I would like the following TradingView code to be converted into Prorealtime. Would that be possible?

//Developed by Henrique Centieiro

//Inspired by the great Andrey Markov

//@version=5

indicator(“Markov Chain Trend Probability”, overlay=false)

// Input parameters

lookback_period = input.int(14, title=”Lookback Period (n)”, minval=1, maxval=50)

atr_threshold = input.float(0.5, title=”ATR Threshold Multiplier (X)”, minval=0.1, maxval=2.0, step=0.1)

history_length = input.int(33, title=”Historical Periods for Probability”, minval=1, maxval=100)

atr_length = input.int(14, title=”ATR Length”, minval=1, maxval=50)

brier_lookback = input.int(20, title=”Brier Score Lookback”, minval=1, maxval=100)

// Colors

uptrend_color = input.color(color.green, title=”Uptrend Color”)

downtrend_color = input.color(color.red, title=”Downtrend Color”)

brier_color = input.color(color.orange, title=”Brier Score Color”)

// Brier Score line settings

show_brier = input.bool(true, title=”Show Brier Score”)

brier_style = input.string(“Dashed”, title=”Brier Score Line Style”, options=[“Solid”, “Dashed”, “Dotted”])

brier_width = input.int(1, title=”Brier Score Line Width”, minval=1, maxval=4)

// Calculate ATR

atr_value = ta.atr(atr_length)

// Define states based on price movement and ATR

price_change = close – close[lookback_period]

atr_normalized_change = price_change / atr_value

// State identification (removed sideways state)

var int UPTREND = 1

var int DOWNTREND = -1

// Variable to store current state

var int current_state = UPTREND

// Update state logic

if atr_normalized_change > atr_threshold

  current_state := UPTREND

else if atr_normalized_change < -atr_threshold

  current_state := DOWNTREND

// If within threshold, keep previous state (no change to current_state)

// Store historical states

var array<int> state_history = array.new<int>()

if barstate.isconfirmed

  array.unshift(state_history, current_state)

  if array.size(state_history) > history_length

    array.pop(state_history)

// Calculate transition probabilities

calculate_transition_probability(from_state, to_state) =>

  transitions = 0

  total_from_state = 0

  if array.size(state_history) >= 2

    for i = 0 to array.size(state_history) – 2

      current = array.get(state_history, i)

      next = array.get(state_history, i + 1)

      if current == from_state

        total_from_state += 1

        if next == to_state

          transitions += 1

  total_from_state > 0 ? transitions / total_from_state : 0.0

// Calculate all transition probabilities

p_up_to_up = calculate_transition_probability(UPTREND, UPTREND)

p_up_to_down = calculate_transition_probability(UPTREND, DOWNTREND)

p_down_to_up = calculate_transition_probability(DOWNTREND, UPTREND)

p_down_to_down = calculate_transition_probability(DOWNTREND, DOWNTREND)

// Calculate steady-state probabilities (probability of being in each state)

calculate_state_probability(target_state) =>

  count = 0

  total = array.size(state_history)

  if total > 0

    for i = 0 to total – 1

      if array.get(state_history, i) == target_state

        count += 1

  total > 0 ? count / total : 0.0

prob_uptrend = calculate_state_probability(UPTREND)

prob_downtrend = calculate_state_probability(DOWNTREND)

// Brier Score calculation variables

var array<float> predictions = array.new<float>()

var array<int> actuals = array.new<int>()

// Store predictions and actual outcomes for Brier Score

if barstate.isconfirmed and array.size(state_history) >= 2

  // Store the probability prediction made in the previous bar

  array.unshift(predictions, prob_uptrend[1])

  // Store the actual outcome (what actually happened)

  actual_outcome = current_state == UPTREND ? 1 : 0

  array.unshift(actuals, actual_outcome)

  // Keep only recent data

  if array.size(predictions) > brier_lookback

    array.pop(predictions)

    array.pop(actuals)

// Calculate Brier Score

calculate_brier_score() =>

  if array.size(predictions) < 1

    na

  else

    sum_squared_diff = 0.0

    size = array.size(predictions)

    for i = 0 to size – 1

      pred = array.get(predictions, i)

      actual = array.get(actuals, i)

      sum_squared_diff += math.pow(pred – actual, 2)

    sum_squared_diff / size

brier_score = calculate_brier_score()

// Functions to classify Brier Score accuracy

get_brier_text(score) =>

  if na(score)

    “Calculating…”

  else if score < 0.10

    “Exceptional”

  else if score < 0.15

    “Excellent”

  else if score < 0.20

    “Very Good”

  else if score < 0.25

    “Good”

  else if score < 0.30

    “Fair”

  else

    “Poor”

get_brier_color(score) =>

  if na(score)

    color.gray

  else if score < 0.10

    color.new(color.lime, 0)

  else if score < 0.15

    color.new(color.green, 0)

  else if score < 0.20

    color.new(color.blue, 0)

  else if score < 0.25

    color.new(color.orange, 0)

  else if score < 0.30

    color.new(color.yellow, 0)

  else

    color.new(color.red, 0)

