Adaptive Market Profile indicator – Auto Detect & Dynamic Activity Zones

-- Adaptive Market Profile Indicator
-- Automatically detects optimal regression channel period using Pearson's R and displays a dynamic market profile.

-- Inputs
InputGroupHeader("General Settings")
local auto_period = Input("Auto Trend Channel Period", true, "Enable automatic period detection based on trend strength.")
local manual_period = Input("Manual Period", 50, "Fixed period if auto is disabled.")
local min_period = Input("Min Scan Period", 10, "Minimum period for auto scanning.")
local max_period = Input("Max Scan Period", 200, "Maximum period for auto scanning.")
local dev_multiplier = Input("Deviation Multiplier", 2.0, "Multiplier for channel width (std dev * multiplier).")

InputGroupHeader("Profile Settings")
local sections = Input("Profile Sections", 10, "Number of zones to divide the channel into.")
local use_volume = Input("Use Volume for Profile", true, "If true, use volume; else, count touches (closes in zone).")
local num_active = Input("Number of Active Lines", 3, "Number of most active zones to plot as parallel lines.")
local log_scale = Input("Log Scale", false, "Perform calculations on log(prices) for log charts.")
local debug_mode = Input("Debug Mode", false, "Enable verbose logging for debugging.")

-- Utility Functions
local function sub_array(arr, len)
    local sub = {}
    for i = 1, len do
        sub[i] = arr[#arr - len + i] or 0
    end
    return sub
end

local function pearson_r(prices)
    local n = #prices
    if n < 2 then return 0 end
    local sum_x, sum_y, sum_xy, sum_x2, sum_y2 = 0, 0, 0, 0, 0
    for i = 1, n do
        local x = i
        local y = tonumber(prices[i]) or 0 -- Convert to number, fallback to 0
        sum_x = sum_x + x
        sum_y = sum_y + y
        sum_xy = sum_xy + x * y
        sum_x2 = sum_x2 + x * x
        sum_y2 = sum_y2 + y * y
        if debug_mode then
            Log("Pearson: Price[" .. i .. "] = " .. y)
        end
    end
    local mean_x = sum_x / n
    local mean_y = sum_y / n
    local cov = (sum_xy - n * mean_x * mean_y) / (n - 1)
    local var_x = (sum_x2 - n * mean_x * mean_x) / (n - 1)
    local var_y = (sum_y2 - n * mean_y * mean_y) / (n - 1)
    if var_x == 0 or var_y == 0 then return 0 end
    local r = cov / math.sqrt(var_x * var_y)
    if debug_mode then
        Log("Pearson R: " .. Round(r, 4))
    end
    return r
end

local function linear_regression(prices)
    local n = #prices
    local sum_x, sum_y, sum_xy, sum_x2 = 0, 0, 0, 0
    for i = 1, n do
        local x = i
        local y = tonumber(prices[i]) or 0 -- Convert to number, fallback to 0
        sum_x = sum_x + x
        sum_y = sum_y + y
        sum_xy = sum_xy + x * y
        sum_x2 = sum_x2 + x * x
        if debug_mode then
            Log("Regression: Price[" .. i .. "] = " .. y)
        end
    end
    local denom = (n * sum_x2 - sum_x * sum_x)
    if denom == 0 then return 0, prices[1] or 0 end
    local slope = (n * sum_xy - sum_x * sum_y) / denom
    local intercept = (sum_y - slope * sum_x) / n
    if debug_mode then
        Log("Slope: " .. Round(slope, 4) .. ", Intercept: " .. Round(intercept, 4))
    end
    return slope, intercept
end

local function get_std_residuals(prices, slope, intercept)
    local n = #prices
    local sum_sq = 0
    for i = 1, n do
        local predicted = intercept + slope * i
        local price = tonumber(prices[i]) or 0 -- Convert to number, fallback to 0
        sum_sq = sum_sq + (price - predicted) ^ 2
        if debug_mode then
            Log("StdDev: Price[" .. i .. "] = " .. price .. ", Predicted = " .. predicted)
        end
    end
    local std = n > 2 and math.sqrt(sum_sq / (n - 2)) or 0
    if debug_mode then
        Log("Standard Deviation: " .. Round(std, 4))
    end
    return std
end

