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Reading the Flow of Money! A Cryptocurrency Trading Challenge with "Chaikin Money Flow"

This strategy aims to identify optimal buy and sell points for cryptocurrencies (specifically Ethereum in this case) by tracking "where the money is flowing." We conducted numerous trades on a short, 5-minute interval. Unfortunately, the results were not favorable this time. Let\'s explore why it didn\'t work and what lessons we can learn from this experience!

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

Introduction and Prerequisites

This strategy aims to identify optimal buy and sell points for cryptocurrencies (specifically Ethereum in this case) by tracking "where the money is flowing." We conducted numerous trades on a short, 5-minute interval. Unfortunately, the results were not favorable this time. Let\'s explore why it didn\'t work and what lessons we can learn from this experience!

[Verification] Strategy Backtest Overview

  • Strategy Name: Trend Following Strategy using Chaikin Money Flow
  • Asset: ETH/USDT
  • Timeframe: 5m
  • Period: 2024-10-11 to 2025-08-25 (317 days)
  • Initial Capital: $10,000
  • Fees/Slippage: 0.1% / 0.1%
  • Exchange: bybit

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 ETH/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 Trades2128 trades
Win Rate21.99%
Average Profit1.02%
Average Loss-0.76%
Expectancy-0.37%
Profit Factor0.33
Max Drawdown99.96%
Final Return-99.96%
Sharpe Ratio-0.82
HODL (Buy & Hold)93.83%

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 win rate was very low at approximately 22%, meaning most trades resulted in losses, leading to escalating deficits.
  2. 2The trading frequency was excessively high at 2128 trades. Since the profit per trade was small, the transaction fees resulted in an overall loss.
  3. 3The 'threshold' values and the 'lookback period' used for calculating money flow may not have been suitable for the 5-minute price movements of Ethereum.

3 Lessons Learned from This Result

  1. 1We learned that even strategies that seem promising can produce unexpected results when put to the test.
  2. 2Although a low win rate can be compensated by large wins, this strategy failed to achieve significant gains on winning trades.
  3. 3We also learned the importance of metrics that evaluate overall trading performance, such as 'Expectancy' (average profit per trade) and 'PF' (profit factor). Both metrics were negative in this strategy.

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 CMF calculated?

A.Simply put, it combines 'intraday momentum' and 'volume.' Momentum is determined by whether the day's closing price was high or low within the day's trading range. This is multiplied by the trading volume to quantify the strength of money flow.

Q.What is the purpose of the 'threshold' values?

A.They serve as borderlines for deciding whether to 'buy' or 'sell.' If the CMF value strongly surpasses the positive threshold (0.05 in this case), it's considered a potential 'buy signal.' Conversely, if it strongly falls below the negative threshold (-0.05), it's seen as a potential 'sell signal.'

Q.Can you explain 'Expectancy' and 'PF' more simply?

A.'Expectancy' is the predicted profit per trade on average. A negative expectancy suggests that the more you trade, the higher the likelihood of incurring losses. 'PF' is the ratio of total profits from winning trades to total losses from losing trades. A PF greater than 1 indicates overall profitability, but here it was significantly less than 1, showing that losses heavily outweighed gains.

Q.What is HODL?

A.HODL is a cryptocurrency term for holding onto an asset without selling it for an extended period. In this experiment, simply holding (HODL) would have yielded approximately a 94% profit, while this strategy resulted in a nearly 100% loss. This means doing nothing would have been far more profitable.

Q.So, is this strategy unusable now?

A.Using this strategy with its current settings is highly risky and not recommended. However, by learning from this failure, it's possible to transform it into a better strategy by adjusting settings, combining it with other indicators, or implementing strict rules for risk management. If you choose to test it, please do so cautiously with an amount of money 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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