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Operation: Finding Bitcoin\'s \'True Momentum\'! An Investment Story for Middle Schoolers

This story is about an operation to find the \'true momentum\' of Bitcoin\'s price movements, specifically by looking at its hourly price changes. We\'ll clearly explain what happened when we tested this operation for approximately four months, from May 8, 2025, to September 5, 2025.

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

Introduction and Prerequisites

This story is about an operation to find the \'true momentum\' of Bitcoin\'s price movements, specifically by looking at its hourly price changes. We\'ll clearly explain what happened when we tested this operation for approximately four months, from May 8, 2025, to September 5, 2025.

[Verification] Strategy Backtest Overview

  • Strategy Name: Trend Following Strategy using Weighted Close
  • Asset: BTC/USDT
  • Timeframe: 1h
  • Period: 2025-05-08 to 2025-09-05 (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 Trades146
Win Rate15.75%
Average Profit1.24%
Average Loss-0.75%
Expectancy-0.44%
Profit Factor0.3
Max Drawdown47.4%
Final Return-47.29%
Sharpe Ratio-1.34
HODL (Buy & Hold)9.85%

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. 1This strategy had a win rate of only 15.75%, which is quite low. This means we lost on most trades, resulting in a significant overall loss of -47.29%.
  2. 2The 'Expectancy' value, which indicates the average profit per trade, was negative at -0.44%. This suggests that the more trades we made, the more likely it was that our money would gradually decrease.
  3. 3The 'Profit Factor' (PF), a number that balances profits against losses, was 0.3. This is much smaller than 1, meaning the total amount lost was significantly larger than the total amount gained.

3 Lessons Learned from This Result

  1. 1I learned that even with a low win rate, it's possible to end up with a profit if you can achieve very large wins on a few trades (though that wasn't the case this time).
  2. 2I clearly understood that making many trades (146) with a low win rate leads to losses piling up.
  3. 3At its worst, nearly half of the initial capital was lost (Max DD 47.4%). This shows that this strategy was quite risky.

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.How is 'Weighted Close' calculated?

A.It's calculated by adding the highest price, the lowest price, and the closing price twice, then dividing the sum by 4. This method gives a little extra importance to the closing price to try and capture the true momentum of that period.

Q.What is a 'Moving Average'?

A.It's the average price over a certain period. For example, a '20-hour Moving Average' is calculated by adding up the prices from the past 20 hours and dividing by 20. When shown on a graph, it forms a line that makes it easier to see the overall price trend.

Q.What does a low win rate mean?

A.It means the percentage of successful trades (where you made a profit) out of the total number of attempts is low. In this strategy's case, it means that for every 100 attempts, only about 15 were successful.

Q.What does a negative 'Expectancy' mean?

A.It means that on average, you are more likely to lose a little bit of money with each trade. Think of it like playing a game where you consistently get prizes worth less than the entry fee.

Q.What is 'PF'?

A.'PF' stands for 'Profit Factor'. It's the number you get when you divide the total amount of money you've won by the total amount of money you've lost. If it's less than 1, it means your losses were greater than your profits. In this case, it was 0.3, so the losses were significantly larger.

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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