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公开目录 · DEVIOLAB

策略目录

探索算法策略、比较表现,并查看每个模型的完整历史。
不知道从哪里开始?
1. 目录 2. 选择 Core 1 3. 加入篮子 4. API 密钥 5. 激活交易机器人
我们的建议: 首次配置建议使用 Core 1——这是 DevioLab 的主要、更稳健的筛选。Core 2 适合明确愿意承受更高风险和更深回撤的用户。
i
什么是 Core 1 和 Core 2?
DevioLab 会分析加密货币和股票市场策略,并整理成可直接使用的精选组合,让您无需手动筛选数百种方案。
★ Core 1
由 DevioLab 筛选的加密货币与股票策略组合,作为主要、更均衡且更稳健的起步选择,重点控制风险与回撤。
◆ Core 2
DevioLab 的独立进取型精选,适合明确愿意承受更高风险和更深回撤,以换取潜在更高收益的用户。
AAOI · Applied Optoelectronics, Inc. (5) AAPL · Apple Inc. (7) ALAB · Astera Labs, Inc. (6) AMAT · Applied Materials, Inc. (6) AMD · Advanced Micro Devices, Inc. (7) AMZN · Amazon.com, Inc. (6) ARM · Arm Holdings plc (5) ASML · ASML Holding N.V. (7) ASTS · AST SpaceMobile, Inc. (6) AVGO · Broadcom Inc. (6) BABA · Alibaba Group Holding Limited (6) BE · Bloom Energy Corporation (5) BMNR · BitMine Immersion Technologies, Inc. (5) COHR · Coherent Corp. (5) CRCL · Circle Internet Group, Inc. (5) CRDO · Credo Technology Group Holding Ltd (8) DELL · Dell Technologies Inc. (6) EWY · iShares MSCI South Korea ETF (6) FLNC · Fluence Energy, Inc. (7) GS · The Goldman Sachs Group, Inc. (6) HOOD · Robinhood Markets, Inc. (7) IBM · International Business Machines Corporation (6) INTC · Intel Corporation (6) IREN · IREN Limited (7) LITE · Lumentum Holdings Inc. (6) META · Meta Platforms, Inc. (7) MRVL · Marvell Technology, Inc. (6) MSFT · Microsoft Corporation (7) MSTR · Strategy Inc (8) MU · Micron Technology, Inc. (7) NFLX · Netflix, Inc. (7) NOK · Nokia Oyj (7) NVDA · NVIDIA Corporation (7) PLTR · Palantir Technologies Inc. (8) PYPL · PayPal Holdings, Inc. (5) QQQ · Invesco QQQ Trust (6) RKLB · Rocket Lab Corporation (4) SKHY · SK hynix Inc. (3) SMCI · Super Micro Computer, Inc. (5) SMH · VanEck Semiconductor ETF (4) SNDK · Sandisk Corporation (4) SOXS · Direxion Daily Semiconductor Bear 3X Shares (3) SPCX · Space Exploration Technologies Corp. (3) TSLA · Tesla, Inc. (6) TSM · Taiwan Semiconductor Manufacturing Company Limited (4) USAR · USA Rare Earth, Inc. (4)
所选策略概览

AMATBUSDT

股票工具 · 通过 Binance 执行
AMATB 0 +20992.60% 1TRAD-FGJ9
DevioLab 推荐 · Core 1 iDevioLab 主要推荐:更加防御、平稳且稳定的配置,重点控制风险和回撤。
DevioLab 推荐 · Core 2 iDevioLab 更激进的推荐:接受更高风险和更深回撤,以换取潜在更高回报。
93交易
77.4%胜率
+6.51%平均交易
+59.91%最佳交易
-12.95%最差交易
+657.5%年化
策略分析档案 · 78b62cf2f3205cda

Algorithmic Strategy Analysis AMAT · Applied Materials, Inc.

Quantitative breakdown of the AMATB 0 +20992.60% 1TRAD-FGJ9 trading model for Applied Materials, Inc., ranked 1st for the ticker with a DevioLab Score of 80.23.

