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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

IBMBUSDT

股票工具 · 通过 Binance 执行
IBMB 0 +1588.79% 1TRAD-VKM3
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。
31交易
87.1%胜率
+10.38%平均交易
+44.88%最佳交易
-15.96%最差交易
+259.1%年化
策略分析档案 · 9a4aec42496928a1

IBM · International Business Machines Corporation: 15m Quantitative Strategy Analysis (IBMB 0 +1588.79% 1TRAD-VKM3)

Detailed quantitative evaluation of the IBMB 0 +1588.79% 1TRAD-VKM3 algorithmic strategy on International Business Machines Corporation (IBM). Based on 31 closed trades across 6.93 years of historical stock data, this core strategy achieved an 87.10% win rate, a profit factor of 5.94, and a total gain of +1588.79%.

阅读完整分析

Strategy profile

The IBMB 0 +1588.79% 1TRAD-VKM3 strategy is a quantitative trading algorithm applied to International Business Machines Corporation (IBM) on a 15-minute chart interval. It is classified as a core balanced strategy and holds rank 3 for the IBM ticker within the DevioLab framework, featuring a DevioLab Score of 79.02. The evaluated historical dataset spans 6.93 years from September 16, 2019 to August 19, 2026.

Trading rhythm and position duration

Despite running on a 15-minute candle interval, the strategy operates with extreme selectivity. Over nearly seven years of history, it registered only 31 completed trades, translating to roughly 4.48 trades per year. This execution model is strictly non-scalping and represents a low-frequency position strategy. Specific average holding hours are not detailed in the dataset, but the low trade frequency highlights prolonged filtering between entries.

Quality of historical results

Across the entire backtest, the algorithm delivered a cumulative closed return of +1588.79%, corresponding to an annualized metric of 259.10%. Out of 31 total closed trades, 27 were profitable, establishing a high win rate of 87.10%. The average trade gain stood at +10.38% with a median return of +9.05%, while the single best trade generated +44.88%. The profit factor reached 5.94, and the top 3 winning trades represented 30.99% of total gross profits, reflecting a well-distributed return profile.

Risk, drawdown and losing behavior

Downside exposure remained tightly controlled throughout the sample. The maximum historical drawdown was capped at 15.96%, matching the loss of the single worst trade (-15.96%). Only 4 losing trades occurred across the entire 6.93-year timeline. The strategy sustained a peak winning streak of 17 consecutive trades, whereas its longest losing streak was limited to just 1 trade.

Behavior through time and yearly stability

The sample period spans 6.93 years of stock price movement. Due to the sparse trading frequency, detailed annual breakdowns are omitted from the statistical set. Performance gains were concentrated during distinct market expansions, interspersed with long passive observation phases.

Strengths and limitations

Key strengths include an outstanding 87.10% win rate, a strong 5.94 profit factor, low drawdown (15.96%), and minimal transaction costs due to low trade execution. Primary limitations center on the small sample size of 31 trades over nearly seven years, which reduces statistical certainty and demands high patience during long periods of inactivity.

DevioLab analytical conclusion

With a DevioLab Score of 79.02 and a rank 3 position for IBM, this strategy demonstrates robust historical risk-adjusted efficiency. It is suitable for systematic market participants seeking highly filtered, high-probability trades with low drawdown tolerances. Users must note that small sample backtests do not guarantee future live returns.

Data scope and methodology

This analysis utilizes historical stock market data for International Business Machines Corporation (IBM) from 2019-09-16 to 2026-08-19 on 15m intervals. Figures represent simulated historical closed trades and do not represent live account trading guarantees or investment advice.

完整策略分析