Price and Date Forecast V1

// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © congacindy

//@version=5
indicator('Price and Date Forecast (2024 V1)', overlay=true)

// Input parameters
cycle_length = input.int(12, title='Dominant Cycle Length', minval=1)
forecast_periods = input.int(10, title='Number of Periods to Forecast', minval=1)
price = input.source(hl2)

// Calculate the dominant cycle using time series analysis
//price = close
//cycle = ta.sma(price, cycle_length)

txt_col_price = input.color(color.rgb(208, 227, 62), 'Text/Frame Color', group='Dashboard')
txt_col_date = input.color(color.rgb(48, 228, 241), 'Text/Frame Color', group='Dashboard')
txt_col_pattern = input.color(color.rgb(238, 232, 166), 'Text/Frame Color', group='Dashboard')
txt_col = input.color(color.rgb(182, 186, 200), 'Text/Frame Color', group='Dashboard')
dash_loc = input.string('Mid Right', 'Dashboard Location', options=['Top Right', 'Bottom Right', 'Top Left', 'Mid Left', 'Mid Right'], group='Dashboard')
text_size = input.string('Tiny', 'Dashboard Size', options=['Tiny', 'Small', 'Normal', 'Large'], group='Dashboard')


// Goertzel algorithm to detect the dominant cycle
goertzel(price, length) =>
    k = 0.0
    w = 2 * math.pi / length
    cosine = math.cos(w)
    sine = math.sin(w)
    coeff = 2 * cosine
    q0 = 0.0
    q1 = 0.0
    q2 = 0.0
    for i = 0 to length - 1
        q0 := coeff * q1 - q2 + price[i]
        q2 := q1
        q1 := q0
    real = (q1 - q2 * cosine)
    imag = (q2 * sine)
    magnitude = math.sqrt(real * real + imag * imag)
    [real, imag, magnitude]

// Calculate the dominant cycle using Goertzel algorithm
//[real, imag, cycle] = goertzel(price, cycle_length)

// Convert cycle to integer
cycle_int = int(cycle_length)

//forecast_price(periods) =>
//    [real, imag, magnitude] = goertzel(price, cycle_length)
//    phase = math.atan(imag/ real)
//    amplitude = magnitude / 2
//    center = (ta.highest(price, cycle_int) + ta.lowest(price, cycle_int)) / 2
//    forecast = center + amplitude * math.sin(phase + 2 * math.pi * periods / cycle_int)
//    forecast

forecast_price(periods) =>
    [real, imag, magnitude] = goertzel(price, cycle_length)
    phase = math.atan(imag / real)
    amplitude = (ta.highest(price, cycle_int) - ta.lowest(price, cycle_int)) / 2
    center = (ta.highest(price, cycle_int) + ta.lowest(price, cycle_int)) / 2
    
    historical_volatility = ta.stdev(price, cycle_int) / center
    
    sine_wave = amplitude * math.sin(phase + 2 * math.pi * periods / cycle_int)
    
    pattern_length = cycle_int
    pattern = true
    for i = 1 to pattern_length - 1
        if price[i] <= price[i-1]
            pattern := false
            break
    
    var forecast = 0.0
    
    if pattern
        forecast := center + sine_wave * (1 + historical_volatility * (math.random() - 0.5))
    else
        forecast := center + sine_wave * (1 - historical_volatility * (math.random() - 0.5))
    
    current_close = close
    forecast_adjusted = (forecast + current_close) / 2
    
    forecast_adjusted





//forecast_price(periods) =>
//    [real, imag, magnitude] = goertzel(price, cycle_length)
//    phase = math.atan(imag / real)
//    amplitude = (ta.highest(price, cycle_int) - ta.lowest(price, cycle_int)) / 2
//    center = (ta.highest(price, cycle_int) + ta.lowest(price, cycle_int)) / 2
//    
//    historical_volatility = ta.stdev(price, cycle_int) / center
//    
//    sine_wave = amplitude * math.sin(phase + 2 * math.pi * periods / cycle_int)
//    forecast = center + sine_wave * (1 + historical_volatility * (math.random() - 0.5))
//    forecast



