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FunctionNNPerceptronLibrary "FunctionNNPerceptron" Perceptron Function for Neural networks. function(inputs, weights, bias, activation_function, alpha, scale) generalized perceptron node for Neural Networks.   Parameters:      inputs : float array, the inputs of the perceptron.      weights : float array, the weights for inputs.      bias : float, default=1.0, the default bias of the perceptron.      activation_function : string, default='sigmoid', activation function applied to the output.      alpha : float, default=na, if required for activation.      scale : float, default=na, if required for activation. @outputs float
Libreria Pine Script®
di RicardoSantos
1
MLActivationFunctionsLibrary "MLActivationFunctions" Activation functions for Neural networks. binary_step(value) Basic threshold output classifier to activate/deactivate neuron.   Parameters:      value : float, value to process.   Returns: float linear(value) Input is the same as output.   Parameters:      value : float, value to process.   Returns: float sigmoid(value) Sigmoid or logistic function.   Parameters:      value : float, value to process.   Returns: float sigmoid_derivative(value) Derivative of sigmoid function.   Parameters:      value : float, value to process.   Returns: float tanh(value) Hyperbolic tangent function.   Parameters:      value : float, value to process.   Returns: float tanh_derivative(value) Hyperbolic tangent function derivative.   Parameters:      value : float, value to process.   Returns: float relu(value) Rectified linear unit (RELU) function.   Parameters:      value : float, value to process.   Returns: float relu_derivative(value) RELU function derivative.   Parameters:      value : float, value to process.   Returns: float leaky_relu(value) Leaky RELU function.   Parameters:      value : float, value to process.   Returns: float leaky_relu_derivative(value) Leaky RELU function derivative.   Parameters:      value : float, value to process.   Returns: float relu6(value) RELU-6 function.   Parameters:      value : float, value to process.   Returns: float softmax(value) Softmax function.   Parameters:      value : float array, values to process.   Returns: float softplus(value) Softplus function.   Parameters:      value : float, value to process.   Returns: float softsign(value) Softsign function.   Parameters:      value : float, value to process.   Returns: float elu(value, alpha) Exponential Linear Unit (ELU) function.   Parameters:      value : float, value to process.      alpha : float, default=1.0, predefined constant, controls the value to which an ELU saturates for negative net inputs. .   Returns: float selu(value, alpha, scale) Scaled Exponential Linear Unit (SELU) function.   Parameters:      value : float, value to process.      alpha : float, default=1.67326324, predefined constant, controls the value to which an SELU saturates for negative net inputs. .      scale : float, default=1.05070098, predefined constant.   Returns: float exponential(value) Pointer to math.exp() function.   Parameters:      value : float, value to process.   Returns: float function(name, value, alpha, scale) Activation function.   Parameters:      name : string, name of activation function.      value : float, value to process.      alpha : float, default=na, if required.      scale : float, default=na, if required.   Returns: float derivative(name, value, alpha, scale) Derivative Activation function.   Parameters:      name : string, name of activation function.      value : float, value to process.      alpha : float, default=na, if required.      scale : float, default=na, if required.   Returns: float
Libreria Pine Script®
di RicardoSantos
2
MLLossFunctionsLibrary "MLLossFunctions" Methods for Loss functions. mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum ((y - y')^2) ".   Parameters:      expects : float array, expected values.      predicts : float array, prediction values.   Returns: float binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log).   Parameters:      expects : float array, expected values.      predicts : float array, prediction values.   Returns: float
Libreria Pine Script®
di RicardoSantos
Market bars sentimentBonjour, j'ai écrit cette indicateur des sentiments de pour avoir une rapide aperçu de marche Voici le code couleurs: -barres de couleur rouge signalons la vent panique( pour certain opportunité) -barres de couleur vert fonce signalons la positivité, début de dominance des acheteurs au la stabilisation des cours âpres les achats euphorique -barres de couleur lime signalons éphorie des acheteurs sur le marche. L’indicateur est adopter pour étudier le marche de cryptomonnaie,Indice boursier au actions en particulier.
