We bring publicly available strategies into one controlled research environment, so differences in results come from the strategy logic—not from different data, costs or backtest settings.
We research popular, publicly available strategies from online communities such as TradingView. The original author is credited on every strategy page. We select strategies because they are widely followed—not because their published backtests look profitable.
Each auditable strategy is translated into a fixed Python program. We compare its trades with the original TradingView strategy and resolve timing, indicator and order-execution differences before publishing it.
Strategies use the same normalized market database, trading sessions and bar-building rules. A 15-minute result cannot gain an advantage from cleaner or differently aligned data than another 15-minute result.
Every standalone strategy is tested with the same published starting-capital convention and fixed parameters. Users cannot tune parameters or choose a flattering custom start date after seeing the result.
Net performance includes market-specific commission and slippage assumptions. We rank and compare net results; gross performance remains visible only to show how much trading friction changed the outcome.
Each equity curve is compared with buying and holding the same market over the same dates and starting capital. This helps distinguish genuine timing value from simply holding an asset that rose strongly.
Instead of collecting attractive screenshots from unrelated backtests, we turn popular public ideas into comparable research objects: one Python engine, one data standard, realistic costs, fixed choices and visible benchmarks across 23 markets and five timeframes.
A backtest is still a historical simulation, not a prediction. Liquidity, financing, taxes, market impact and future regime changes can make live results materially different.