-- Main Logic
local prices = ClosePrices()
local volumes = GetVolume()

-- Ensure prices and volumes are valid
if #prices < 2 or #volumes < 2 then
    LogError("Insufficient data for calculation. Prices: " .. #prices .. ", Volumes: " .. #volumes)
    return
end

-- Apply log scale if enabled
if log_scale then
    local temp_prices = {}
    for i = 1, #prices do
        local price = tonumber(prices[i]) or 0
        temp_prices[i] = price > 0 and math.log(price) or 0
        if debug_mode then
            Log("Log Scale: Price[" .. i .. "] = " .. price .. ", Log = " .. temp_prices[i])
        end
    end
    prices = temp_prices
end

-- Auto period detection
local best_len = manual_period
local best_r = 0
if auto_period then
    for len = min_period, max_period do
        if len > #prices then break end
        local sub = sub_array(prices, len)
        local r = math.abs(pearson_r(sub))
        if r > best_r then
            best_r = r
            best_len = len
        end
    end
end

if best_len > #prices then best_len = #prices end
if best_len < 2 then best_len = 2 end
if debug_mode then
    Log("Selected Period: " .. best_len .. ", Pearson R: " .. Round(best_r, 4))
end

local sub_prices = sub_array(prices, best_len)
local sub_volumes = sub_array(volumes, best_len)

local slope, intercept = linear_regression(sub_prices)
local std = get_std_residuals(sub_prices, slope, intercept)
local dev = std * dev_multiplier

local n = best_len
local middle = intercept + slope * n
local upper = middle + dev
local lower = middle - dev

-- Reverse log scale for plotting
if log_scale then
    middle = math.exp(middle)
    upper = math.exp(upper)
    lower = math.exp(lower)
end

-- Plot the Channel
Plot(0, "Upper Channel", upper, Red)
Plot(0, "Middle Channel", middle, Blue)
Plot(0, "Lower Channel", lower, Green)
if debug_mode then
    Log("Plotting Channels: Upper = " .. Round(upper, 4) .. ", Middle = " .. Round(middle, 4) .. ", Lower = " .. Round(lower, 4))
end

-- Market Profile Calculation
local bins = {}
for i = 1, sections do bins[i] = 0 end

for j = 1, best_len do
    local past_price = tonumber(sub_prices[j]) or 0
    local predicted = intercept + slope * j
    local lower_at_j = predicted - dev
    local upper_at_j = predicted + dev
    if upper_at_j > lower_at_j then
        local rel_pos = (past_price - lower_at_j) / (upper_at_j - lower_at_j)
        local bin_idx = math.floor(rel_pos * sections) + 1
        if bin_idx < 1 then bin_idx = 1 elseif bin_idx > sections then bin_idx = sections end
        local add = use_volume and (tonumber(sub_volumes[j]) or 0) or 1
        bins[bin_idx] = bins[bin_idx] + add
        if debug_mode then
            Log("Profile: Price[" .. j .. "] = " .. past_price .. ", Bin[" .. bin_idx .. "] += " .. add)
        end
    end
end

-- Find Top N Active Bins
local bin_list = {}
for i = 1, sections do
    table.insert(bin_list, {idx = i, val = bins[i]})
end
table.sort(bin_list, function(a, b) return a.val > b.val end)

-- Plot Active Lines (using only 4 arguments: chartId, name, value, color)
local colors = {Green, Yellow, Orange, Red, Purple}
for k = 1, math.min(num_active, sections) do
    local bin_idx = bin_list[k].idx
    local f = (bin_idx - 0.5) / sections
    local active_line = lower + f * (upper - lower)
    Plot(0, "Active Line " .. k, active_line, colors[k] or White) -- Removed Dashed to avoid 5th argument
    if debug_mode then
        Log("Plotting Active Line " .. k .. ": Value = " .. Round(active_line, 4) .. ", Color = " .. tostring(colors[k] or White))
    end
end

-- Reporting
Finalize(function()
    CustomReport("Optimal Period", best_len)
    CustomReport("Trend Strength (R)", Round(best_r, 4))
end)