ProTrend Adaptive Strategy by TradingClue

What it is
A volatility-adaptive trend-following strategy optimized on BTCUSD, 4-hour bars. It is built on the same adaptive signal engine as the ProTrend Adaptive Indicator by TradingClue, turning it into rule-based entries, exits, and position handling. The engine adjusts its effective lookback between user-defined bounds as volatility changes—tightening in quiet conditions and widening during expansions.
Entries require agreement between the adaptive signal and a Supertrend directional filter. The default exit closes on a Supertrend flip; no fixed profit target is used by default to preserve trend capture.

Why “adaptive” matters
Static lookbacks tend to over-trade in chop and under-react in fast moves. By letting the effective length glide between a minimum and a maximum based on ATR-style dispersion, the strategy aims to filter some sideways noise without giving up major runs.

Scope & portability
While tuned on BTCUSD 4h, the approach is portable to other symbols and asset classes—FX pairs/crosses, crypto assets, equities, and commodities—provided that parameters are re-tuned (lookback bounds, Supertrend factor, ATR settings, costs). Market microstructure differs, so validation with rolling metrics and cost assumptions is essential.

Context vs. Buy & Hold
This strategy is not intended to “win” against buy-and-hold in every regime. In persistent bull markets, passive exposure can deliver higher absolute returns.
In the backtest window shown on the strategy report, the parameter preset produced a higher (after-cost) equity curve than buy-and-hold for the same symbol/timeframe. Results are time-frame and cost dependent: during mixed or range-bound regimes, rules-based exits and variable exposure can reduce drawdowns and smooth the curve; during strong, uninterrupted uptrends, buy-and-hold may lead.
Results shown are for Jan 1, 2024 – present. The choice of a recent window is intentional:
- Relevance of regime. Crypto market microstructure evolves quickly (liquidity, spreads, leverage, volatility clustering). Using a contemporary window reduces non-stationarity between the sample and the environment we expect over the next quarters.
- Parameter fit to current volatility. The strategy’s adaptive engine is tuned to recent volatility levels; very old regimes can dilute evaluation and are less informative for forward expectations.
- Representative mix. The window contains both trending and range-bound segments, which is useful to assess trend capture vs. whipsaw control on BTCUSD 4h.

Robustness.
We also review longer-history runs and cost-stress scenarios (higher commission/slippage) and recommend users to do the same. Backtests are approximations; live results can differ due to fills, fees, slippage, funding, outages, and latency.

Intended use
Research/education and systematic testing. If used live, align commission and slippage with your venue, review rolling metrics (e.g., 90/180-day Profit Factor), and perform walk-forward and cost-stress checks.

Backtest context for the shared parameter-set
Symbol/TF: BTCUSD, 4h
commission: 0.04%
slippage: 2 ticks
backtesting-window: Jan 1, 2024 – present

Limitations
Backtests are approximations. Fills, fees, slippage, funding, outages and latency can deviate from live execution. Past performance does not guarantee future results. This publication is not financial advice.