Indicatore Pine Script®
di DRAWA2009
Combo 4+ KDJ STO RSI EMA3 Visual Trend Pine V5@RL! English ! Combo 4+ KDJ STO RSI EMA3 Visual Trend Pine V5 @ RL Combo 4+ KDJ STO RSI EMA3 Visual Trend Pine V5 @ RL is a visual trend following indicator that groups and combines four trend following indicators. It is compiled in PINE Script Version V5 language. • STOCH: Stochastic oscillator. • RSI Divergence: Relative Strength Index Divergence. RSI Divergence is a difference between a fast and a slow RSI. • KDJ: KDJ Indicator. (trend following indicator). • EMA Triple: 3 exponential moving averages (Default display). This indicator is intended to help beginners (and also the more experienced ones) to trade in the right direction of the market trend. It allows you to avoid the mistakes of always trading against the trend. The calculation codes of the different indicators used are standard public codes used in the usual TradingView coding for these indicators. The STO indicator calculation script is taken from TradingView's standard STOCH calculation. The RSI indicator calculation script is a replica of the one created by @Shizaru. The KDJ indicator calculation script is a replica of the one created by @iamaltcoin. The Triple EMA indicator calculation script is a replica of the one created by @jwilcharts. This indicator can be configured to your liking. It can even be used several times on the same graph (multi-instance), with different configurations or display of another indicator among the four that compose it, according to your needs or your tastes. A single plot, among the 4 indicators that make it up, can be displayed at a time, but either with its own trend or with the trend of the 4 (3 by default) combined indicators (sell=green or buy=red, background color). Trend indications (potential sell or buy areas) are displayed as a background color (bullish: green or bearish: red) when at least three of the four indicators (3 by default and configurable from 1 to 4) assume that the market is moving in the same direction. These trend indications can be configured and displayed, either only for the signal of the selected indicator and displayed, or for the signals of the four indicators together and combined (logical AND). You can tune the input, style and visibility settings of each indicator to match your own preferences or habits. A 'buy stop' or 'sell stop' signal is displayed (layouts) in the form of a colored square (green for 'stop buy' and red for 'stop sell'. These 'stop' signals can be configured and displayed, either only for the indicator chosen, or for the four indicators together and combined (logical OR). Note that the presence of a Stop Long signal cancels the background color of the Long trend (green). Likewise, the presence of a Stop Short signal cancels out the background color of the Short trend (red). It is also made up of 3 labels: • Trend Label • signal Stop Label (signals Stop buy or sell ) • Info Label (Names of Long / Short / Stop Long / Stop Short indicators, and / Open / Close / High / Low ). Each label is configurable (visibility and position on the graph). • Trend label: indicates the number of indicators suggesting the same trend (Long or Short) as well as a strength index (PWR) of this trend: For example: 3 indicators in Short trend, 1 indicator in Long trend and 1 indicator in neutral trend will give: PWR SHORT = 2/4. (3 Short indicators - 1 Long indicator = 2 Pwr Short). And if PWR = 0 then the display is "Wait and See". It also indicates which current indicator is displayed and the display mode used (combined 1 to 4 indicators or not combined ). • Signal Stop Label: Indicates a possible stop of the current trend. • Label Info (Simple or Full) gives trend info for each of the 4 indicators and OHLC info for the chart (in “Full” mode). It is possible to display this indicator several times on a chart (up to 3 indicators max with the Basic TradingView Plan and more with the paid plans), with different configurations: For example: • 1-Stochastic - 2/4 Combined Signals - no Label displayed • 1-RSI - Combined Signals 3/4 - Stop Label only displayed • 1-KDJ - Combined Signals 4/4 - the 3 Labels displayed • 1-EMA'3 - Non-combined signals (EMA only) - Trend Label displayed Some indicators have filters / thresholds that can be configured according to your convenience and experience! The choice of indicator colors is suitable for a graph with a "dark" theme, which you will probably need to modify for visual comfort, if you are using a "Light" mode or a custom mode. This script is an indicator that you can run on standard chart types. It also works on non-standard chart types but the results will be skewed and different. Non-standard charts are: • Heikin Ashi (HA) • Renko • Kagi • Point & Figure • Range As a reminder: No indicator is capable of providing accurate signals 100% of the time. Every now and then, even the best will fail, leaving you with a losing deal. Whichever indicator you base yourself on, remember to follow the basic rules of risk management and capital allocation. BINANCE:BTCUSDT **********************************************************************************************************************************************************************************************************************************************************************************  ! Français ! Combo 4+ KDJ STO RSI EMA3 Visual Trend Pine V5@RL Combo 4+ KDJ STO RSI EMA3 Visual Trend Pine V5@RL est un indicateur visuel de suivi de tendance qui regroupe et combine quatre indicateurs de suivi de tendance. Il est compilé en langage PINE Script Version V5. • STOCH : Stochastique. • RSI Divergence : Relative Strength Index Divergence. La Divergence RSI est une différence entre un RSI rapide et un RSI lent. • KDJ : KDJ Indicateur. (indicateur de suivi de tendance). • EMA Triple : 3 moyennes mobiles exponentielles (Affichage par défaut). Cet indicateur est destiné à aider les débutants (et aussi les plus confirmé) à trader à dans le bon sens de la tendance du marché. Il permet d'éviter les erreurs qui consistent à toujours trader à contre tendance. Les codes de calcul des différents indicateurs utilisés sont des codes publics standards utilisés dans le codage habituel de TradingView pour ces indicateurs ! Le script de calcul de l’indicateur STO est issu du calcul standard du STOCH de TradingView. Le script de calcul de l’indicateur RSI Div est une réplique de celui créé par @Shizaru. Le script de calcul de l’indicateur KDJ est une réplique de celui créé par @iamaltcoin. Le script de calcul de l’indicateur Triple EMA est une réplique de celui créé par @jwilcharts Cet indicateur peut être configuré à votre convenance. Il peut même être utilisé plusieurs fois sur le même graphique (multi-instance), avec des configurations différentes ou affichage d’un autre indicateur parmi les quatre qui le composent, selon vos besoins ou vos goûts. Un seul tracé, parmi les 4 indicateurs qui le composent, peut être affiché à la fois mais, soit avec sa propre tendance soit avec la tendance des 4 (3 par défaut) indicateurs combinés (couleur de fond vente=vert ou achat=rouge). Les indications de tendance (zones de vente ou d’achat potentielles) sont affichés sous la forme de couleur de fond (Haussier : vert ou baissier : rouge) lorsque au moins trois des quatre indicateurs (3 par défaut et configurable de 1 à 4) supposent que le marché évolue dans la même direction. Ces indications de tendance peuvent être configuré et affichés, soit uniquement pour le signal de l’indicateur choisi et affiché, soit pour les signaux des quatre indicateurs ensemble et combinés (ET logique). Vous pouvez accorder les paramètres d’entrée, de style et de visibilité de chacun des indicateurs pour correspondre à vos propres préférences ou habitudes. Un signal ‘stop achat’ ou ‘stop vente’ est affiché (layouts) sous la forme d’un carré de couleur (vert pour ‘stop achat’ et rouge pour ‘stop vente’. Ces signaux ‘stop’ peuvent être configuré et affichés, soit uniquement pour l’indicateur choisi, soit pour les quatre indicateurs ensemble et combinés (OU logique). A noter que la présence d’un signal Stop Long annule la couleur de fond de la tendance Long (vert). De même, la présence d’un signal Stop Short annule la couleur de fond de la tendance Short (rouge). Il est aussi composé de 3 étiquettes (Labels) : • Trend Label (infos de tendance) • Signal Stop Label (signaux « Stop » achat ou vente) • Infos Label (Noms des indicateurs Long/Short/Stop Long/Stop Short, et /Open/Close/High/Low ) Chaque label est configurable (visibilité et position sur le graphique). • Label Trend : indique le nombre d’indicateurs suggérant une même tendance (Long ou Short) ainsi qu’un indice de force (PWR) de cette tendance : Par exemple : 3 indicateurs en tendance Short, 1 indicateur en tendance Long et 1 indicateur en tendance neutre donnera : PWR SHORT = 2/4. (3 indicateurs Short – 1 indicateur Long=2 Pwr Short). Et si PWR=0 alors l’affichage est « Wait and See » (Attendre et Observer). Il indique aussi quel indicateur actuel est affiché et le mode d’affichage utilisé (combiné 1 à 4 indicateurs ou non combiné ). • Signal Stop Label : Indique un possible arrêt de la tendance en cours. • Infos Label (Simple ou complet) donne les infos de tendance de chacun des 4 indicateurs et les infos OHLC du graphique (en mode « Complet »). Il est possible d’afficher ce même indicateur plusieurs fois sur un graphique (jusqu’à 3 indicateurs max avec le Plan Basic TradingView et plus avec les plans payants), avec des configurations différentes : Par exemple : • 1-Stochastique – Signaux Combinés 2/4 – aucun Label affiché • 1-RSI – Signaux Combinés 3/4 – Label Stop uniquement affiché • 1-KDJ – Signaux Combinés 4/4 – les 3 Labels affichés • 1-EMA’3 - Signaux Non combinés (EMA seuls) – Trend Label affiché Certains indicateurs ont des filtres/seuils (Thresholds) configurables selon votre convenance et votre expérience ! Le choix des couleurs de l’indicateur est adapté pour un graphique avec thème « sombre », qu’il vous faudra probablement modifier pour le confort visuel, si vous utilisez un mode « Clair » ou un mode personnalisé. Ce script est un indicateur que vous pouvez exécuter sur des types de graphiques standard. Il fonctionne aussi sur des types de graphiques non-standard mais les résultats seront faussés et différents. Les graphiques Non-standard sont : • Heikin Ashi (HA) • Renko • Kagi • Point & Figure • Range Pour rappel : Aucun indicateur n’est capable de fournir des signaux précis 100% du temps. De temps en temps, même les meilleurs échoueront, vous laissant avec une affaire perdante. Quel que soit l’indicateur sur lequel vous vous basez, n’oubliez pas de suivre les règles de base de gestion des risques et de répartition du capital. BINANCE:BTCUSDT
