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Can Chart Lines Predict the Future? I Tried the "Keltner Channel" Strategy!

I experimented with applying the "Keltner Channel" strategy to cryptocurrency trading, specifically using the "ETH/USDT" pair. I\'ll explain the results of a computer simulation of trading with this strategy over approximately one year. The outcome was quite surprising.

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

Introduction and Prerequisites

I experimented with applying the "Keltner Channel" strategy to cryptocurrency trading, specifically using the "ETH/USDT" pair. I\'ll explain the results of a computer simulation of trading with this strategy over approximately one year. The outcome was quite surprising.

[Verification] Strategy Backtest Overview

  • Strategy Name: Trend Following Strategy using Keltner Channel
  • Asset Pair: ETH/USDT
  • Timeframe: 5m
  • Period: 2024-07-21 to 2025-08-25 (399 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 Trades684 trades
Win Rate27.92%
Average Profit2.15%
Average Loss-1.15%
Expectancy-0.23%
Profit Factor0.64
Max Drawdown84.89%
Final Return-82.4%
Sharpe Ratio-0.15
HODL (Buy & Hold)34.47%

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 for this strategy was around 28%, which is quite low. This is likely due to many 'false signals' where the price seemed to break a line only to reverse sharply in the opposite direction.
  2. 2The Profit Factor, a measure of performance, was 0.64, which is less than 1. This means that more money was lost in losing trades than was gained in winning trades.
  3. 3The maximum drawdown, representing the largest drop in capital, reached approximately 85%. This indicates a significant risk of substantial capital loss when using this strategy.

3 Lessons Learned from This Result

  1. 1I learned that simply trading based on the price crossing a line can easily lead to losses due to falling for false signals.
  2. 2Even with a low win rate, it's possible to achieve overall profitability if each winning trade yields a very large profit. However, this strategy struggled to achieve that.
  3. 3Frequent trading doesn't guarantee profits if the rules aren't effective. In fact, transaction fees incurred with each trade can lead to capital depletion.

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.Is Keltner Channel the name of a character?

A.No, it's not a character's name. It's a tool used to analyze price charts to help predict future price movements.

Q.What does ETH/USDT mean?

A.'ETH' refers to the cryptocurrency Ethereum, and 'USDT' refers to Tether. 'ETH/USDT' represents the price of one Ethereum in terms of Tether.

Q.Does a low win rate mean you can't win at all?

A.Not necessarily. The win rate is the percentage of trades that were profitable. Even with a low win rate, if the profit from each winning trade is significant enough, the overall result can still be positive.

Q.Is Max Drawdown the largest amount lost?

A.Yes, that's generally correct. More precisely, it's the percentage drop from the peak equity to the subsequent trough. A higher number indicates a greater risk of significant losses with the strategy.

Q.What does HODL mean?

A.'HODL' is an internet slang term meaning to hold onto a cryptocurrency or other asset without selling, regardless of market fluctuations. In this experiment, simply holding the asset performed better than using this strategy.

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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