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Unlocking the Secrets of Bitcoin\'s Price Movements! What is the \'Ergodic Oscillator\'?

This story is about a strategy to determine when to buy and sell by looking at Bitcoin\'s price movements over short intervals, specifically every 5 minutes. Unfortunately, when tested with past data, the result was that all the money was lost. Let\'s explore what this strategy was and why it didn\'t work.

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

Introduction and Prerequisites

This story is about a strategy to determine when to buy and sell by looking at Bitcoin\'s price movements over short intervals, specifically every 5 minutes. Unfortunately, when tested with past data, the result was that all the money was lost. Let\'s explore what this strategy was and why it didn\'t work.

[Verification] Strategy Backtest Overview

  • Strategy Name: Trend Following Strategy using Ergodic Oscillator
  • Asset: BTC/USDT
  • Timeframe: 5m
  • Period: 2024-05-20 to 2025-08-25 (462 days)
  • Initial Capital: $10,000
  • Fees/Slippage: 0.1% / 0.1%
  • Exchange: okx

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 Trades10806 trades
Win Rate8.25%
Average Profit0.44%
Average Loss-0.47%
Expectancy-0.4%
Profit Factor0.07
Max Drawdown100%
Final Return-100%
Sharpe Ratio-3.99
HODL (Buy & Hold)67.67%

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. 1There were over 10,000 trades, but only about 8% were successful. This means most trades resulted in losses.
  2. 2On average, money was lost with each trade. This implies that the more trades were made, the greater the losses became.
  3. 3Ultimately, the result was a complete loss of the initial capital. Simply holding Bitcoin without trading would have yielded a much better outcome.

3 Lessons Learned from This Result

  1. 1It's not about trading frequently, but about winning each individual trade. Even if many signals are generated, they are meaningless if they are not accurate.
  2. 2It's crucial to re-evaluate metrics like 'Expectancy' to determine if a strategy is truly viable.
  3. 3Strategies that perform poorly on historical data should not be used as-is. They require adjustments, such as changing parameters or combining them with other methods, to find better approaches.

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.What exactly is the 'Ergodic Oscillator'?

A.It's like a measuring tool to gauge the 'momentum' of price movements. Similar to a car's speedometer, it indicates whether the momentum is strong or weak, and in which direction it's heading.

Q.Isn't an '8.25% win rate' extremely low?

A.Yes, that's correct. It's very low. It means if you made 100 trades, only 8 would be winning trades. Therefore, most trades result in losses.

Q.What does 'PF: 0.07' mean?

A.This is like a performance report calculated as 'Profits / Losses'. A value less than 1 indicates losses, and 0.07 is a very poor number. For example, it implies that for every 100 units of loss, only 7 units were gained. This signifies a situation where losses increase with more trading.

Q.What does 'Max DD: 100%' mean?

A.This refers to 'the percentage of the maximum loss experienced at any point'. A '100%' indicates that at its worst, all the capital was lost. This signifies a very high-risk situation.

Q.Is this strategy safe to use with real money?

A.Based on the test results, using this strategy with real money as-is is very risky. If you're interested, try modifying the settings yourself to achieve better performance. When you do test it, we strongly recommend starting with a very small amount of money that you can afford to lose.

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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