Loading US stock market status…
➜
HOW TO START
Complete the full system setup in just a few minutes.
Open Quick Start Guide →
Public catalog · DEVIOLAB

STRATEGY CATALOG

Explore algorithmic strategies, compare performance and open the complete history of every model.
Each algorithmic strategy corresponds to a real trading agent (Trading Bot) created by the DevioLab team from historical-data testing. It can be used for automated trading through supported exchange APIs — Binance, OKX, Bybit, Gate.io, KuCoin and others as they are enabled — or as a Signal Bot with Telegram and email notifications.
2 · Ticker
⌕
FILTERS
Every selected condition works together
Selected strategy overview

ARBUSDT

Crypto market · Binance
ARB 215000 +8836.62% 1TRAD-NVM5
Recommended by DevioLab · Core 2 iMore aggressive DevioLab recommendation: accepts higher risk and deeper drawdowns in exchange for potentially higher returns.
112Trades
77.7%Win rate
+4.59%Avg trade
+35.15%Best trade
-17.92%Worst trade
+1,119.7%Annualized
Strategy analytical profile · 64d03e65034eb483

ARB · ARB Quantitative Strategy Analysis: 77.68% Win Rate and 6.25 Profit Factor on 15m Interval

A detailed quantitative evaluation of the algorithmic strategy for the ARB cryptocurrency asset. The model ranks second for this ticker, delivering an 8836.62% historical net return, a 77.68% win rate, and a controlled drawdown of 19.90%.

Analysis as of 14.08.2026. Strategy data is updated separately.
Read full analysis

Strategy profile

This algorithmic trading model is designed for the ARB cryptocurrency asset operating on a 15-minute chart timeframe. Based on comprehensive statistical evaluation, the strategy achieved a DevioLab Score of 74.14, ranking second among strategies for this specific asset. Over the analyzed history, it generated a cumulative total profit of 8836.62% across 112 completed trades. While not designated as a core strategy, its overall performance metrics represent a high level of historical trade efficiency.

Trading rhythm and position duration

Operating on the 15-minute timeframe, the strategy captures price dynamics on short intervals. Across the recorded history, a total of 112 closed trades were executed. The dataset does not include exact average or median position holding durations in hours, nor does it provide trades per active month or year. Consequently, performance is evaluated strictly on the total sample size of 112 closed trades, which constitutes a concise dataset requiring careful statistical consideration.

Quality of historical results

The algorithm displays strong trade distribution characteristics. Out of 112 completed positions, 87 were profitable and 25 closed at a loss, yielding a win rate of 77.68%. The profit factor stands at an impressive 6.25. The average return per trade reached 4.59%, closely aligned with the median trade return of 4.83%. The single best trade yielded a 35.15% gain. Notably, the top 3 winning trades generated only 12.27% of total gross profit, demonstrating that returns are well dispersed across many trades rather than dependent on rare price spikes.

Risk, drawdown and losing behavior

Risk parameters remain tightly bounded throughout the backtest. The maximum historical drawdown was kept to 19.90%, which is remarkably conservative for crypto volatility. The worst individual trade resulted in a loss of -17.92%. The strategy achieved a maximum winning streak of 10 consecutive trades, while its longest losing streak was limited to just 3 trades. This ratio highlights the strategy's resilience against prolonged loss sequences.

Behavior through time and yearly stability

Granular yearly breakdown data and exact history start timestamps are omitted in this dataset. Standardized metrics show an annualized return figure of 1119.70%. Because calendar-by-calendar breakdown figures are unavailable, individual yearly consistency cannot be explicitly verified, representing a limitation in historical granularity that users must consider.

Strengths and limitations

Key strengths include a strong profit factor of 6.25, a high win rate of 77.68%, low gross profit concentration in top winners (12.27%), and a short maximum losing streak of 3 trades. Limitations include the small total sample size of 112 trades, zero trading activity since June 2024, non-core status, and the absence of granular duration and annual breakdown statistics.

DevioLab analytical conclusion

From DevioLab's analytical perspective, this ARB strategy on the 15m timeframe exhibits high precision and robust risk metrics, earning a DevioLab Score of 74.14 and rank 2 for the ticker. The combination of a high win rate and moderate drawdown makes it a compelling historical model. However, the lack of recent trade triggers and limited overall sample size require prudent forward testing before practical implementation.

Data scope and methodology

This analysis relies on historical simulated data comprising 112 closed spot-style trades up to 2026-08-10 on the 15m ARB chart. Percentages describe historical simulated closed trades; they do not represent exact exchange account returns and do not guarantee future performance.

Full strategy analysis