// Convert to percentage values for proper scaling (0-100 instead of 0-1)

prob_uptrend_pct = prob_uptrend * 100

prob_downtrend_pct = prob_downtrend * 100

brier_score_pct = na(brier_score) ? na : brier_score * 100

// Background color based on current state

bg_color = current_state == UPTREND ? color.new(uptrend_color, 95) : color.new(downtrend_color, 95)

bgcolor(bg_color, title=”State Background”)

// Create table for current information

var table info_table = table.new(position.middle_left, 2, 6, bgcolor=color.white, border_width=1)

if barstate.islast

  table.cell(info_table, 0, 0, “Current State”, text_color=color.black, bgcolor=color.gray)

  state_text = current_state == UPTREND ? “UPTREND” : “DOWNTREND”

  state_color = current_state == UPTREND ? uptrend_color : downtrend_color

  table.cell(info_table, 1, 0, state_text, text_color=color.white, bgcolor=state_color)

  table.cell(info_table, 0, 1, “ATR Change”, text_color=color.black)

  table.cell(info_table, 1, 1, str.tostring(atr_normalized_change, “#.##”), text_color=color.black)

  table.cell(info_table, 0, 2, “P(Uptrend)”, text_color=color.black)

  table.cell(info_table, 1, 2, str.tostring(prob_uptrend * 100, “#.#”) + “%”, text_color=uptrend_color)

  table.cell(info_table, 0, 3, “P(Downtrend)”, text_color=color.black)

  table.cell(info_table, 1, 3, str.tostring(prob_downtrend * 100, “#.#”) + “%”, text_color=downtrend_color)

  table.cell(info_table, 0, 4, “P(Up→Up) | P(Down→Down)”, text_color=color.black)

  table.cell(info_table, 1, 4, str.tostring(p_up_to_up * 100, “#.#”) + “% | ” + str.tostring(p_down_to_down * 100, “#.#”) + “%”, text_color=color.black)

  // Brier Score classification

  table.cell(info_table, 0, 5, “Model Accuracy (Brier Score)”, text_color=color.black)

  table.cell(info_table, 1, 5, get_brier_text(brier_score), text_color=get_brier_color(brier_score))

// Convert line style string to Pine Script constant

get_line_style(style_str) =>

  switch style_str

    “Solid” => plot.style_line

    “Dashed” => plot.style_stepline

    “Dotted” => plot.style_circles

    => plot.style_circles

// Plot probabilities as percentages (0-100 scale)

plot(prob_uptrend_pct, title=”Uptrend Probability”, color=uptrend_color, linewidth=2)

plot(prob_downtrend_pct, title=”Downtrend Probability”, color=downtrend_color, linewidth=2)

// Plot Brier Score as a line (scaled to 0-100 for visibility)

plot(show_brier ? brier_score_pct : na, title=”Brier Score (%)”, color=brier_color, linewidth=brier_width, style=get_line_style(brier_style))

// Add horizontal reference lines (now in percentage scale 0-100)

hline(50, title=”50% Line”, color=color.gray, linestyle=hline.style_dashed)

hline(30, title=”30% Line”, color=color.gray, linestyle=hline.style_dotted)

hline(70, title=”70% Line”, color=color.gray, linestyle=hline.style_dotted)

hline(10, title=”10% Line”, color=color.gray, linestyle=hline.style_dotted)

hline(90, title=”90% Line”, color=color.gray, linestyle=hline.style_dotted)

hline(25, title=”Random Baseline (25%)”, color=color.yellow, linestyle=hline.style_dashed)

hline(15, title=”Excellent Threshold (15%)”, color=color.lime, linestyle=hline.style_dotted)

// Alerts (using original 0-1 scale for logic)

alertcondition(prob_uptrend > 0.7, title=”High Uptrend Probability”, message=”Uptrend probability > 70%”)

alertcondition(prob_downtrend > 0.7, title=”High Downtrend Probability”, message=”Downtrend probability > 70%”)

alertcondition(ta.crossover(prob_uptrend, prob_downtrend), title=”Uptrend Probability Crossover”, message=”Uptrend probability crossed above downtrend probability”)

alertcondition(ta.crossover(prob_downtrend, prob_uptrend), title=”Downtrend Probability Crossover”, message=”Downtrend probability crossed above uptrend probability”)

alertcondition(not na(brier_score) and brier_score < 0.15, title=”Excellent Model Accuracy”, message=”Brier Score indicates excellent prediction accuracy”)

alertcondition(not na(brier_score) and brier_score > 0.3, title=”Poor Model Accuracy”, message=”Brier Score indicates poor prediction accuracy”)