Indicatore Pine Script®
di RegisL76
1
neutronix community bot ML + Alerts 4h-daily (mod. capissimo)Gm traders, i have been a python programmer for some years studying artificial intelligence for general purpose; after some time i finally decided to have a look at some finance related stuff and scripts. Moved by curiosity i've decided to make some but decisive modifications to a script i tried to use initially but without success: the LVQ machine learning strategy. So after studying the charts and indicators, i have rewritten this script made by Capissimo and added heavy filtering thanks to vwap and vwma, then fixed repaint and other issues. I hope you enjoy it and that it could increase your possibilities of success in trading. HOW TO USE THE SCRIPT Add the script to 3h+ charts like for example BTC 4h, 6h, 8h, 12h, daily. (In order for it to work on shorter timeframes charts you can try to change to lookback window but i dont advise it). Change only rsi and volfilter(volume filtering) settings to try to find the best winrate. Leave dataset to open. Fyi the winrate isn't 100% accurate but can give you a raw vision of final results. Use alerts included for trading and and in options click on 'Once per bar'. If you have checked 'Reverse Signals' in the control panel you have got more 'risky' signals so be advised if trading futures and stocks. Exit trade signals not provided, so it is recommended the use of take profits and stop loss (1.5:1 ratio) As always, the script is for study purposes. Do not risk more than you can spend! Original LVQ-based strategy made by capissimo Modified by gravisxv 13/10/2021
Indicatore Pine Script®
di gravisxv
92
FOTSI - Open sourceI WOULD LIKE TO SPECIFY TWO THINGS: - The indicator was absolutely not designed by me, I do not take any credit and much less I want them, I am just making this fantastic indicator open source and accessible to all - The script code was not recycled from other indicators, but was created from 0 following the theory behind it to the letter, thus avoiding copyright infringement - Advices and improvements are accepted, as having very little programming experience in Pine Script I consider this work still rough and slow WHAT IS THE FOTSI? The FOTSI is an oscillator that measures the relative strength of the individual currencies that make up the 28 major Forex exchanges. By identifying the currencies that are in the overbought (+50) and oversold (-50) areas, it is possible to anticipate the correction of a currency pair following a strong trend. THE THEORY BEHIND 1) At the base of everything is the 1-period momentum (close-open) of the single currency pairs that contain a certain currency. For example, the momentum of the USD currency is composed of all the exchange rates that contain the US dollar inside it: mom_usd = - mom_eurusd - mom_gbpusd + mom_usdchf + mom_usdjpy - mom_audusd + mom_usdcad - mom_nzdusd. Where the base currency is in second position, the momentum is subtracted instead of adding it. 2) The IST formula is applied to the momentum of the individual currencies obtained. In this way we get an oscillator that oscillates between 0 and its overbought and oversold areas. The area between +25 and -25 is an area in which we can consider the movements of individual currencies to be neutral. 3) The TSI is nothing more than a double smoothing on the momentum of individual currencies. This particularity makes the indicator very reactive, minimizing the delays of the trend reversal. HOW TO USE 1) A currency is identified that is in the overbought (+50) or oversold (-50) area. Example GBP = 50 2) The second currency is identified as the one most opposite to the first. Example USD = -25 3) The currency pair consisting of the two currencies opens. So GBP / USD 4) Considering that GBP is oversold, we anticipate its future devaluation. So in this case we are short on GBP / SUD. Otherwise if GBP had been oversold (-50) we expect its future valuation and therefore we enter long. 5) It is used on the H1, H4 and D1 timeframes 6) Closing conditions: the position on the 50-period exponential moving average is split / the position at target on the 100-period exponential moving average is closed 7) Stoploss: it is recommended not to use it, if you want to use it it is equivalent to 5 times the ATR on the reference timeframe 8) Position sizing: go very slow! Being a counter-trend strategy, it is very risky to position yourself heavily. Use common sense in everything! 