The transaction fee is the universal odds for high-frequency strategies—A one-year backtest reveals the critical dividing line between life and death

When you are placing orders on an exchange, you might think you’re fighting against the market, but in reality, you’re battling an invisible opponent—called the universal odds—which is another name for transaction fees. A real backtest of high-frequency strategies on popular cryptocurrencies reveals this brutal truth with stark numbers: even if your strategy logic is perfectly sound and market trends align with expectations, a mere 0.04% difference in fee rate can turn a 47% profit into a 13% loss. This is not an exception but the rule.

The Tragedy of Fee Sensitivity: How 0.04% Can Reverse an Entire Strategy

To verify this phenomenon, researchers used an initial capital of $100,000 to backtest the most famous open-source strategy on TradingView, “Squeeze Momentum” (developed by LazyBear), over a full year. This strategy is widely circulated among retail traders for its clear logic and reproducibility, seemingly a “stable profit machine.” But when the researchers simulated three typical fee scenarios—0% (ideal), 0.02% (maker fee), and 0.06% (taker fee)—the results completely overturned common perceptions.

The 15-minute high-frequency interval is the stage where strategy signals are most concentrated, generating 600-800 trades annually. In this timeframe, fees are no longer invisible mosquito bites but decisive executioners.

Data speaks:

For Bitcoin (BTC): In an ideal 0% fee environment, the strategy yields +21.47%. But once trading at a low fee of 0.02%, this positive return instantly flips to -14.45%, with cumulative fees totaling $29,596. If trading on a high-fee exchange with 0.06% taker fees, the account plunges into a -55.94% abyss, with fees consuming $64,193.

For Ethereum (ETH): The outlook seems more optimistic—+68.66% profit at 0% ideal fees, three times higher than BTC. But this advantage is fragile in the face of fees. At 0.02%, the strategy still maintains a substantial +47.34% profit, but once market orders with a 0.06% fee are used, the $76,536 in accumulated fees can push the account into a -13.81% loss.

The significance of this turning point is: your strategy parameters remain unchanged, your technical analysis remains the same, and market trends are as expected, yet just because of different fee structures, the entire account can flip from euphoria to despair. This is why fees are called the “universal odds” of high-frequency strategies—they can almost negate everything and are the primary variable determining success or failure.

The Cycle Paradox: Why Extending Trading Periods Can Deepen Losses

The common advice is “lengthen the cycle, reduce trading frequency, and thus minimize fee erosion.” This logic seems flawless, but the backtest brutally exposes its flaws.

When the same strategy parameters are applied to a 1-hour (1H) timeframe, the situation worsens. Under a 0.06% taker fee, BTC’s performance improves slightly from -55.94% (15-minute) to -37.33%, but ETH’s situation hardly changes, remaining at -34.49%.

More painfully, even with zero fees (0%), BTC at the 1-hour level records a -12.29% loss, and ETH similarly loses -11.51%. This indicates the problem isn’t just fees but that the default parameters (20/2.0) are already invalid at longer cycles.

The core reason is signal lag. When the “compression-release” momentum indicator confirms on a 1-hour chart, the trend has already been underway for some time. Entering trades at this point often means buying at local highs, then facing subsequent pullbacks, ultimately leading to stop-outs. In other words, you’re applying short-cycle-optimized parameters to a long-cycle game, which naturally results in market lessons.

This reveals a profound truth: There are no universal parameters—only context-specific adjustments. Blindly extending the cycle won’t save you; it may instead cause parameter mismatches and losses.

The Mathematical Trap of Fees: Risk-Reward and Compound Erosion

Understanding all this hinges on grasping a mathematical issue: The win rate of the Squeeze Momentum strategy isn’t very high. Backtests show a win rate between 26% and 40%, relying on a favorable risk-reward ratio—small losses are offset by a few big wins.

But fees directly alter this equation. When executing 600-800 trades per year, each trade carries an invisible “entry tax.” For example, with ETH at 15-minute intervals, $76,536 in total fees mean about $92 per trade on average. This effectively adds an invisible “reverse position” to your strategy—regardless of profit or loss, this fee is relentlessly eating away at your account.

In a 100% position mode, this erosion can have a compounding effect. As the principal diminishes repeatedly, not only is the original capital lost, but also the gains it could have generated. Under a 15-minute + 0.06% fee scenario, BTC’s maximum drawdown reaches 58.32%—meaning the account shrinks by more than half at its worst. In real trading, this often leads to liquidation or mental breakdown.

This is why fees are called the “universal odds”: they act impartially on every trade, no matter how clever your strategy, and over time, they can cause you to lose to mathematics.

Four Practical Strategies to Break Through

Once the root issues are understood, solutions become clearer.

First: Fee structure is the foundation of survival
Don’t expect to run such strategies on high-fee exchanges. High-frequency strategies are only feasible if you can reliably obtain maker (limit order) fills. This means implementing passive order logic at the algorithm level rather than market orders. If taker fees exceed 0.05%, the strategy has little chance of survival.

Second: Underlying volatility is the core moat
Bitcoin’s “assetization” has led to decreasing volatility over the years, making high-frequency trading a way to “contribute fees” to the exchange in low-volatility environments. Ethereum and other high-beta altcoins are more suitable for such strategies. On ETH, even with higher fees, the high volatility still offers enough redemptive profit; but on BTC, even at the lowest fees, low volatility makes it hard to cover high-frequency costs.

Third: Introduce trend filtering to reject ineffective oscillations
The original strategy often opens trades in sideways markets, which is a major source of losses. The solution is to incorporate the ADX indicator (setting ADX > 20) to confirm trend strength, or use multi-timeframe resonance: before opening on the 15-minute chart, confirm that the 1-hour or 4-hour chart is in a bullish alignment. This helps avoid repeated erosion in non-trending markets.

Fourth: Dynamic parameter adaptation
The failure at the 1-hour level shows that parameters are not fixed golden rules. For different timeframes, you need to optimize Bollinger Band length (BB Length) and standard deviation multiplier (MultFactor) to reduce signal lag and avoid chasing highs. This requires more detailed backtesting but is the real process of “finding the golden goose.”

Final Thoughts

If there is a “Holy Grail” in quantitative trading, it isn’t some magical indicator or perfect parameter, but extreme control over execution costs and a deep understanding of market microstructure. The logic of the Squeeze Momentum strategy remains valid, but success now depends less on the technical indicator’s efficacy and more on your ability to execute it at the lowest possible fee cost.

This means that for high-frequency traders aiming to profit in crypto markets, optimizing code is just the first step. Optimizing fee tiers, striving for maker executions, and choosing suitable liquidity provision methods often matter more than parameter tuning. In other words, the real key to profitability isn’t how clever your strategy is, but how deeply you understand and manage the “universal odds”—the invisible opponent of fees.