Create npm-grunt.yml

[monjie110]

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[monjie110]

pinescript
//@Version=5
strategy("EURUSD 1min Strategy with Trade Statistics", overlay=false, initial_capital=1000)

// Define your custom parameters
rsi_length = input(5, title="RSI Length")
rsi_oversold = input(23, title="RSI Oversold Level")
rsi_overbought = input(72, title="RSI Overbought Level")
macd_fast_length = input(12, title="MACD Fast Length")
macd_slow_length = input(26, title="MACD Slow Length")
macd_signal_length = input(9, title="MACD Signal Length")
sar_start = input(0.02, title="SAR Start")
sar_increment = input(0.02, title="SAR Increment")
sar_maximum = input(0.2, title="SAR Maximum")
atr_period = input(14, title="ATR Period")
risk_percentage = input(2, title="Risk Percentage") / 100
equity_protector_percentage = input(1, title="Equity Protector Percentage") / 100
equity_protector_tp_percentage = input(2, title="Equity TP Percentage") / 100
tp_multiplier = input(5, title="Take Profit Multiplier")
sl_multiplier = input(5, title="Stop Loss Multiplier")
showTradeStats = input(true, title="Show Trade Statistics")

// Calculate ATR
atr_value = ta.atr(atr_period)

// Calculate equity stopout levels
equity_stopout_level = abs(strategy.openprofit / strategy.equity) > equity_protector_percentage
equity_tp_level = strategy.openprofit / strategy.equity > equity_protector_tp_percentage

// Calculate stop loss and take profit levels
stop_loss = atr_value * 100000 * sl_multiplier
take_profit = atr_value * 100000 * tp_multiplier

// Calculate trade size
risk_in_usd = balance * risk_percentage
lots = risk_in_usd / stop_loss
contracts = floor(lots * 100000)

// SAR Calculation
[sar, af] = ta.sar(sar_start, sar_increment, sar_maximum)

// RSI Conditions
rsi_value = ta.rsi(close, rsi_length)
rsi_cross_above = rsi_value > rsi_oversold
rsi_cross_below = rsi_value < rsi_overbought

// MACD Calculation
[macd_value, signal_value, _] = ta.macd(close, macd_fast_length, macd_slow_length, macd_signal_length)
fast_ma = ta.ema(close, macd_fast_length)
slow_ma = ta.ema(close, macd_slow_length)

// Strategy Conditions
uptrend = ta.highest(rsi_cross_above, 10) == 1
long_condition = uptrend and rsi_cross_above and fast_ma > slow_ma and macd_value > signal_value
short_condition = not uptrend and rsi_cross_below and fast_ma < slow_ma and macd_value < signal_value

// Entry and Exit
if long_condition
strategy.order("Long", strategy.long, contracts)
strategy.exit("Exit Long", "Long", stop=stop_loss, limit=take_profit)
if short_condition
strategy.order("Short", strategy.short, contracts)
strategy.exit("Exit Short", "Short", stop=stop_loss, limit=take_profit)

// Plotting SAR and Equity
plot(sar, style=plot.style_cross, linewidth=3, color=color.orange)
plot(sar[1], style=plot.style_cross, linewidth=3, color=color.aqua)
plot(strategy.equity, title="Equity", color=color.red, linewidth=2, style=plot.style_area)

// Trade Statistics
if showTradeStats
totalTrades = strategy.opentrades
totalWins = strategy.closedtrades.win
totalLosses = strategy.closedtrades.loss
totalEarlySignalFlips = strategy.closedtrades.early_signal_flip
winLossRatio = totalWins / totalLosses
winRate = totalWins / totalTrades
var tbl = ml.init_table()
update_table(tbl, "Trade Statistics", totalTrades, totalWins, totalLosses, winLossRatio, winRate, totalEarlySignalFlips)
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[monjie110]

