Machine Learning: Logistic Regression

• 03/01/2021 at 5:37 PM #162889
  NEOMKEY

  Hola a todos, me gustaría poder tener este indicador (proveniente de la plataforma Tradingview) en PRT v.11.

  Os dejo el código, así cómo un pantallazo de dicho indicador en Tradingview.
  Muchas gracias de antemano.

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  // This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
  // © capissimo

  //@version=4
  study(‘Machine Learning: Logistic Regression (v.3)’, ”, true)

  // Multi-timeframe Strategy based on Logistic Regression algorithm

  // Description:

  // This strategy uses a classic machine learning algorithm that came from statistics –
  // Logistic Regression (LR).

  // The first and most important thing about logistic regression is that it is not a
  // ‘Regression’ but a ‘Classification’ algorithm. The name itself is somewhat misleading.
  // Regression gives a continuous numeric output but most of the time we need the output in
  // classes (i.e. categorical, discrete). For example, we want to classify emails into “spam” or
  // ‘not spam’, classify treatment into “success” or ‘failure’, classify statement into “right”
  // or ‘wrong’, classify election data into ‘fraudulent vote’ or ‘non-fraudulent vote’,
  // classify market move into ‘long’ or ‘short’ and so on. These are the examples of
  // logistic regression having a binary output (also called dichotomous).

  // You can also think of logistic regression as a special case of linear regression when the
  // outcome variable is categorical, where we are using log of odds as dependent variable.
  // In simple words, it predicts the probability of occurrence of an event by fitting data to a
  // logit function.

  // Basically, the theory behind Logistic Regression is very similar to the one from Linear
  // Regression, where we seek to draw a best-fitting line over data points, but in Logistic
  // Regression, we don’t directly fit a straight line to our data like in linear regression.
  // Instead, we fit a S shaped curve, called Sigmoid, to our observations, that best
  // SEPARATES data points. Technically speaking, the main goal of building the model is to
  // find the parameters (weights) using gradient descent.

  // In this script the LR algorithm is retrained on each new bar trying to classify it
  // into one of the two categories. This is done via the logistic_regression function by
  // updating the weights w in the loop that continues for iterations number of times.
  // In the end the weights are passed through the sigmoid function, yielding a
  // prediction.

  // Mind that some assets require to modify the script’s input parameters. For instance,
  // when used with BTCUSD and USDJPY, the ‘Normalization Lookback’ parameter should be set
  // down to 4 (2,…,5..), and optionally the ‘Use Price Data for Signal Generation?’
  // parameter should be checked. The defaults were tested with EURUSD.

  // Note: TradingViews’s playback feature helps to see this strategy in action.
  // Warning: Signals ARE repainting.

  // Style tags: Trend Following, Trend Analysis
  // Asset class: Equities, Futures, ETFs, Currencies and Commodities
  // Dataset: FX Minutes/Hours++/Days

  //——————– Inputs

  ptype = input(‘Close’,’Price Type’, options=[‘Open’,’High’,’Low’,’Close’,’HL2′,’OC2′,’OHL3′,’HLC3′,’OHLC4′])
  reso = input(”, ‘Resolution’, input.resolution)
  lookback = input(3, ‘Lookback Window Size |2..n| (2)’, minval=2)
  nlbk = input(2, ‘Normalization Lookback |2..240| (120)’, minval=2, maxval=240)
  lrate = input(0.0009, ‘Learning Rate |0.0001..0.01|’, minval=0.0001, maxval=0.01, step=0.0001)
  iterations = input(1000, ‘Training Iterations |50..20000|’, minval=50)
  ftype = input(‘None’, ‘Filter Signals by’, options=[‘Volatility’,’Volume’,’Both’,’None’])
  curves = input(false, ‘Show Loss & Prediction Curves?’)
  easteregg = input(true, ‘Optional Calculation?’)
  useprice = input(true, ‘Use Price Data for Signal Generation?’)
  holding_p = input(5, ‘Holding Period |1..n|’, minval=1)

  //——————– System Variables

  var BUY = 1, var SELL = -1, var HOLD = 0
  var signal = HOLD
  var hp_counter = 0

  //——————– Custom Functions

  cAqua(g) => g>9?#0080FFff:g>8?#0080FFe5:g>7?#0080FFcc:g>6?#0080FFb2:g>5?#0080FF99
  : g>4?#0080FF7f:g>3?#0080FF66:g>2?#0080FF4c:g>1?#0080FF33:#00C0FF19
  cPink(g) => g>9?#FF0080ff:g>8?#FF0080e5:g>7?#FF0080cc:g>6?#FF0080b2:g>5?#FF008099
  : g>4?#FF00807f:g>3?#FF008066:g>2?#FF00804c:g>1?#FF008033:#FF008019

  volumeBreak(thres) =>
  rsivol = rsi(volume, 14)
  osc = hma(rsivol, 10)
  osc > thres

  volatilityBreak(volmin, volmax) => atr(volmin) > atr(volmax)

  dot(v, w, p) => sum(v * w, p) // dot product

  sigmoid(z) => 1.0 / (1.0 + exp(-z))

  logistic_regression(X, Y, p, lr, iterations) =>
  w = 0.0, loss = 0.0
  for i=1 to iterations
  hypothesis = sigmoid(dot(X, 0.0, p)) //– prediction
  loss := -1.0 / p * (dot(dot(Y, log(hypothesis) + (1.0 – Y), p), log(1.0 – hypothesis), p))
  gradient = 1.0 / p * (dot(X, hypothesis – Y, p))
  w := w – lr * gradient //– update weights

