AKAM · Akamai Technologies Inc.
Stock Universe · global stock marketAKAM · Quantitative Analysis of a High-Precision Multi-Week Horizon Strategy
This empirical quantitative analysis evaluates an algorithmic trading strategy engineered for AKAM over nearly seven years of historical backtested data from September 2019 through September 2026. Configured on a 15-minute chart interval, the system demonstrates an atypical structural profile: despite operating on granular intraday price data, positions remain open for multi-week durations, resulting in an average holding time of 423.35 hours (17.6 days) and a selective execution rate of 10.60 trades per year. The historical performance exhibits an 85.14% win rate across 74 completed trades, achieving a cumulative simulated gain of +2671.35% and a profit factor of 3.75, while restricting peak-to-trough maximum drawdown to 9.41%. Furthermore, profit distribution is remarkably well-dispersed, with the top three winning trades contributing only 18.39% of total gross profit. Designed for deployment within automated trading bots and algorithmic trading agents, this strategy presents a disciplined, low-frequency framework that balances patience with robust payoff mechanics across changing market regimes.
Analysis as of 13.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy for AKAM operates on a 15-minute price aggregation interval, evaluated across a historical dataset spanning from September 16, 2019 to September 9, 2026, representing approximately 6.98 years of market activity. Over this complete multi-year history, the strategy registered 74 completed trades, achieving 63 winning exits and 11 losing exits. This yields a high baseline win rate of 85.14%. The cumulative historical closed trade return totals +2671.35%, which translates to a stylized annualized return metric of 245.03%. Within DevioLab's relative evaluation framework for AKAM strategies, this model secures a DevioLab Score of 79.01 and ranks 2nd among tested architectures for this equity ticker. An essential characteristic revealed by the strategy profile is the interplay between high execution precision and low total transaction frequency. Achieving a profit factor of 3.75 alongside an 85.14% win rate implies that gross historical profits exceeded gross losses by nearly four times. Rather than relying on high-frequency turnover, the decision logic processes 15-minute candle formations to identify selective, long-duration entry windows. When deployed in live execution environments via automated trading bots, trading robots, or quantitative trading agents, such a setup reduces structural exposure to constant intraday noise, focusing instead on high-conviction structural shifts in AKAM price action.
Trading rhythm and position duration
Although the underlying data structure relies on a 15-minute chart interval, the strategy's operational cadence is fundamentally multi-week rather than intraday. Across the 74 historical trades, the average holding duration stands at 423.35 hours, equivalent to roughly 17.6 calendar days. The median holding duration of 263.88 hours (approximately 11.0 days) confirms that even shorter-lived trades typically bridge multiple trading weeks. This discrepancy between the input timeframe (15m) and the holding duration highlights an algorithmic model that uses fine-grained intraday price action for precision timing while managing risk and exits on macro trend horizons. The temporal separation between completed trades further emphasizes this deliberate rhythm. The average time elapsed between trade exits is 34.07 days, while the median interval between exits is 24.04 days. On an annualized basis, the strategy generates approximately 10.60 completed trades per year, or 1.37 trades per active month. Consequently, an automated trading agent running this logic spends considerable periods idle in cash, waiting for specific multi-factor technical setups to materialize. This lower trade frequency avoids over-trading during noisy, range-bound market environments, aligning position exposure with high-probability trend cycles.
Quality of historical results
Evaluating the structural quality of returns requires examining the mathematical distribution of trade outcomes rather than total profit alone. Across 74 trades, the average return per trade reached +4.97%, while the median trade yield stood at +3.85%. The positive delta between average and median trade yields points to a right-skewed profit distribution, where occasional outsized winners uplift the net return without skewing the core baseline experience. The single best historical trade yielded +29.62%, whereas the single worst closed trade sustained a loss of -8.01%. A common vulnerability in quantitative strategies with high cumulative returns is an over-reliance on a tiny handful of explosive outlier trades. In this strategy, however, the three largest winning trades account for just 18.39% of total gross historical profit. This relatively low concentration ratio indicates that historical earnings were widely distributed across numerous winning cycles rather than dependent on unpredictable tail events. Coupled with a profit factor of 3.75, the payoff structure demonstrates consistent profit generation across varying market phases, confirming that the high win rate is supported by healthy upside captures relative to realized losses.
Risk, drawdown and losing behavior
Risk management metrics reveal controlled downside exposure during the 6.98-year backtest period. The maximum peak-to-trough drawdown recorded by the strategy was 9.41%. In the context of a total net strategy accumulation of +2671.35%, maintaining single-digit maximum equity decay suggests effective capital protection mechanisms. The worst single trade loss of -8.01% closely tracks the maximum overall drawdown, implying that equity drawdowns were primarily driven by individual trade stop-outs rather than compounding sequences of multiple severe losses. This drawdown structure is further reinforced by the streak metrics. The strategy's longest historical winning streak reached 21 consecutive profitable trades, whereas its longest losing streak was limited to just 2 consecutive trades. Experiencing no more than two back-to-back losses over nearly seven years of history demonstrates strong regime resilience. For operators deploying trading robots or automated bots, this behavioral stability minimizes the psychological and financial friction typically associated with prolonged drawdowns, though future performance may experience different clustering dynamics.