9) To insert the alerts that warn you of an overbought and oversold condition, it is necessary to enter the signals called "Overbought Signal" and "Oversold Signal" for each chart used, in the specific Trading View window. like me using multiple charts in the same window. I hope you enjoy my work. For any questions write in the comments. Thanks <3 //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- TENGO A PRECISARE DUE COSE: - L'indicatore non è stato assolutamente ideato da me, non mi assumo nessun merito e tanto meno li voglio, io sto solo rendendo questo fantastico indicatore open source ed accessibile a tutti - Il codice dello script non è stato riciclato da altri indicatori, ma è stato creato da 0 seguendo alla lettere la teoria che sta alla sua base, evitando così di violare il copyright - Si accettano consigli e migliorie, visto che avendo pochissima esperienza di programmazione in Pine Script considero questo lavoro ancora grezzo e lento COS'È IL FOTSI? Il FOTSI è un oscillatore che misura la forza relativa delle singole valute che compongono i 28 cambi major del Forex. Individuando le valute che si trovano nelle aree di ipercomprato (+50) ed ipervenduto (-50) , è possibile anticipare la correzione di una coppia valutaria al seguito di un forte trend. LA TEORIA ALLA BASE 1) Alla base di tutto c'è il momentum ad 1 periodo (close-open) delle singole coppie valutarie che contengono una determinata valuta. Ad esempio il momentum della valuta USD è composto da tutti i cambi che contengono il dollaro americano al suo interno: mom_usd = - mom_eurusd - mom_gbpusd + mom_usdchf + mom_usdjpy - mom_audusd + mom_usdcad - mom_nzdusd . Ove la valuta base si trova in seconda posizione si sottrae il momentum al posto che sommarlo. 2) Si applica la formula del TSI ai momentum delle singole valute ottenute. In questo modo otteniamo un oscillatore che oscilla tra lo 0 e le sue aree di ipercomprato ed ipervenduto. L'area compresa tra +25 e -25 è un area in cui possiamo considerare neutri i movimenti delle singole valute. 3) Il TSI non è altro che un doppio smoothing sul momentum delle singole valute. Questa particolarità rende l'indicatore molto reattivo, minimizzando i ritardi dell'inversione del trend. COME SI USA 1) Si individua una valuta che si trova nell'area di ipercomprato (+50) o ipervenduto (-50) . Esempio GBP = 50 2) Si individua come seconda valuta quella più opposta alla prima. Esempio USD = -25 3) Si apre la coppia di valuta composta dalle due valute. Quindi GBP/USD 4) Considerando che GBP è in fase di ipervenduto prevediamo una sua futura svalutazione. Quindi in questo caso entriamo short su GBP/SUD. Diversamente se GBP fosse stato in fase di ipervenduto (-50) ci aspettiamo una sua futura valutazione e quindi entriamo long. 5) Si usa sui timeframe H1, H4 e D1 6) Condizioni di chiusura: si smezza la posizione sulla media mobile esponenziale a 50 periodi / si chiude la posizione a target sulla media mobile esponenziale a 100 periodi 7) Stoploss: è consigliato non usarlo, nel caso lo si voglia utilizzare esso equivale a 5 volte l'ATR sul timeframe di riferimento 8) Position sizing: andateci molto piano! Essendo una strategia contro trend è molto rischioso posizionarsi in modo pesante. Usate il buonsenso in tutto! 9) Per inserire gli allert che ti avvertono di una condizione di ipercomprato ed ipervenduto, è necessario inserire dall'apposita finestra di Trading View i segnali denominati "Segnale di ipercomprato" ed "Segnale di ipervenduto" per ogni grafico che si usa, nel caso come me che si utilizzano più grafici nella stessa finestra. Spero che possiate apprezzare il mio lavoro. Per qualsiasi domanda scrivete nei commenti. Grazie<3
Indicatore Pine Script®
di stefanopanetta-sph28
63
Monte Carlo Range Forecast [DW]This is an experimental study designed to forecast the range of price movement from a specified starting point using a Monte Carlo simulation. Monte Carlo experiments are a broad class of computational algorithms that utilize random sampling to derive real world numerical results. These types of algorithms have a number of applications in numerous fields of study including physics, engineering, behavioral sciences, climate forecasting, computer graphics, gaming AI, mathematics, and finance. Although the applications vary, there is a typical process behind the majority of Monte Carlo methods: -> First, a distribution of possible inputs is defined. -> Next, values are generated randomly from the distribution. -> The values are then fed through some form of deterministic algorithm. -> And lastly, the results are aggregated over some number of iterations. In this study, the Monte Carlo process used generates a distribution of aggregate pseudorandom linear price returns summed over a user defined period, then plots standard deviations of the outcomes from the mean outcome generate forecast regions. The pseudorandom process used in this script relies on a modified Wichmann-Hill pseudorandom number generator (PRNG) algorithm. Wichmann-Hill is a hybrid generator that uses three linear congruential generators (LCGs) with different prime moduli. Each LCG within the generator produces an independent, uniformly distributed number between 0 and 1. The three generated values are then summed and modulo 1 is taken to deliver the final uniformly distributed output. Because of its long cycle length, Wichmann-Hill is a fantastic generator to use on TV since it's extremely unlikely that you'll ever see a cycle repeat. The resulting pseudorandom output from this generator has a minimum repetition cycle length of 6,953,607,871,644. Fun fact: Wichmann-Hill is a widely used PRNG in various software applications. For example, Excel 2003 and later uses this algorithm in its RAND function, and it was the default generator in Python up to v2.2. The generation algorithm in this script takes the Wichmann-Hill algorithm, and uses a multi-stage transformation process to generate