GLMUSDT
Crypto market · BinanceGLM Strategy Analysis: High-Win-Rate Swing Model Demonstrates Exceptional Profit Factor and Organic Return Distribution
This quantitative analytical report examines the top-ranked algorithmic trading model for GLM on the 15-minute timeframe, designated as GLM 215000 +166550.11% 1TRAD-TVC9. Achieving a DevioLab score of 82.81 and ranking first for GLM, the strategy has generated a cumulative historical return sum of 166,550.11% across a 3.46-year evaluation window from March 17, 2023, to August 29, 2026. Built on a sample of 200 completed trades, the strategy delivers an 84.00% win rate paired with a profit factor of 5.00. Key empirical features include a multi-day holding profile with a median duration of 48.13 hours, a remarkably broad profit distribution where the top three winning trades contribute only 8.14% of gross profits, and a maximum drawdown capped at 29.60%. The algorithm maintains a disciplined trading rhythm, averaging 4.76 trades per active month. Recent window data since June 1, 2024, underscores ongoing operational activity, accounting for 128 trades and a combined return sum of 490.33%.
Analysis as of 29.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The GLM 215000 +166550.11% 1TRAD-TVC9 strategy represents a core systematic quantitative model developed for GLM within the cryptocurrency market. Evaluated across a dataset spanning 3.46 years from March 17, 2023, to August 29, 2026, the strategy holds the number one ranking for GLM on DevioLab with an overall score of 82.81. Operating on a 15-minute price interval, the system utilizes lower-timeframe candle data to time entries while managing positions over broader market swings. Over its historical backtest period, the model completed 200 trades, comprised of 168 winning trades and 32 losing trades, establishing a high baseline win rate of 84.00%. The strategy achieved an aggregate closed-trade return sum of 166,550.11%, which translates to an annualized return metric of 7,981.52%. A defining characteristic of the profile is its profit factor of 5.00, meaning gross gains exceeded gross losses by five times over the multi-year sample. This mathematical symmetry highlights a strategy capable of sustaining robust expectancy across changing market conditions in GLM.
Trading rhythm and position duration
Despite relying on a 15-minute chart for price granularity, the strategy operates as a medium-term swing model rather than a high-frequency scalp system. Over 3.46 years, the strategy executed 200 completed trades, reflecting a selective pace of 57.89 trades per year and an average of 4.76 trades per active month. The model avoids frequent portfolio turnover, taking exposure only when strict signal criteria are satisfied. The duration statistics confirm a multi-day holding structure. The average holding duration per trade is 76.97 hours (approximately 3.2 days), while the median holding duration is 48.13 hours (approximately 2.0 days). This indicates that while entries are determined on 15-minute bars, trades are given ample time to develop across multiple daily cycles. Exits are similarly spaced out, with an average of 6.31 days between trade exits and a median of 4.83 days. This rhythmic distribution allows the strategy to capture meaningful price trends in GLM without over-exposing capital to short-term market noise.
Quality of historical results
The historical return profile of this strategy displays strong mathematical balance and healthy profit distribution. The average return per trade across all 200 trades stands at +4.14%, closely aligning with the median trade return of +4.42%. The close alignment between average and median metrics confirms that overall profitability is driven by consistent execution rather than distorted by extreme positive outliers. A key quantitative highlight is the concentration metric: the top three winning trades account for only 8.14% of total gross profit. In many trading strategies, net profits are heavily concentrated in two or three exceptional trades, creating structural fragility if those rare events are missed. In this system, gross profit is distributed broadly across its 168 winning trades. The strategy's best individual trade registered a gain of +30.29%, while its worst trade registered a loss of -29.03%. Combined with a profit factor of 5.00, these figures demonstrate an organic, broad-based payoff structure.
Risk, drawdown and losing behavior
Risk statistics for the strategy demonstrate significant drawdown containment, primarily driven by high hit-rate frequency and short losing sequences. The maximum recorded historical drawdown across the 3.46-year history was 29.60%. Notably, this drawdown level closely matches the worst single trade loss of -29.03%, suggesting that historical drawdowns were largely triggered by single adverse price movements or short-lived volatility shocks rather than systemic operational breakdown. The strategy exhibits strong resilience during adverse periods, as reflected in its streak metrics. The maximum consecutive losing streak across 200 trades was limited to just 3 trades. In contrast, the longest consecutive winning streak reached 26 trades. Because 84.00% of trades are profitable, losing trades rarely cluster together, preventing deep compounding drawdowns. However, the worst single trade loss of -29.03% serves as an important reminder that position risk must be respected, as single-trade adverse moves represent the strategy's primary source of historical peak-to-trough decline.
