アルゴクラウドポートフォリオの内部で何が起きているのか?
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The Double 7 is a classic mean-reversion strategy developed by Larry Connors. Its purpose is to buy temporary weakness while the market remains in a long-term uptrend.
The strategy uses only three elements:
The strategy opens a long position when both of the following conditions are true:
The order is executed on bar close.
In simple terms, the strategy waits for the market to remain in a long-term uptrend and then buys when it reaches a short-term seven-day low.
The position is closed when the current Close is equal to or higher than the highest Close of the last seven bars, including the current bar.
The exit is also executed on bar close.
The strategy does not use a fixed Stop Loss, Profit Target, or time-based exit. It remains in the trade until the seven-day recovery condition is reached.
The strategy was tested on daily SPY data from January 1993 to September 2026.
Using a starting capital of $10,000 and allocating 100% of the available capital to each trade, the backtest produced:
The 100% allocation was used to examine the raw performance of the trading rule. It should be considered a research setting rather than a recommendation for live position sizing.
A direct comparison with Buy & Hold would be misleading because Buy & Hold remains fully invested and therefore takes substantially more market risk.
For this reason, the benchmark was normalized to produce the same maximum drawdown as the Double 7 strategy. At an equal drawdown of approximately 12.5%, the results were:
Under these specific backtest settings, the Double 7 strategy therefore generated roughly 10× more profit at the same level of historical drawdown.
Short-term declines in equity markets are often driven by fear, headlines, and temporary selling pressure. When the broader trend remains positive, these moves can be followed by a short-term recovery.
The Double 7 strategy attempts to capture this behavior systematically:
The same rules can be tested on other equity indices and ETFs without changing the parameters. However, the strategy may behave differently in markets that are driven by other forces. For example, it performed poorly on long-duration Treasury bonds, where price changes are more directly connected to shifts in interest-rate expectations.
Watch the video to see the whole process of creating:
Backtest results are hypothetical and do not represent actual trading. The published test used zero commission, spread, and slippage. Real trading costs and execution conditions can materially affect performance.
AlgoCloudは最近、多岐にわたる大規模な改良と完全に新しい機能の提供を開始しました。最も重要な追加機能の一つが新しいアナリティクスセクションであり、これにより大幅に明確な…

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