Accrudonage runs backtested strategies against real market data, then translates the results into supplemental income opportunities suited to Nigeria's gig economy — without asking you to trade on a hunch.
A simplified view of how a backtested strategy performs against raw market movement. Illustrative only; past results do not guarantee future returns.
Ride-hailing, delivery, freelance contracts and short-term trading all share one trait: the money comes in waves, not a steady tide. A slow week can undo a strong one before you have had time to plan around it.
Many gig workers try to smooth this out by trading currencies, stocks or digital assets on the side. The problem is timing. Markets move continuously, and reading enough data quickly enough — while holding down a full-time hustle — is close to impossible without help.
Accrudonage was built for that gap: the space between wanting a second income stream and having the hours to analyse it properly.
Accrudonage analyses large volumes of historical and live market data, then narrows the output to a small set of recommendations tailored to the markets you choose to follow.
Accrudonage pulls historical and live pricing data across the markets you choose to track, from currency pairs to listed equities.
Each candidate strategy is run against years of historical data before it is ever suggested to a user, a process known as backtesting.
You receive a small number of ranked suggestions, with the reasoning behind each one, rather than an unexplained signal.
As new data comes in, the model reviews its own assumptions and flags when a strategy's conditions have changed.
Every recommendation shown in the app carries a record of the historical period it was tested against, so you can judge its relevance to current conditions yourself.
The blue line represents a backtested strategy's return path; the grey line shows unmanaged exposure to the same asset over the same historical window. The comparison is illustrative — individual results will vary by asset, timing and account size.
The model's task is not only to find opportunities. It is also to flag conditions where a strategy is unlikely to hold, before those conditions affect your account.
Strategies are checked against historical periods of sharp market movement, not just calm ones.
Recommendations include a suggested exposure size relative to typical account balances, not a fixed amount for everyone.
The model highlights when current conditions diverge from the data a strategy was tested on.
You decide whether to act on a recommendation; Accrudonage does not move your money for you.
Backtested performance describes how a strategy behaved on historical data; it is not a promise of future results. All investing and trading carries risk, including loss of capital.
We do not publish testimonials, because a testimonial cannot show you how a strategy performed in 2016 or in 2022. Instead, every strategy on Accrudonage is documented against the specific historical window it was tested on.
Historical and live data are drawn from established public market feeds. We do not use anonymised customer trading data from other platforms.
How do you know a strategy actually works?
We test it against historical price data covering multiple market conditions — calm periods, sharp corrections, and periods of currency stress — before it appears as a recommendation.
What data do you use?
Publicly available historical pricing data for the assets a strategy covers, combined with live pricing feeds for current conditions.
Does a good backtest guarantee a good result going forward?
No. Markets change, and past behaviour is a reference point, not a guarantee. That is why each strategy is reviewed on a rolling basis rather than tested once and left alone.
Can I see the historical period a strategy was tested against?
Yes. Every recommendation includes the date range and market conditions it was backtested on.
Creating an account takes a few minutes. Add the markets you want to track, and Accrudonage begins comparing live conditions against its backtested library at the next data cycle. There are no manual reports to compile between shifts.
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