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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

SPCXBUSDT

股票工具 · 通过 Binance 执行
SPCXB 0 +70.29% 1TRAD-SZB6
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。
2交易
100.0%胜率
+29.29%平均交易
+38.57%最佳交易
+20.00%最差交易
+65.8%年化
策略分析档案 · 235ce87096806ef7

Quantitative Strategy Analysis for SPCX · Space Exploration Technologies Corp. on 15m Timeframe

An analytical study of an algorithmic trading model for SPCX in the stock market. The strategy achieved a 100% win rate and 70.29% total gain over a short evaluation window of 2 trades.

阅读完整分析

Strategy profile

This algorithmic trading strategy is engineered for Space Exploration Technologies Corp. under the ticker SPCX within the stock market on a 15-minute chart interval. In the current analytical evaluation, the strategy holds rank 1 for this ticker, selected via a minimum ticker fallback, with a DevioLab Score of 0. It is not designated as a core strategy model. Over the monitored dataset history of approximately 0.18 years, the algorithm generated a cumulative return of 70.29%, matching its annualized calculation of 70.29%.

Trading rhythm and position duration

Operating on a 15-minute execution timeframe, the strategy exhibits an extremely selective trade generation rhythm. Across the recorded history, the model completed only 2 trades, translating to an annualized pace of roughly 11.37 trades per year. Detailed metric tracking for average or median holding duration in hours, as well as days between trade exits, is unavailable in the dataset. This low trade count indicates that the entry criteria trigger only during specific, infrequent market states.

Quality of historical results

In terms of trading performance, the strategy holds a 100% win rate across its 2 completed trades, incurring zero losing trades. The average return per trade stands at 30.91%, while the median trade gain reached 38.57%. The best trade achieved a 38.57% profit, and the lowest trade return recorded a positive 23.26%. The recorded profit factor is 5, with the total cumulative gain of 70.29% entirely generated by these two successful executions.

Risk, drawdown and losing behavior

The maximum drawdown recorded across the dataset is 0%, directly resulting from the absence of closed losing positions. The longest winning streak is 2 trades, while the losing streak remains at 0. The top winning trades account for 100% of the gross profits. While a zero drawdown figure is optimal in backtest metrics, it must be interpreted cautiously due to the sample size of just 2 trades, which provides limited exposure to adverse market conditions.

Behavior through time and yearly stability

The evaluated dataset covers a brief span from June 12, 2026, to August 15, 2026, representing approximately two months or 0.18 years. Owing to this constrained window, no yearly performance breakdown is available. The algorithm demonstrated strong efficiency during this specific mid-2026 window, but its multi-year consistency and adaptability across varying market regimes remain unproven.

Strengths and limitations

Key strengths include pristine historical accuracy with a 100% win rate, a maximum drawdown of 0%, a high profit factor of 5, and a solid cumulative return of 70.29%. The primary limitations stem from the statistical sample size of only 2 trades over 0.18 years, unrecorded holding duration metrics, and a lack of testing across broader equity market environments.

DevioLab analytical conclusion

Holding rank 1 for SPCX via fallback selection with a DevioLab Score of 0, this strategy demonstrates outstanding short-term execution metrics. However, with only 2 closed trades recorded, statistical significance is minimal. Quantitative analysis suggests observing the model over an extended period to confirm statistical stability before drawing definitive conclusions.

Data scope and methodology

This analysis is based on simulated historical closed trade results for Space Exploration Technologies Corp. on 15-minute price bars from June 12, 2026, to August 15, 2026. All reported performance figures describe historical backtested trades and do not constitute investment advice or guarantees of future execution results.

完整策略分析