阅读完整分析

Strategy profile

This quantitative trading model was engineered for trading Applied Materials, Inc. (ticker AMAT) using a 15-minute chart interval. Within the evaluation framework of DevioLab, it holds the 1st position among all tested strategy configurations for this specific asset, achieving a top score of 80.23 and earning the core2_independent_best classification. The algorithm is built to identify high-probability momentum setups while implementing strict quality control filters. Across the historical evaluation span, the strategy delivered a compelling balance between statistical precision and drawdown control, outperforming alternative approaches for this asset.

Trading rhythm and position duration

Although the algorithm executes on a 15-minute timeframe, its operational rhythm is highly selective rather than high-frequency or scalping-oriented. Over a total historical observation period of approximately 6.92 years, the strategy completed exactly 93 trades. This translates to an average trading frequency of 13.43 trades per year, reflecting a patient approach that ignores noise in favor of high-conviction market structures. While average and median holding hours are not explicitly recorded in the raw dataset, the sparse trade frequency strongly indicates a swing or positional holding period rather than rapid intra-day turnover.

Quality of historical results

The strategy recorded outstanding historical backtest performance. Across the full 6.92-year sample, cumulative return reached 20,992.60%, corresponding to an annualized yield of 693.19%. Out of 93 completed trades, 72 were profitable and 21 were loss-making, yielding an impressive win rate of 77.42%. The system achieved a profit factor of 1.88, demonstrating that total gains consistently outweighed total gross losses. The average gain per trade was 6.56%, while the median trade return stood at 4.85%. The single best trade delivered a 59.91% gain. Furthermore, the top three winning trades accounted for only 18.33% of total gross profit, confirming that the overall return was driven by consistent edge rather than a few lucky outliers.

Risk, drawdown and losing behavior

Risk statistics reveal a disciplined drawdown profile given the massive cumulative gain. The maximum historical equity drawdown was capped at 18.52%, representing an unusually controlled risk profile for a strategy exceeding 20,000% cumulative gain. The worst single trade generated a loss of -12.95%, demonstrating effective downside limitation during adverse market movements. Streak analysis further underscores system stability: the longest winning streak reached 18 consecutive positive trades, whereas the longest losing streak was limited to just 4 consecutive losses. This asymmetry helps preserve capital and minimize drawdown duration.

Behavior through time and yearly stability

Evaluating temporal stability relies on the aggregated metrics collected across the 6.92-year dataset. While individual yearly breakdowns are not available in this dataset, the high overall win rate of 77.42% and the robust 18-trade winning streak suggest consistent adaptability to the price dynamics of Applied Materials, Inc. over time. However, traders analyzing this historical footprint should acknowledge that without year-by-year distribution tables, performance consistency across distinct macroeconomic regimes cannot be broken down annually.

Strengths and limitations

The primary strengths of this model include a high historical win rate of 77.42%, a low maximum drawdown of 18.52%, a solid profit factor of 1.88, and a healthy profit distribution where the top three trades represent only 18.33% of gross profit. Limitations stem from the relatively small sample size of 93 trades over 6.92 years, the absence of closed positions since June 2024, and missing holding period metrics in the dataset. The low trade frequency requires significant patience during extended inactive periods.

DevioLab analytical conclusion

This algorithmic strategy for Applied Materials, Inc. represents a top-tier quantitative model, holding 1st place for the ticker with a DevioLab Score of 80.23. Its combination of strong risk control with an 18.52% peak drawdown and exceptional trade accuracy makes it a standout benchmark in our stock strategy library. However, the operational pause observed since mid-2024 highlights its highly conservative filtering logic. This document serves purely research and educational purposes and does not constitute financial advice.

Data scope and methodology

This research is based on historical simulated closed trades spanning from September 16, 2019 to August 18, 2026, covering a total duration of 6.92 years on a 15-minute chart interval. All reported returns, drawdowns, and trade metrics reflect simulated past performance on historical data, do not represent actual Binance account trading returns, and offer no guarantee of future results.

完整策略分析