the results. First, a parent seed is selected. This can either be a fixed value, or a dynamic value. The dynamic parent value is produced by taking advantage of Pine's timenow variable behavior. It produces a variable parent seed by using a frozen ratio of timenow/time. Because timenow always reflects the current real time when frozen and the time variable reflects the chart's beginning time when frozen, the ratio of these values produces a new number every time the cache updates. After a parent seed is selected, its value is then fed through a uniformly distributed seed array generator, which generates multiple arrays of pseudorandom "children" seeds. The seeds produced in this step are then fed through the main generators to produce arrays of pseudorandom simulated outcomes, and a pseudorandom series to compare with the real series. The main generators within this script are designed to (at least somewhat) model the stochastic nature of financial time series data. The first step in this process is to transform the uniform outputs of the Wichmann-Hill into outputs that are normally distributed. In this script, the transformation is done using an estimate of the normal distribution quantile function. Quantile functions, otherwise known as percent-point or inverse cumulative distribution functions, specify the value of a random variable such that the probability of the variable being within the value's boundary equals the input probability. The quantile equation for a normal probability distribution is μ + σ(√2)erf^-1(2(p - 0.5)) where μ is the mean of the distribution, σ is the standard deviation, erf^-1 is the inverse Gauss error function, and p is the probability. Because erf^-1() does not have a simple, closed form interpretation, it must be approximated. To keep things lightweight in this approximation, I used a truncated Maclaurin Series expansion for this function with precomputed coefficients and rolled out operations to avoid nested looping. This method provides a decent approximation of the error function without completely breaking floating point limits or sucking up runtime memory. Note that there are plenty of more robust techniques to approximate this function, but their memory needs very. I chose this method specifically because of runtime favorability. To generate a pseudorandom approximately normally distributed variable, the uniformly distributed variable from the Wichmann-Hill algorithm is used as the input probability for the quantile estimator. Now from here, we get a pretty decent output that could be used itself in the simulation process. Many Monte Carlo simulations and random price generators utilize a normal variable. However, if you compare the outputs of this normal variable with the actual returns of the real time series, you'll find that the variability in shocks (random changes) doesn't quite behave like it does in real data. This is because most real financial time series data is more complex. Its distribution may be approximately normal at times, but the variability of its distribution changes over time due to various underlying factors. In light of this, I believe that returns behave more like a convoluted product distribution rather than just a raw normal. So the next step to get our procedurally generated returns to more closely emulate the behavior of real returns is to introduce more complexity into our model. Through experimentation, I've found that a return series more closely emulating real returns can be generated in a three step process: -> First, generate multiple independent, normally distributed variables simultaneously. -> Next, apply pseudorandom weighting to each variable ranging from -1 to 1, or some limits within those bounds. This modulates each series to provide more variability in the shocks by producing product distributions. -> Lastly, add the results together to generate the final pseudorandom output with a convoluted distribution. This adds variable amounts of constructive and destructive interference to produce a more "natural" looking output. In this script, I use three independent normally distributed variables multiplied by uniform product distributed variables. The first variable is generated by multiplying a normal variable by one uniformly distributed variable. This produces a bit more tailedness (kurtosis) than a normal distribution, but nothing too extreme. The second variable is generated by multiplying a normal variable by two uniformly distributed variables. This produces moderately greater tails in the distribution. The third variable is generated by multiplying a normal variable by three uniformly distributed variables. This produces a distribution with heavier tails. For additional control of the output distributions, the uniform product distributions are given optional limits. These limits control the boundaries for the absolute value of the uniform product variables, which affects the tails. In other words, they limit the weighting applied to the normally distributed variables in this transformation. All three sets are then multiplied by user defined amplitude factors to adjust presence, then added together to produce our final pseudorandom return series with a convoluted product distribution. Once we have the final, more "natural" looking pseudorandom