Adaptive Trend SelectorThe Adaptive Trend Selector is a comprehensive trend-following tool designed to automatically identify the optimal moving average crossover strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple moving average combinations, determining the best performing lengths, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy.
At its core, the model systematically backtests a wide range of moving average combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using the Sharpe, Sortino, or Calmar ratios. Alternatively, users can enable manual optimization to test custom fast and slow moving average lengths and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the fast and slow lengths displayed at the top:
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Return sensitivity relative to buy-and-hold.
Alpha (α) = Excess annualized risk-adjusted returns.
Win Rate = Ratio of profitable trades to total trades.
Profit Factor = Total gross profit per unit of losses.
Expectancy = Average expected return per trade.
Trades/Year = Average number of trades per year.
This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period and the preferred moving average strategy. Supported strategies include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes longer, more robust combinations by requiring shorter-length strategies to exceed this threshold. The table follows an intuitive color logic that enables quick performance comparison against buy-and-hold (B&H):
Sharpe = Green indicates better than B&H, while red indicates worse.
Sortino = Green indicates better than B&H, while red indicates worse.
Calmar = Green indicates better than B&H, while red indicates worse.
Max DD = Green indicates better than B&H, while red indicates worse.
Beta (β) = Green indicates better than B&H, while red indicates worse.
Alpha (α) = Green indicates above 0%, while red indicates below 0%.
Win Rate = Green indicates above 50%, while red indicates below 50%.
Profit Factor = Green indicates above 2, while red indicates below 1.
Expectancy = Green indicates above 0%, while red indicates below 0%.
In summary, the Adaptive Trend Selector is a powerful tool designed to help investors make data-driven decisions when selecting moving average crossover strategies. By optimizing for risk-adjusted returns, investors can confidently identify the best lengths using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of potential overfitting, as past results may not persist under future market conditions. Since the model recalibrates to incorporate new data, the recommended lengths may evolve over time.
Autotrend
Automatic TrendlinesIntroduction
For a full free tutorial explaining this code in more detail, visit the backtest-rookies (.com) website.
This indicator will plot two trend lines at any given time. A resistance trend line and a support trend line. The resistance trend is shown with red circles and is created by joining swing highs together. The second is a support trend which is created by joining swing lows.
Since we need swings to make the trend, the trend line code contains code for the swing detection. You can play around with the swing detection to alter how frequently new trend lines are detected. Relying on swings also means that there will be some delay in trend detection depending on how you configure the swing detection. The higher you set rightbars, the more lag you will have before a trend is detected. However, at the same time the quality of the pivots found will increase. So it is a trade-off you need to come to terms with and decide what the best settings are for you.
Lines
A single trend line is made up of several components.
Pivot Points: Marked as blue or orange circles. There will be two pivots per trend.
Orange/Purple Lines: Connecting all pivot points. You will see these lines change direction slightly each time a new pivot is detected (new circles appear).
Green/Red Circle lines: Showing the trend line from the earliest moment a new trend is detected.
Blue Dashed lines: Joining the purple and green/red circle lines so the full trend line can be seen.
Note: The blue dashed lines use pine-scripts drawing functions. As such, there is a limit to how many of these can be placed on a chart. When the limit is reached, the oldest line will be removed so the newest can be drawn. This means that if you detect enough trends and scroll back in time, the blue dashed lines will disappear at some point!