// @Version=5 indicator('Machine Learning: Lorentzian Classification', 'Lorentzian Classification', true, precision=4, max_labels_count=500) import jdehorty/MLExtensions/2 as ml import jdehorty/KernelFunctions/2 as kernels type Settings float source int neighborsCount int maxBarsBack int featureCount int colorCompression bool showExits bool useDynamicExits type Label int long int short int neutral type FeatureArrays array f1 array f2 array f3 array f4 array f5 type FeatureSeries float f1 float f2 float f3 float f4 float f5 type MLModel int firstBarIndex array trainingLabels int loopSize float lastDistance array distancesArray array predictionsArray int prediction type FilterSettings bool useVolatilityFilter bool useRegimeFilter bool useAdxFilter float regimeThreshold int adxThreshold type Filter bool volatility bool regime bool adx // ========================== // ==== Helper Functions ==== // ========================== series_from(feature_string, _close, _high, _low, _hlc3, f_paramA, f_paramB) => switch feature_string "RSI" => ml.n_rsi(_close, f_paramA, f_paramB) "WT" => ml.n_wt(_hlc3, f_paramA, f_paramB) "CCI" => ml.n_cci(_close, f_paramA, f_paramB) "ADX" => ml.n_adx(_high, _low, _close, f_paramA) get_lorentzian_distance(int i, int featureCount, FeatureSeries featureSeries, FeatureArrays featureArrays) => switch featureCount 5 => math.log(1+math.abs(featureSeries.f1 - array.get(featureArrays.f1, i))) + math.log(1+math.abs(featureSeries.f2 - array.get(featureArrays.f2, i))) + math.log(1+math.abs(featureSeries.f3 - array.get(featureArrays.f3, i))) + math.log(1+math.abs(featureSeries.f4 - array.get(featureArrays.f4, i))) + math.log(1+math.abs(featureSeries.f5 - array.get(featureArrays.f5, i))) 4 => math.log(1+math.abs(featureSeries.f1 - array.get(featureArrays.f1, i))) + math.log(1+math.abs(featureSeries.f2 - array.get(featureArrays.f2, i))) + math.log(1+math.abs(featureSeries.f3 - array.get(featureArrays.f3, i))) + math.log(1+math.abs(featureSeries.f4 - array.get(featureArrays.f4, i))) 3 => math.log(1+math.abs(featureSeries.f1 - array.get(featureArrays.f1, i))) + math.log(1+math.abs(featureSeries.f2 - array.get(featureArrays.f2, i))) + math.log(1+math.abs(featureSeries.f3 - array.get(featureArrays.f3, i))) 2 => math.log(1+math.abs(featureSeries.f1 - array.get(featureArrays.f1, i))) + math.log(1+math.abs(featureSeries.f2 - array.get(featureArrays.f2, i))) // ================ // ==== Inputs ==== // ================ // Settings Object: General User-Defined Inputs Settings settings = Settings.new( input.source(title='Source', defval=close, group="General Settings", tooltip="Source of the input data"), input.int(title='Neighbors Count', defval=8, group="General Settings", minval=1, maxval=100, step=1, tooltip="Number of neighbors to consider"), input.int(title="Max Bars Back", defval=2000, group="General Settings"), input.int(title="Feature Count", defval=5, group="Feature Engineering", minval=2, maxval=5, tooltip="Number of features to use for ML predictions."), input.int(title="Color Compression", defval=1, group="General Settings", minval=1, maxval=10, tooltip="Compression factor for adjusting the intensity of the color scale."), input.bool(title="Show Default Exits", defval=false, group="General Settings", tooltip="Default exits occur exactly 4 bars after an entry signal. This corresponds to the predefined length of a trade during the model's training process.", inline="exits"), input.bool(title="Use Dynamic Exits", defval=false, group="General Settings", tooltip="Dynamic exits attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression logic.", inline="exits") ) // Trade Stats Settings // Note: The trade stats section is NOT intended to be used as a replacement for proper backtesting. It is intended to be used for calibration purposes only. showTradeStats = input.bool(true, 'Show Trade Stats', tooltip='Displays the trade stats for a given configuration. Useful for optimizing the settings in the Feature Engineering section. This should NOT replace backtesting and should be used for