series, the values are recursively summed over the forecast period to generate a simulated result. This process of generation, weighting, addition, and summation is repeated over the user defined number of simulations with different seeds generated from the parent to produce our array of initial simulated outcomes. After the initial simulation array is generated, the max, min, mean and standard deviation of this array are calculated, and the values are stored in holding arrays on each iteration to be called upon later. Reference difference series and price values are also stored in holding arrays to be used in our comparison plots. In this script, I use a linear model with simple returns rather than compounding log returns to generate the output. The reason for this is that in generating outputs this way, we're able to run our simulations recursively from the beginning of the chart, then apply scaling and anchoring post-process. This allows a greater conservation of runtime memory than the alternative, making it more suitable for doing longer forecasts with heavier amounts of simulations in TV's runtime environment. From our starting time, the previous bar's price, volatility, and optional drift (expected return) are factored into our holding arrays to generate the final forecast parameters. After these parameters are computed, the range forecast is produced. The basis value for the ranges is the mean outcome of the simulations that were run. Then, quarter standard deviations of the simulated outcomes are added to and subtracted from the basis up to 3σ to generate the forecast ranges. All of these values are plotted and colorized based on their theoretical probability density. The most likely areas are the warmest colors, and least likely areas are the coolest colors. An information panel is also displayed at the starting time which shows the starting time and price, forecast type, parent seed value, simulations run, forecast bars, total drift, mean, standard deviation, max outcome, min outcome, and bars remaining. The interesting thing about simulated outcomes is that although the probability distribution of each simulation is not normal, the distribution of different outcomes converges to a normal one with enough steps. In light of this, the probability density of outcomes is highest near the initial value + total drift, and decreases the further away from this point you go. This makes logical sense since the central path is the easiest one to travel. Given the ever changing state of markets, I find this tool to be best suited for shorter term forecasts. However, if the movements of price are expected to remain relatively stable, longer term forecasts may be equally as valid. There are many possible ways for users to apply this tool to their analysis setups. For example, the forecast ranges may be used as a guide to help users set risk targets. Or, the generated levels could be used in conjunction with other indicators for meaningful confluence signals. More advanced users could even extrapolate the functions used within this script for various purposes, such as generating pseudorandom data to test systems on, perform integration and approximations, etc. These are just a few examples of potential uses of this script. How you choose to use it to benefit your trading, analysis, and coding is entirely up to you. If nothing else, I think this is a pretty neat script simply for the novelty of it. ---------- How To Use: When you first add the script to your chart, you will be prompted to confirm the starting date and time, number of bars to forecast, number of simulations to run, and whether to include drift assumption. You will also be prompted to confirm the forecast type. There are two types to choose from: -> End Result - This uses the values from the end of the simulation throughout the forecast interval. -> Developing - This uses the values that develop from bar to bar, providing a real-time outlook. You can always update these settings after confirmation as well. Once these inputs are confirmed, the script will boot up and automatically generate the forecast in a separate pane. Note that if there is no bar of data at the time you wish to start the forecast, the script will automatically detect use the next available bar after the specified start time. From here, you can now control the rest of the settings. The "Seeding Settings" section controls the initial seed value used to generate the children that produce the simulations. In this section, you can control whether the seed is a fixed value, or a dynamic one. Since selecting the dynamic parent option will change the seed value every time you change the settings or refresh your chart, there is a "Regenerate" input built into the script. This input is a dummy input that isn't connected to any of the calculations. The purpose of this input is to force an update of the dynamic parent without affecting the generator or forecast settings. Note that because we're running a limited number of simulations, different parent seeds will typically yield slightly different forecast ranges. When using a small number of simulations, you will likely see a higher amount of variance between differently seeded results because smaller numbers of sampled simulations yield a heavier bias. The more