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Bitcoin Trading Challenge! What Were the Results of the "QStick Strategy"?

This strategy involves observing Bitcoin\'s hourly price movements to find optimal buy and sell timings. The core idea is simple: buy when the momentum of price increase is strong, and sell when it weakens. Let\'s see what happened after trying this strategy for about a year, starting from July 2024!

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

Introduction and Prerequisites

This strategy involves observing Bitcoin\'s hourly price movements to find optimal buy and sell timings. The core idea is simple: buy when the momentum of price increase is strong, and sell when it weakens. Let\'s see what happened after trying this strategy for about a year, starting from July 2024!

[Verification] Strategy Backtest Overview

  • Strategy Name: Trend Following Strategy using QStick
  • Asset: BTC/USDT
  • Timeframe: 1h
  • Period: 2024-07-21 to 2025-08-25 (399 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 Trades753 trades
Win Rate17.93%
Average Profit1.67%
Average Loss-0.79%
Expectancy-0.35%
Profit Factor0.48
Max Drawdown93.25%
Final Return-93.13%
Sharpe Ratio-1.13
HODL (Buy&Hold)69.08%

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. 1During the test period, there were 753 trades, indicating a very high frequency of transactions.
  2. 2Out of 100 trades, only about 18 were successful (17.93% win rate), unfortunately meaning most trades resulted in losses.
  3. 3Comparing the total profits to the total losses (Profit Factor), it shows that the total losses were more than twice the total profits.

3 Lessons Learned from This Result

  1. 1We learned that timing trades solely based on price 'momentum' can be more difficult than expected.
  2. 2Even with a low win rate, a strategy can succeed if it achieves very large wins on individual trades. However, that was not the case here.
  3. 3We learned that even with many trades, if the expectancy of each individual trade is negative, continuing to trade will inevitably lead to a decrease in capital.

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 is QStick?

A.It's a 'momentum meter'. It's like a score that averages the recent hourly price increases and decreases over the past roughly 10 hours.

Q.What does a low win rate mean?

A.It means the percentage of profitable trades out of the total number of trades is low. In this strategy, it means fewer than 2 out of 10 trades were profitable.

Q.What is PF? Is it bad if it's less than 1?

A.It's the number you get by dividing the 'total amount of money made' by the 'total amount of money lost'. If it's less than 1, it means the total amount lost was greater than the total amount made.

Q.Does Max DD mean the biggest loss?

A.It's the 'percentage decrease' from the peak asset value to the subsequent lowest point. It indicates that there was a moment when the asset value dropped by 93%.

Q.What does HODL mean?

A.It's internet slang, a playful variation of the word 'HOLD' in English, meaning to 'hold on forever' without selling or buying.

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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