//forecast_price(periods) =>
//    [real, imag, magnitude] = goertzel(price, cycle_length)
//    phase = math.atan(imag / real)
//    amplitude = (ta.highest(price, cycle_int) - ta.lowest(price, cycle_int)) / 2
//    center = (ta.highest(price, cycle_int) + ta.lowest(price, cycle_int)) / 2
//    sine_wave = amplitude * math.sin(phase + 2 * math.pi * periods / cycle_int)
//    forecast = center + sine_wave
//    forecast

// Forecast future prices using time series analysis
forecast_price2(periods) =>
    slope = ta.linreg(price, cycle_int, 0)
    intercept = price[0] - slope * bar_index
    forecast2 = intercept + slope * (bar_index + periods)
    forecast2



// Forecast future prices using a sine wave
forecast_price3(periods) =>
    amplitude = (ta.highest(price, cycle_int) - ta.lowest(price, cycle_int)) / 2
    center = (ta.highest(price, cycle_int) + ta.lowest(price, cycle_int)) / 2
    phase = 2 * math.pi /cycle_int
    forecast3 = center + amplitude * math.sin(phase * (bar_index + periods))
    forecast3


// Forecast future dates
//forecast_date(periods) =>
//    timestamp(year(timenow) + math.floor((month(timenow) + periods - 1) / 12), (month(timenow) + periods - 1) % 12 + 1, dayofmonth(timenow), hour(timenow), minute(timenow), second(timenow))

// Forecast future dates
forecast_date(periods) =>
    timestamp(year(timenow), month(timenow), dayofmonth(timenow) + periods, hour(timenow), minute(timenow), second(timenow))


// Plot the dominant cycle
//plot(cycle, color=color.new(color.blue, 0), title='Dominant Cycle')

// Display the prices following the matching price pattern
//var table forecast_table = table.new(position.bottom_right, 1, forecast_length + 1, border_width=1)
//table.cell(forecast_table, 0, 0, 'Forecasted Prices', text_color=color.white, bgcolor=color.gray)

//for i = 1 to forecast_length by 1
//    if similar_pattern_index + pattern_length + i - 1 < bar_index
//        forecasted_price = close[similar_pattern_index + pattern_length + i - 1]
//        table.cell(forecast_table, 0, i, str.tostring(forecasted_price, '#.##'), bgcolor=color.white)
//    else
//        table.cell(forecast_table, 0, i, 'N/A', bgcolor=color.white)


var table_position = dash_loc == 'Top Left' ? position.top_left : dash_loc == 'Bottom Left' ? position.bottom_left : dash_loc == 'Top Right' ? position.top_right : dash_loc == 'Mid Left' ? position.middle_left : dash_loc == 'Mid Right' ? position.middle_right : position.bottom_right
var table_text_size = text_size == 'Tiny' ? size.tiny : text_size == 'Small' ? size.small : text_size == 'Normal' ? size.normal : size.large

// Create a table to display the forecasted prices and dates
var table forecast_table = table.new(table_position, 2, forecast_periods + 1, border_width=1, border_color=txt_col,frame_color=txt_col,frame_width=1)

// Add table headers
//table.cell(forecast_table, 0, 0, 'Period', text_color=txt_col, text_size=table_text_size)
table.cell(forecast_table, 0, 0, 'Price', text_color=txt_col, text_size=table_text_size)
table.cell(forecast_table, 1, 0, 'Date', text_color=txt_col, text_size=table_text_size)

// Populate the table with forecasted prices and dates
for i = 1 to forecast_periods by 1
    forecast_price_value = forecast_price(i)
    forecast_date_value = forecast_date(i)
    //table.cell(forecast_table, 0, i, str.tostring(i), text_color=txt_col, text_size=table_text_size)
    table.cell(forecast_table, 0, i, str.tostring(forecast_price_value, '#.##'), text_color=txt_col_price, text_size=table_text_size)
    table.cell(forecast_table, 1, i, str.format("{0,date,yyyy-MM-dd}", forecast_date_value), text_color=txt_col_date, text_size=table_text_size)