calibration purposes only. Early Signal Flips represent instances where the model changes signals before 4 bars elapses; high values can indicate choppy (ranging) market conditions.', group="General Settings") useWorstCase = input.bool(false, "Use Worst Case Estimates", tooltip="Whether to use the worst case scenario for backtesting. This option can be useful for creating a conservative estimate that is based on close prices only, thus avoiding the effects of intrabar repainting. This option assumes that the user does not enter when the signal first appears and instead waits for the bar to close as confirmation. On larger timeframes, this can mean entering after a large move has already occurred. Leaving this option disabled is generally better for those that use this indicator as a source of confluence and prefer estimates that demonstrate discretionary mid-bar entries. Leaving this option enabled may be more consistent with traditional backtesting results.", group="General Settings") // Settings object for user-defined settings FilterSettings filterSettings = FilterSettings.new( input.bool(title="Use Volatility Filter", defval=true, tooltip="Whether to use the volatility filter.", group="Filters"), input.bool(title="Use Regime Filter", defval=true, group="Filters", inline="regime"), input.bool(title="Use ADX Filter", defval=false, group="Filters", inline="adx"), input.float(title="Threshold", defval=-0.1, minval=-10, maxval=10, step=0.1, tooltip="Whether to use the trend detection filter. Threshold for detecting Trending/Ranging markets.", group="Filters", inline="regime"), input.int(title="Threshold", defval=20, minval=0, maxval=100, step=1, tooltip="Whether to use the ADX filter. Threshold for detecting Trending/Ranging markets.", group="Filters", inline="adx") ) // Filter object for filtering the ML predictions Filter filter = Filter.new( ml.filter_volatility(1, 10, filterSettings.useVolatilityFilter), ml.regime_filter(ohlc4, filterSettings.regimeThreshold, filterSettings.useRegimeFilter), ml.filter_adx(settings.source, 14, filterSettings.adxThreshold, filterSettings.useAdxFilter) ) // Feature Variables: User-Defined Inputs for calculating Feature Series. f1_string = input.string(title="Feature 1", options=["RSI", "WT", "CCI", "ADX"], defval="RSI", inline = "01", tooltip="The first feature to use for ML predictions.", group="Feature Engineering") f1_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 1.", defval=14, inline = "02", group="Feature Engineering") f1_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 2 (if applicable).", defval=1, inline = "02", group="Feature Engineering") f2_string = input.string(title="Feature 2", options=["RSI", "WT", "CCI", "ADX"], defval="WT", inline = "03", tooltip="The second feature to use for ML predictions.", group="Feature Engineering") f2_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 2.", defval=10, inline = "04", group="Feature Engineering") f2_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 2 (if applicable).", defval=11, inline = "04", group="Feature Engineering") f3_string = input.string(title="Feature 3", options=["RSI", "WT", "CCI", "ADX"], defval="CCI", inline = "05", tooltip="The third feature to use for ML predictions.", group="Feature Engineering") f3_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 3.", defval=20, inline = "06", group="Feature Engineering") f3_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 3 (if applicable).", defval=1, inline = "06", group="Feature Engineering") f4_string = input.string(title="Feature 4", options=["RSI", "WT", "CCI", "ADX"], defval="ADX", inline = "07", tooltip="The fourth feature to use for ML predictions.", group="Feature Engineering") f4_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 4.", defval=20, inline = "08", group="Feature Engineering") f4_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 4 (if applicable).", defval=2, inline = "08", group="Feature Engineering") f5_string = input.string(title="Feature 5", options=["RSI", "WT", "CCI", "ADX"], defval="RSI", inline = "09", tooltip="The fifth feature to use for ML predictions.", group="Feature Engineering") f5_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 5.", defval=9, inline = "10", group="Feature Engineering") f5_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 5 (if applicable).", defval=1, inline = "10", group="Feature Engineering") // FeatureSeries Object: Calculated Feature Series based on Feature Variables featureSeries = FeatureSeries.new( series_from(f1_string, close, high, low, hlc3, f1_paramA, f1_paramB), // f1 series_from(f2_string, close, high, low, hlc3, f2_paramA, f2_paramB), // f2 series_from(f3_string, close, high, low, hlc3, f3_paramA, f3_paramB), // f3 series_from(f4_string, close, high, low, hlc3, f4_paramA, f4_paramB), // f4 series_from(f5_string, close, high, low, hlc3, f5_paramA, f5_paramB) // f5 ) // FeatureArrays Variables: Storage of Feature Series as Feature Arrays Optimized for ML // Note: These arrays cannot be dynamically created within the FeatureArrays Object Initialization and thus must be set-up in advance. var f1Array = array.new_float() var f2Array = array.new_float() var f3Array = array.new_float() var f4Array = array.new_float() var f5Array = array.new_float() array.push(f1Array, featureSeries.f1) array.push(f2Array, featureSeries.f2) array.push(f3Array, featureSeries.f3) array.push(f4Array, featureSeries.f4) array.push(f5Array, featureSeries.f5) // FeatureArrays Object: Storage of the calculated FeatureArrays into a single object featureArrays = FeatureArrays.new( f1Array, // f1 f2Array, // f2 f3Array, // f3 f4Array, // f4 f5Array // f5 ) // Label Object: Used for classifying historical data as training data for the ML Model Label direction = Label.new( long=1, short=-1, neutral=0 ) // Derived from General Settings maxBarsBackIndex = last_bar_index >= settings.maxBarsBack ? last_bar_index - settings.maxBarsBack : 0 // EMA Settings useEmaFilter = input.bool(title="Use EMA Filter", defval=false, group="Filters", inline="ema") emaPeriod = input.int(title="Period", defval=200, minval=1, step=1, group="Filters", inline="ema", tooltip="The period of the EMA used for the EMA Filter.") isEmaUptrend = useEmaFilter ? close > ta.ema(close, emaPeriod) : true isEmaDowntrend = useEmaFilter ? close < ta.ema(close, emaPeriod) : true useSmaFilter = input.bool(title="Use SMA Filter", defval=false, group="Filters", inline="sma") smaPeriod = input.int(title="Period", defval=200, minval=1, step=1, group="Filters", inline="sma", tooltip="The period of the SMA used for the SMA Filter.") isSmaUptrend = useSmaFilter ? close > ta.sma(close, smaPeriod) : true isSmaDowntrend = useSmaFilter ? close < ta.sma(close, smaPeriod) : true // Nadaraya-Watson Kernel Regression Settings useKernelFilter = input.bool(true, "Trade with Kernel", group="Kernel Settings", inline="kernel") showKernelEstimate = input.bool(true, "Show Kernel Estimate", group="Kernel Settings", inline="kernel") useKernelSmoothing = input.bool(false, "Enhance Kernel Smoothing", tooltip="Uses a crossover based mechanism to smoothen kernel color changes. This often results in less color transitions overall and may result in more ML entry signals being generated.", inline='1', group='Kernel Settings') h = input.int(8, 'Lookback Window', minval=3, tooltip='The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50', group="Kernel Settings", inline="kernel") r = input.float(8., 'Relative Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Settings", inline="kernel") x = input.int(25, "Regression Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Settings", inline="kernel") lag = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='1', group='Kernel Settings') // Display Settings showBarColors = input.bool(true, "Show Bar Colors", tooltip="Whether to show the bar colors.", group="Display Settings") showBarPredictions = input.bool(defval = true, title = "Show Bar Prediction Values", tooltip = "Will show the ML model's evaluation of each bar as an integer.", group="Display Settings") useAtrOffset = input.bool(defval = false, title = "Use ATR Offset", tooltip = "Will use the ATR offset instead of the bar prediction offset.", group="Display Settings") barPredictionsOffset = input.float(0, "Bar Prediction Offset", minval=0, tooltip="The offset of the bar predictions as a percentage from the bar high or close.", group="Display Settings") // ================================= // ==== Next Bar Classification ==== // ================================= // This model specializes specifically in predicting the direction of price action over the course of the next 4 bars. // To avoid complications with the ML model, this value is hardcoded to 4 bars but support for other training lengths may be added in the future. src = settings.source y_train_series = src[4] < src[0] ? direction.short : src[4] > src[0] ? direction.long : direction.neutral var y_train_array = array.new_int(0) // Variables used for ML Logic var predictions = array.new_float(0) var prediction = 0. var signal = direction.neutral var distances = array.new_float(0) array.push(y_train_array, y_train_series) lastDistance = -1.0 size = math.min(settings.maxBarsBack-1, array.size(y_train_array)-1) sizeLoop = math.min(settings.maxBarsBack-1, size) if bar_index >= maxBarsBackIndex //{ for i = 0 to sizeLoop //{ d = get_lorentzian_distance(i, settings.featureCount, featureSeries, featureArrays) if d >= lastDistance and i%4 //{ lastDistance := d array.push(distances, d) array.push(predictions, math.round(array.get(y_train_array, i))) if array.size(predictions) > settings.neighborsCount //{ lastDistance := array.get(distances, math.round(settings.neighborsCount3/4)) array.shift(distances) array.shift(predictions) //} //} //} prediction := array.sum(predictions) //} // ============================ // ==== Prediction Filters ==== // ============================ // User Defined Filters: Used for adjusting the frequency of the ML Model's predictions filter_all = filter.volatility and filter.regime and filter.adx // Filtered Signal: The model's prediction of future price movement direction with user-defined filters applied signal := prediction > 0 and filter_all ? direction.long : prediction < 0 and filter_all ? direction.short : nz(signal[1]) // Bar-Count Filters: Represents strict filters based on a pre-defined holding period of 4 bars var int barsHeld = 0 barsHeld := ta.change(signal) ? 0 : barsHeld + 1 isHeldFourBars = barsHeld == 4 isHeldLessThanFourBars = 0 < barsHeld and barsHeld < 4 // Fractal Filters: Derived from relative appearances of signals in a given time series fractal/segment with a default length of 4 bars isDifferentSignalType = ta.change(signal) isEarlySignalFlip = ta.change(signal) and (ta.change(signal[1]) or ta.change(signal[2]) or ta.change(signal[3])) isBuySignal = signal == direction.long and isEmaUptrend and isSmaUptrend isSellSignal = signal == direction.short and isEmaDowntrend and isSmaDowntrend isLastSignalBuy = signal[4] == direction.long and isEmaUptrend[4] and isSmaUptrend[4] isLastSignalSell = signal[4] == direction.short and isEmaDowntrend[4] and isSmaDowntrend[4] isNewBuySignal = isBuySignal and isDifferentSignalType isNewSellSignal = isSellSignal and isDifferentSignalType // Kernel Regression Filters: Filters based on Nadaraya-Watson Kernel Regression using the Rational Quadratic Kernel // For more information on this technique refer to my other open source indicator located here: // https://www.tradingview.com/script/AWNvbPRM-Nadaraya-Watson-Rational-Quadratic-Kernel-Non-Repainting/ c_green = color.new(#009988, 20) c_red = color.new(#CC3311, 20) transparent = color.new(#000000, 100) yhat1 = kernels.rationalQuadratic(settings.source, h, r, x) yhat2 = kernels.gaussian(settings.source, h-lag, x) kernelEstimate = yhat1 // Kernel Rates of Change bool wasBearishRate = yhat1[2] > yhat1[1] bool wasBullishRate = yhat1[2] < yhat1[1] bool isBearishRate = yhat1[1] > yhat1 bool isBullishRate = yhat1[1] < yhat1 isBearishChange = isBearishRate and wasBullishRate isBullishChange = isBullishRate and wasBearishRate // Kernel