Volume Storm Trend [ChartPrime]The Volume Storm Trend (VST) indicator is a robust tool for traders looking to analyze volume momentum and trend strength in the market. By incorporating key volume-based calculations and dynamic visualizations, VST provides clear insights into market conditions.
Components:
Calculating the median of the source data.
Volume Power Calculation: The indicator calculates the "heat power" and "cold power" by applying an Exponential Moving Average (EMA) to the median of volume data arrays.
// ---------------------------------------------------------------------------------------------------------------------}
// 𝙄𝙉𝘿𝙄𝘾𝘼𝙏𝙊𝙍 𝘾𝘼𝙇𝘾𝙐𝙇𝘼𝙏𝙄𝙊𝙉𝙎
// ---------------------------------------------------------------------------------------------------------------------{
max_val = 1000
src = close
source = ta.median(src, len)
heat.push(src > source ? (volume > max_val ? max_val : volume) : 0)
heat.remove(0)
cold.push(src < source ? (volume > max_val ? max_val : volume) : 0)
cold.remove(0)
heat_power = ta.ema(heat.median(), 10)
cold_power = ta.ema(cold.median(), 10)
Visualization:
Gradient Colors: The indicator uses gradient colors to visualize bullish volume and bearish volume powers, providing a clear contrast between rising and falling trends.
Bars Fill Color: The color fill between high and low prices changes based on whether the heat power is greater than the cold power.
Bottom Line: A zero line with changing colors based on the dominance of heat or cold power.
Weather Symbols: Visual indicators ("☀" for hot weather and "❄" for cold weather) appear on the chart when the heat and cold powers crossover, helping traders quickly identify trend changes.
Inputs:
Source: The input data source, typically the closing price.
Median Length: The period length for calculating the median of the source. Default is 40.
Volume Length: The period length for calculating the average volume. Default is 3.
Show Weather: A toggle to display weather symbols on the chart. Default is false.
Temperature Type: Allows users to choose between Celsius (°C) and Fahrenheit (°F) for temperature display.
Show Weather Function:
The `Show Weather?` function enhances the VST indicator by displaying weather symbols ("☀" for hot and "❄" for cold) when there are significant crossovers between heat power and cold power. This feature adds a visual cue for potential market tops and bottoms. When the market heats to a high temperature, it often indicates a potential top, signaling traders to consider exiting long positions or preparing for a reversal.
Additional Features:
Dynamic Table Display: A table displays the current "temperature" on the chart, indicating market heat based on the calculated heat and cold powers.
The Volume Storm Trend indicator is a powerful tool for traders
looking to enhance their market analysis with volume and momentum insights, providing a clear and visually appealing representation of key market dynamics.
Weather
DatasetWeatherTokyoMeanAirTemperatureLibrary "DatasetWeatherTokyoMeanAirTemperature"
Provides a data set of the monthly mean air temperature (°C) for the city of Tokyo in Japan.
this was just for fun, no financial implications in this.
reference:
www.data.jma.go.jp
TOKYO WMO Station ID:47662 Lat 35o41.5'N Lon 139o45.0'E
year_()
the years of the data set.
Returns: array : year values.
january()
the january values of the dataset
Returns: array\ : data values for january.
february()
the february values of the dataset
Returns: array\ : data values for february.
march()
the march values of the dataset
Returns: array\ : data values for march.
april()
the april values of the dataset
Returns: array\ : data values for april.
may()
the may values of the dataset
Returns: array\ : data values for may.
june()
the june values of the dataset
Returns: array\ : data values for june.
july()
the july values of the dataset
Returns: array\ : data values for july.
august()
the august values of the dataset
Returns: array\ : data values for august.
september()
the september values of the dataset
Returns: array\ : data values for september.
october()
the october values of the dataset
Returns: array\ : data values for october.
november()
the november values of the dataset
Returns: array\ : data values for november.
december()
the december values of the dataset
Returns: array\ : data values for december.
annual()
the annual values of the dataset
Returns: array\ : data values for annual.
select_month(idx)
get the temperature values for a specific month.
Parameters:
idx : int, month index (1 -> 12 | any other value returns annual average values).
Returns: array\ : data values for selected month.
select_value(year_, month_)
get the temperature value of a specified year and month.
Parameters:
year_ : int, year value.
month_ : int, month index (1 -> 12 | any other value returns annual average values).
Returns: float : value of specified year and month.
diff_to_median(month_)
the difference of the month air temperature (ºC) to the median of the sample.
Parameters:
month_ : int, month index (1 -> 12 | any other value returns annual average values).
Returns: float : difference of current month to median in (Cº)