Behavior through time and yearly stability
An examination of the yearly performance breakdown provides valuable insight into the strategy's consistency across distinct equity market environments. In 2019, during the strategy's brief initial recording period of 0.29 years, only 1 trade was executed, resulting in a loss of -1.31%. Performance accelerated markedly in 2020, registering 17 completed trades with 16 wins (94.12% win rate) and a combined return of +92.61%. In 2021, activity moderated to 6 trades, all of which were profitable (100% win rate), generating +35.83% net yield. During the broader equity market headwinds of 2022, the strategy logged 12 trades, securing 10 wins (83.33% win rate) and +34.70% sum profit, demonstrating solid defensiveness. Subsequent years maintained positive momentum: 2023 yielded +64.13% across 6 trades (100% win rate), 2024 produced +24.13% across 6 trades (100% win rate), 2025 recorded 12 trades with 9 wins (75.00% win rate) and +36.81% sum yield, and 2026 registered 14 trades with 10 wins (71.43% win rate) and +80.87% net yield. Aside from the single trade recorded in late 2019, every full calendar year generated positive net closed-trade returns, displaying multi-year operational stability.
Recent period since 2024-06-01 versus full history
Isolating trade activity from June 1, 2024 onward allows for an evaluation of recent strategy performance under contemporary market conditions. Out of the 74 total historical trades, 30 trades were closed within this recent window. These recent trades produced a aggregate closed yield sum of +138.05%. This indicates an increase in trade execution frequency during the most recent phase, with 30 of 74 lifetime trades (40.5% of total volume) occurring over the final ~2.25 years of the sample. Comparing recent output with full-history baseline characteristics shows sustained efficacy. While earlier years (such as 2021-2024) featured highly sparse trade generation (6 trades per year), the post-June 2024 period saw higher engagement while preserving net profitability. Generating +138.05% across 30 trades aligns comfortably with the historical average yield of +4.97% per trade. This confirms that the decision parameters have remained effective through recent structural market shifts rather than deteriorating post-backtest development.
Strengths and limitations
The primary analytical strength of this AKAM quantitative strategy lies in its combination of high win rate (85.14%), robust profit factor (3.75%), and disciplined downside containment (9.41% maximum drawdown). The low concentration of gross profits among top winners (18.39%) confirms that results are broad-based rather than dependent on single lucky events. Furthermore, the low trade frequency (10.60 trades per year) makes the strategy highly practical for execution via automated trading agents or trading bots, as low turnover naturally minimizes operational overhead and transaction fatigue. Conversely, important limitations must be noted. With only 74 completed trades over 6.98 years, the statistical sample size is relatively compact. While multi-year consistency is evident across annual sub-periods, small-sample metrics inherit wider statistical confidence intervals than high-frequency models. Additionally, the average holding duration of 17.6 days exposes active positions to overnight gap risk and quarterly earnings announcements in AKAM stock, which cannot be smoothed out intraday once a position is open.
DevioLab analytical conclusion
In summary, the analyzed algorithm represents a high-conviction, low-turnover trading system tailored for AKAM. Securing a DevioLab Score of 79.01 and ranking 2nd for this asset, the model effectively bridges intraday parameter monitoring on a 15-minute timeframe with multi-week swing positioning. Its empirical track record demonstrates an exceptional 85.14% win rate, a 3.75 profit factor, and a tight 9.41% maximum historical drawdown across nearly seven years of backtested data. For quantitative traders and operators seeking logic suitable for automated trading bots, trading robots, or algorithmic execution agents, this strategy offers an appealing blueprint characterized by high selectivity and disciplined risk control. However, prospective deployment requires ongoing risk management, as historical multi-week swing metrics do not eliminate market gap risks or guarantee identical future return distributions under evolving market conditions.
Data scope and methodology
The statistics analyzed in this article represent historical backtested performance derived from simulated closed trades for AKAM between September 16, 2019 and September 9, 2026. The evaluation utilizes 15-minute price bar data to execute decision logic, with trade statistics compiled exclusively upon position exit. The recent evidence window covering trades closed on or after June 1, 2024 contains 30 completed trades totaling +138.05% in combined trade percentages. All return figures, profit factors, drawdowns, and holding durations reported herein reflect mathematical calculations on historical price series and do not account for slippage, broker commissions, borrow costs, or execution latency. This analysis is provided strictly for educational and historical research purposes by DevioLab.com and does not constitute investment advice, financial planning, or a solicitation to trade securities.