wtc

The provided strategy, called "wtc," calculates several indicators and uses them to generate buy and sell signals. Here's a brief description of what the strategy does: The strategy begins by populating indicators based on the input data, including: Average Price (ap): Calculated as the average of the high, low, and close prices. Exponential Moving Average (esa): A 10-period EMA of the average price.

"d": A 10-period EMA of the absolute difference between the average price and esa.

"ci": Represents the deviation between the average price and esa, normalized by "d." "tci": A 21-period EMA of ci.

"wt1" and "wt2": Derived from tci and represent different moving averages. The strategy then applies the Stochastic indicator to the data, calculating the "slowk" value. The data is scaled using MinMaxScaler to normalize the values. The "def" column is calculated as the difference between the slowk and wt1. If any error occurs during the indicator calculations, default values are assigned to wt1, wt2, def, and slowk. The populate_buy_trend function identifies potential buy signals based on the following conditions: wt1 crossing above wt2. wt1 being within the specified buy_min0 and buy_max0 values. slowk being within the specified buy_min1 and buy_max1 values. def being within the specified buy_min and buy_max values. The populate_sell_trend function identifies potential sell signals based on the following conditions: wt1 crossing below wt2. wt1 being within the specified sell_min0 and sell_max0 values. slowk being within the specified sell_min1 and sell_max1 values. def being within the specified sell_min and sell_max values. Both populate_buy_trend and populate_sell_trend functions update the "buy" and "sell" columns of the dataframe accordingly, marking the respective signals. Overall, the strategy uses various indicators and their relationships to generate buy and sell signals based on predefined ranges and conditions.