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Can Bitcoin\'s Movements Be Predicted? Taking on the Challenge with the "Mysterious Movement Detector"!

This strategy attempts to find optimal buy/sell timing by observing how much Bitcoin\'s price movements deviate from a \'random, dice-roll-like movement.\' We tried frequent buying and selling within short intervals, looking at the price every 5 minutes. Unfortunately, the result was that all our capital was lost... Let\'s explore together why this happened.

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

Introduction and Prerequisites

This strategy attempts to find optimal buy/sell timing by observing how much Bitcoin\'s price movements deviate from a \'random, dice-roll-like movement.\' We tried frequent buying and selling within short intervals, looking at the price every 5 minutes. Unfortunately, the result was that all our capital was lost... Let\'s explore together why this happened.

[Verification] Strategy Backtest Overview

  • Strategy Name: Trend Following Strategy using Random Walk Index
  • Asset: BTC/USDT
  • Timeframe: 5m
  • Period: 2024-07-23 to 2025-08-25 (397 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 Trades4733 trades
Win Rate10.8%
Average Profit0.58%
Average Loss-0.51%
Expected Value-0.4%
Profit Factor0.18
Max Drawdown100%
Final Return-100%
Sharpe Ratio-2.12
HODL (Buy & Hold)68.75%

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. 1The probability of winning was very low (about 1 in 10 trades), meaning most buy/sell operations resulted in a loss.
  2. 2On average, the amount lost per trade was greater than the amount gained. Therefore, the more trades we made, the more money we lost.
  3. 3The balance between profit and loss was also poor. The amount gained was significantly smaller compared to the amount lost, as indicated by the low profit factor.

3 Lessons Learned from This Result

  1. 1We learned that relying on a single tool like the 'Random Walk Index' to determine buy/sell timing can be difficult.
  2. 2Even with a low win rate, it's possible to achieve an overall profit if each winning trade yields a very large profit and losses are kept small. However, this strategy did not achieve that.
  3. 3Simply buying and holding Bitcoin (HODLing) yielded better results. This suggests that frequent trading, in this case, actually increased 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.How is the Random Walk Index calculated?

A.It's calculated by comparing past price movements with current price movements to gauge momentum.

Q.When should this strategy be used?

A.It's said to be responsive during periods of significant price volatility or when the price movement is very stagnant. It's suitable for short timeframes like 5-minute intervals.

Q.Does a low win rate mean this is a completely ineffective strategy?

A.Not necessarily. Even with a low win rate, if a single winning trade generates a substantial profit and losses are minimized, the overall outcome can still be positive.

Q.Can this be used for assets other than Bitcoin?

A.Theoretically, yes. However, the price movements of Bitcoin and stocks differ, so its effectiveness would need to be tested on other assets.

Q.What is 'HODL,' and why did it perform better than this strategy?

A.HODL means buying and holding an asset like Bitcoin without selling. In this case, frequent trading incurred fees and potentially poor timing, likely leading to worse performance compared to simply holding the asset.

Q.What period and timeframe were used for verification?

A.Verified using 5m 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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