Behavior through time and yearly stability
An analysis of yearly breakdown statistics reveals consistent performance across multiple calendar years. In 2023, the strategy executed 45 trades with an 84.44% win rate (38 wins, 7 losses), generating a sum return of +187.77%. The model expanded its yield in 2024, its most productive calendar year, closing 65 trades with an 89.23% win rate (58 wins, 7 losses) and a combined sum return of +323.34%. Operational consistency remained high into 2025, where the system logged 64 trades with an 82.81% win rate (53 wins, 11 losses) and a total trade sum of +297.10%. In the partial year of 2026 (through August 29, 2026), activity moderated, yielding 26 trades with 19 wins and 7 losses (73.08% win rate) and a return sum of +20.71%. Annual trade frequency remained remarkably steady during full operating years (ranging between 45 and 65 trades), confirming that the algorithmic signal generator maintains continuous structural relevance across different market phases.
Recent period since 2024-06-01 versus full history
Evaluating performance in the recent period from June 1, 2024, to August 29, 2026, provides insight into how the system has adapted to contemporary market conditions. Of the 200 total completed trades in the history, 128 trades occurred within this recent window, representing 64.00% of the entire dataset. The accumulated closed-trade return sum during this recent period reached +490.33%. Comparing the recent window to the full history demonstrates robust operational continuity. Executing 128 trades over roughly 27 months aligns closely with the long-term rhythm of approximately 4.76 trades per month. While the moderation in 2026 trade returns reduced overall acceleration slightly, the accumulated sum of +490.33% since June 2024 confirms that the strategy's systematic edge remains active and heavily tested in recent market environments.
Strengths and limitations
The primary strength of the strategy lies in its combination of a high win rate (84.00%) and a high profit factor (5.00). Its gain structure is exceptionally broad, with the top three winning trades generating only 8.14% of gross profits, proving that performance does not depend on lucky outliers. Furthermore, a measured trade pace (57.89 trades per year) and short losing streaks (maximum of 3 trades) provide structural stability and reduce the likelihood of sustained equity drawdowns. Conversely, the strategy's primary limitation is its single-trade tail risk. The worst trade loss of -29.03% and maximum drawdown of 29.60% reveal that when losses occur, they can be sizeable relative to average trade gains (+4.14%). Additionally, as seen in the 2026 data where the win rate slowed to 73.08% and the sum return reached +20.71%, strategy performance experiences periodic consolidation phases when market volatility or regime conditions change.
DevioLab analytical conclusion
Earning a DevioLab score of 82.81 and ranking first for GLM, this core quantitative strategy represents a highly disciplined approach to swing trading GLM. By combining lower-timeframe entry resolution with multi-day position holding (median 48.13 hours), the model effectively captures macro price moves while maintaining a high 84.00% hit rate. Statistical evidence confirms that the strategy's 166,550.11% historical return sum is built upon consistent execution across 200 trades rather than extreme profit concentration. While traders must remain mindful of the strategy's maximum drawdown of 29.60% and potential single-trade risk, the quantitative profile demonstrates robust multi-year reliability.
Data scope and methodology
The figures analyzed in this report are based on simulated historical closed trades for GLM on a 15-minute candle timeframe between March 17, 2023, and August 29, 2026. The full historical dataset comprises 200 completed trade events generated across a 3.46-year backtesting window. Metrics for the recent period reflect closed trades with sell timestamps on or after June 1, 2024 UTC. All reported return percentages, win rates, and profit factors are mathematical aggregations of backtested trade results designed for strategy benchmarking on DevioLab. These statistics do not reflect actual live account trading on Binance or any other exchange and do not account for transaction fees, execution slippage, or market order book depth. Historical backtest performance is provided for educational and analytical purposes only and does not guarantee future operational results.