Crossovers bool isBullishCrossAlert = ta.crossover(yhat2, yhat1) bool isBearishCrossAlert = ta.crossunder(yhat2, yhat1) bool isBullishSmooth = yhat2 >= yhat1 bool isBearishSmooth = yhat2 <= yhat1 // Kernel Colors color colorByCross = isBullishSmooth ? c_green : c_red color colorByRate = isBullishRate ? c_green : c_red color plotColor = showKernelEstimate ? (useKernelSmoothing ? colorByCross : colorByRate) : transparent plot(kernelEstimate, color=plotColor, linewidth=2, title="Kernel Regression Estimate") // Alert Variables bool alertBullish = useKernelSmoothing ? isBullishCrossAlert : isBullishChange bool alertBearish = useKernelSmoothing ? isBearishCrossAlert : isBearishChange // Bullish and Bearish Filters based on Kernel isBullish = useKernelFilter ? (useKernelSmoothing ? isBullishSmooth : isBullishRate) : true isBearish = useKernelFilter ? (useKernelSmoothing ? isBearishSmooth : isBearishRate) : true // =========================== // ==== Entries and Exits ==== // =========================== // Entry Conditions: Booleans for ML Model Position Entries startLongTrade = isNewBuySignal and isBullish and isEmaUptrend and isSmaUptrend startShortTrade = isNewSellSignal and isBearish and isEmaDowntrend and isSmaDowntrend // Dynamic Exit Conditions: Booleans for ML Model Position Exits based on Fractal Filters and Kernel Regression Filters lastSignalWasBullish = ta.barssince(startLongTrade) < ta.barssince(startShortTrade) lastSignalWasBearish = ta.barssince(startShortTrade) < ta.barssince(startLongTrade) barsSinceRedEntry = ta.barssince(startShortTrade) barsSinceRedExit = ta.barssince(alertBullish) barsSinceGreenEntry = ta.barssince(startLongTrade) barsSinceGreenExit = ta.barssince(alertBearish) isValidShortExit = barsSinceRedExit > barsSinceRedEntry isValidLongExit = barsSinceGreenExit > barsSinceGreenEntry endLongTradeDynamic = (isBearishChange and isValidLongExit[1]) endShortTradeDynamic = (isBullishChange and isValidShortExit[1]) // Fixed Exit Conditions: Booleans for ML Model Position Exits based on a Bar-Count Filters endLongTradeStrict = ((isHeldFourBars and isLastSignalBuy) or (isHeldLessThanFourBars and isNewSellSignal and isLastSignalBuy)) and startLongTrade[4] endShortTradeStrict = ((isHeldFourBars and isLastSignalSell) or (isHeldLessThanFourBars and isNewBuySignal and isLastSignalSell)) and startShortTrade[4] isDynamicExitValid = not useEmaFilter and not useSmaFilter and not useKernelSmoothing endLongTrade = settings.useDynamicExits and isDynamicExitValid ? endLongTradeDynamic : endLongTradeStrict endShortTrade = settings.useDynamicExits and isDynamicExitValid ? endShortTradeDynamic : endShortTradeStrict // ========================= // ==== Plotting Labels ==== // ========================= // Note: These will not repaint once the most recent bar has fully closed. By default, signals appear over the last closed bar; to override this behavior set offset=0. plotshape(startLongTrade ? low : na, 'Buy', shape.labelup, location.belowbar, color=ml.color_green(prediction), size=size.small, offset=0) plotshape(startShortTrade ? high : na, 'Sell', shape.labeldown, location.abovebar, ml.color_red(-prediction), size=size.small, offset=0) plotshape(endLongTrade and settings.showExits ? high : na, 'StopBuy', shape.xcross, location.absolute, color=#3AFF17, size=size.tiny, offset=0) plotshape(endShortTrade and settings.showExits ? low : na, 'StopSell', shape.xcross, location.absolute, color=#FD1707, size=size.tiny, offset=0) // ================ // ==== Alerts ==== // ================ // Separate Alerts for Entries and Exits alertcondition(startLongTrade, title='Open Long ?', message='LDC Open Long ? | {{ticker}}@{{close}} | ({{interval}})') alertcondition(endLongTrade, title='Close Long ?', message='LDC Close Long ? | {{ticker}}@{{close}} | ({{interval}})') alertcondition(startShortTrade, title='Open Short ?', message='LDC Open Short | {{ticker}}@{{close}} | ({{interval}})') alertcondition(endShortTrade, title='Close Short ?', message='LDC Close Short ? | {{ticker}}@{{close}} | ({{interval}})') // Combined