  [loss, sigmoid(dot(X, w, p))] //– current loss & prediction

  minimax(ds, p, min, max) => // normalize to price
  hi = highest(ds, p), lo = lowest(ds, p)
  (max – min) * (ds – lo)/(hi – lo) + min

  //——————– Logic

  ds = ptype==’Open’ ? open
  : ptype==’High’ ? high
  : ptype==’Low’ ? low
  : ptype==’Close’ ? close
  : ptype==’HL2′ ? (high+low)/2
  : ptype==’OC2′ ? (open+close)/2
  : ptype==’OHL3′ ? (open+high+low)/3
  : ptype==’HLC3′ ? (high+low+close)/3
  : (open+high+low+close)/4

  base_ds = security(”, reso, ds[barstate.ishistory ? 0 : 1])
  synth_ds = log(abs(pow(base_ds, 2) – 1) + .5) //– generate a synthetic dataset

  base = easteregg ? time : base_ds //– standard and optional calculation
  synth = easteregg ? base_ds : synth_ds

  [loss, prediction] = logistic_regression(base, synth, lookback, lrate, iterations)
  scaled_loss = minimax(loss, nlbk, lowest(base_ds, nlbk), highest(base_ds, nlbk))
  scaled_prediction = minimax(prediction, nlbk, lowest(base_ds, nlbk), highest(base_ds, nlbk))

  filter = ftype==’Volatility’ ? volatilityBreak(1, 10)
  : ftype==’Volume’ ? volumeBreak(49)
  : ftype==’Both’ ? volatilityBreak(1, 10) and volumeBreak(49)
  : true

  signal := useprice ?
  base_ds < scaled_loss and filter ? SELL : base_ds > scaled_loss and filter ? BUY : nz(signal[1])
  : crossunder(scaled_loss, scaled_prediction) and filter ? SELL : crossover(scaled_loss, scaled_prediction) and filter ? BUY : nz(signal[1])

  changed = change(signal)

  hp_counter := changed ? 0 : hp_counter + 1

  startLongTrade = changed and signal==BUY
  startShortTrade = changed and signal==SELL
  endLongTrade = (signal==BUY and hp_counter==holding_p and not changed) or (changed and signal==SELL)
  endShortTrade = (signal==SELL and hp_counter==holding_p and not changed) or (changed and signal==BUY)

  //——————– Rendering

  plot(curves ? scaled_loss : na, ”, color.blue, 3)
  plot(curves ? scaled_prediction : na, ”, color.lime, 3)

  plotshape(startLongTrade ? low : na, ‘Buy’, shape.labelup, location.belowbar, cAqua(10), size=size.small)
  plotshape(startShortTrade ? high : na, ‘Sell’, shape.labeldown, location.abovebar, cPink(10), size=size.small)
  plot(endLongTrade ? high : na, ‘StopBuy’, cAqua(6), 2, plot.style_cross)
  plot(endShortTrade ? low : na, ‘StopSell’, cPink(6), 2, plot.style_cross)

  //——————– Alerting

  alertcondition(startLongTrade, ‘Buy’, ‘Go long!’)
  alertcondition(startShortTrade, ‘Sell’, ‘Go short!’)
  alertcondition(startLongTrade or startShortTrade, ‘Alert’, ‘Deal Time!’) //– general alert

  //——————– Backtesting (TODO)

  lot_size = input(0.01, ‘Lot Size’, options=[0.01,0.1,0.2,0.3,0.5,1,2,3,5,10,20,30,50,100,1000])

  ohl3 = (open+high+low)/3

  var float start_long_trade = 0.
  var float long_trades = 0.
  var float start_short_trade = 0.
  var float short_trades = 0.
  var int wins = 0
  var int trade_count = 0

  if startLongTrade
  start_long_trade := ohl3
  if endLongTrade
  trade_count := 1
  ldiff = (ohl3 – start_long_trade)
  wins := ldiff > 0 ? 1 : 0
  long_trades := ldiff * lot_size
  if startShortTrade
  start_short_trade := ohl3
  if endShortTrade
  trade_count := 1
  sdiff = (start_short_trade – ohl3)
  wins := sdiff > 0 ? 1 : 0
  short_trades := sdiff * lot_size

  cumreturn = cum(long_trades) + cum(short_trades) //– cumulative return
  totaltrades = cum(trade_count)
  totalwins = cum(wins)
  totallosses = totaltrades – totalwins == 0 ? 1 : totaltrades – totalwins

  //——————– Information

  show_info = input(true, ‘Show Info?’)

  var label lbl = na

  tbase = (time – time[1]) / 1000
  tcurr = (timenow – time_close[1]) / 1000

  info = ‘CR=’ + tostring(cumreturn, ‘#.#’)
  + ‘\nTrades: ‘ + tostring(totaltrades, ‘#’)
  + ‘\nWin/Loss: ‘ + tostring(totalwins/totallosses, ‘#.##’)
  + ‘\nWinrate: ‘ + tostring(totalwins/totaltrades, ‘#.#%’)
  + ‘\nBar Time: ‘ + tostring(tcurr / tbase, ‘#.#%’)

  if show_info and barstate.islast
  lbl := label.new(bar_index, close, info, xloc.bar_index, yloc.price,
  color.new(color.blue, 100), label.style_label_left, color.black, size.small, text.align_left)
  label.delete(lbl[1])

  

  NEOMKEY

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