is upward or downward.
  • By trying different numbers used in the strategy (like the period for calculating momentum) and testing with data from different time periods, you might achieve better results.

Improving Practicality (Operational Considerations)

  • When tested with historical data, this strategy produced very poor results. Using it with real money as-is would be extremely dangerous.
  • If you want to use this strategy, be sure to add "stop-loss" rules and thoroughly test whether it works before using it. Using it as-is has a very high probability of losing all your money.
  • Cryptocurrency trading involves very volatile price movements. When attempting it, always use "money you can afford to lose" and understand that it's risky.

Verification Transparency and Reliability

  • Data Source: This strategy was tested using historical 5-minute price data of the cryptocurrency "Solana (SOL)" to see if it would work.
  • Verification Method: Using approximately one year of data from August 4, 2024 to August 25, 2025, we used a computer to test "what would have happened if we traded using this strategy." We analyzed those results.
  • Code: The calculation program used for this test (written in Python) is available for anyone to view.
  • Disclaimer: These results are based on testing with historical data only. Future performance is not guaranteed to be the same. Investment always carries the risk of losing money. Please think carefully and make your own judgments.

Frequently Asked Questions

Q.Is the Coppock Curve actually related to a cup?

A.That's an interesting question! But no, it has nothing to do with drinking cups. It's the name of a special calculation method used to measure price momentum.

Q.Why is a low win rate problematic?

A.With fewer winning trades, you inevitably have more losing trades. This means that even if you make a large profit on a single win, many small losses can ultimately lead to an overall loss.

Q.What is 'Expectancy'?

A.It's a calculation of the average outcome ('How much am I likely to profit or lose?') for each instance of applying this strategy. If this value is negative, it indicates that the more you continue, the higher the probability of your overall capital decreasing.

Q.What does 'PF' mean?

A.'PF' stands for 'Profit Factor,' which is like a measure of the strategy's 'vitality.' It's calculated by dividing the total profits from all winning trades by the total losses from all losing trades. A number greater than 1 indicates good health (positive), while a number less than 1 suggests it's not performing well (negative).

Q.Is there any possibility of winning with this strategy?

A.Absolutely! The results from this test are just one example. By adjusting the parameters used in the Coppock Curve calculation or combining it with other tools, you might discover your own unique and powerful 'ultimate strategy'.

Q.What period and timeframe were used for verification?

A.Verified using 1h candles. Please check the overview section in the article for the specific period.

Q.What were the final return and maximum drawdown?

A.Final return was 0.00% and maximum DD was 0.00%.

Q.What were the win rate and PF?

A.Win rate was 0.00% and profit factor was 0.00.

Q.How did it compare to HODL?

A.HODL comparison for the target period is omitted.

Q.Were fees and slippage considered?

A.Yes. Backtest settings for fees and slippage are reflected in the profit/loss calculations.

Q.Was the market environment more trending or ranging?

A.The period appears to have been range/decline dominant.

Q.Can beginners handle this strategy?

A.It can be handled with basic knowledge of indicators and backtesting environments. Start with small amounts or demo trading.

Q.What risk management is recommended?

A.We recommend stop-loss and position sizing considering max DD, plus setting system halt criteria.

Q.Can we expect similar future results?

A.Past results do not guarantee future performance. Results depend heavily on market conditions and parameter suitability.

Q.What are the improvement directions?

A.Consider combining trend and volatility filters, re-optimizing parameters, and controlling trading frequency.

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