Alerts for Entries and Exits alertcondition(startShortTrade or startLongTrade, title='Open Position ??', message='LDC Open Position ?? | {{ticker}}@{{close}} | ({{interval}})') alertcondition(endShortTrade or endLongTrade, title='Close Position ??', message='LDC Close Position ?? | {{ticker}}@[{{close}}] | ({{interval}})') // Kernel Estimate Alerts alertcondition(condition=alertBullish, title='Kernel Bullish Color Change', message='LDC Kernel Bullish ? | {{ticker}}@{{close}} | ({{interval}})') alertcondition(condition=alertBearish, title='Kernel Bearish Color Change', message='LDC Kernel Bearish ? | {{ticker}}@{{close}} | ({{interval}})') // ========================= // ==== Display Signals ==== // ========================= atrSpaced = useAtrOffset ? ta.atr(1) : na compressionFactor = settings.neighborsCount / settings.colorCompression c_pred = prediction > 0 ? color.from_gradient(prediction, 0, compressionFactor, #787b86, #009988) : prediction <= 0 ? color.from_gradient(prediction, -compressionFactor, 0, #CC3311, #787b86) : na c_label = showBarPredictions ? c_pred : na c_bars = showBarColors ? color.new(c_pred, 50) : na x_val = bar_index y_val = useAtrOffset ? prediction > 0 ? high + atrSpaced: low - atrSpaced : prediction > 0 ? high + hl2barPredictionsOffset/20 : low - hl2*barPredictionsOffset/30 label.new(x_val, y_val, str.tostring(prediction), xloc.bar_index, yloc.price, color.new(color.white, 100), label.style_label_up, c_label, size.normal, text.align_left) barcolor(showBarColors ? color.new(c_pred, 50) : na) // ===================== // ==== Backtesting ==== // ===================== // The following can be used to stream signals to a backtest adapter backTestStream = switch startLongTrade => 1 endLongTrade => 2 startShortTrade => -1 endShortTrade => -2 plot(backTestStream, "Backtest Stream", display=display.none) // The following can be used to display real-time trade stats. This can be a useful mechanism for obtaining real-time feedback during Feature Engineering. This does NOT replace the need to properly backtest. // Note: In this context, a "Stop-Loss" is defined instances where the ML Signal prematurely flips directions before an exit signal can be generated. [totalWins, totalLosses, totalEarlySignalFlips, totalTrades, tradeStatsHeader, winLossRatio, winRate] = ml.backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isEarlySignalFlip, maxBarsBackIndex, bar_index, settings.source, useWorstCase) init_table() => c_transparent = color.new(color.black, 100) table.new(position.top_right, columns=2, rows=7, frame_color=color.new(color.black, 100), frame_width=1, border_width=1, border_color=c_transparent) update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winRate, stopLosses) => c_transparent = color.new(color.black, 100) table.cell(tbl, 0, 0, tradeStatsHeader, text_halign=text.align_center, text_color=color.gray, text_size=size.normal) table.cell(tbl, 0, 1, 'Winrate', text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) table.cell(tbl, 1, 1, str.tostring(totalWins / totalTrades, '#.#%'), text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) table.cell(tbl, 0, 2, 'Trades', text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) table.cell(tbl, 1, 2, str.tostring(totalTrades, '#') + ' (' + str.tostring(totalWins, '#') + '|' + str.tostring(totalLosses, '#') + ')', text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) table.cell(tbl, 0, 5, 'WL Ratio', text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) table.cell(tbl, 1, 5, str.tostring(totalWins / totalLosses, '0.00'), text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) table.cell(tbl, 0, 6, 'Early Signal Flips', text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) table.cell(tbl, 1, 6, str.tostring(totalEarlySignalFlips, '#'), text_halign=text.align_center, bgcolor=c_transparent, text_color=color.gray, text_size=size.normal) if showTradeStats var tbl = ml.init_table() if barstate.islast update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winRate, totalEarlySignalFlips)