simulations you run, the smaller this variance will become since the outcomes become more convergent toward the same distribution, so the differences between differently seeded forecasts will become more marginal. When using a dynamic parent, pay attention to the dispersion of ranges. When you find a set of ranges that is dispersed how you like with your configuration, set your fixed parent value to the parent seed that shows in the info panel. This will allow you to replicate that dispersion behavior again in the future. An important thing to note when settings alerts on the plotted levels, or using them as components for signals in other scripts, is to decide on a fixed value for your parent seed to avoid minor repainting due to seed changes. When the parent seed is fixed, no repainting occurs. The "Amplitude Settings" section controls the amplitude coefficients for the three differently tailed generators. These amplitude factors will change the difference series output for each simulation by controlling how aggressively each series moves. When "Adjust Amplitude Coefficients" is disabled, all three coefficients are set to 1. Note that if you expect volatility to significantly diverge from its historical values over the forecast interval, try experimenting with these factors to match your anticipation. The "Weighting Settings" section controls the weighting boundaries for the three generators. These weighting limits affect how tailed the distributions in each generator are, which in turn affects the final series outputs. The maximum absolute value range for the weights is . When "Limit Generator Weights" is disabled, this is the range that is automatically used. The last set of inputs is the "Display Settings", where you can control the visual outputs. From here, you can select to display either "Forecast" or "Difference Comparison" via the "Output Display Type" dropdown tab. "Forecast" is the type displayed by default. This plots the end result or developing forecast ranges. There is an option with this display type to show the developing extremes of the simulations. This option is enabled by default. There's also an option with this display type to show one of the simulated price series from the set alongside actual prices. This allows you to visually compare simulated prices alongside the real prices. "Difference Comparison" allows you to visually compare a synthetic difference series from the set alongside the actual difference series. This display method is primarily useful for visually tuning the amplitude and weighting settings of the generators. There are also info panel settings on the bottom, which allow you to control size, colors, and date format for the panel. It's all pretty simple to use once you get the hang of it. So play around with the settings and see what kinds of forecasts you can generate! ---------- ADDITIONAL NOTES & DISCLAIMERS Although I've done a number of things within this script to keep runtime demands as low as possible, the fact remains that this script is fairly computationally heavy. Because of this, you may get random timeouts when using this script. This could be due to either random drops in available runtime on the server, using too many simulations, or running the simulations over too many bars. If it's just a random drop in runtime on the server, hide and unhide the script, re-add it to the chart, or simply refresh the page. If the timeout persists after trying this, then you'll need to adjust your settings to a less demanding configuration. Please note that no specific claims are being made in regards to this script's predictive accuracy. It must be understood that this model is based on randomized price generation with assumed constant drift and dispersion from historical data before the starting point. Models like these not consider the real world factors that may influence price movement (economic changes, seasonality, macro-trends, instrument hype, etc.), nor the changes in sample distribution that may occur. In light of this, it's perfectly possible for price data to exceed even the most extreme simulated outcomes. The future is uncertain, and becomes increasingly uncertain with each passing point in time. Predictive models of any type can vary significantly in performance at any point in time, and nobody can guarantee any specific type of future performance. When using forecasts in making decisions, DO NOT treat them as any form of guarantee that values will fall within the predicted range. When basing your trading decisions on any trading methodology or utility, predictive or not, you do so at your own risk. No guarantee is being issued regarding the accuracy of this forecast model. Forecasting is very far from an exact science, and the results from any forecast are designed to be interpreted as potential outcomes rather than anything concrete. With that being said, when applied prudently and treated as "general case scenarios", forecast models like these may very well be potentially beneficial tools to have in the arsenal.
Indicatore Pine Script®
di DonovanWall
75
Machine Learning: LVQ-based StrategyLVQ-based Strategy (FX and Crypto) Description: Learning Vector Quantization (LVQ) can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all learning-based approach. It is based on prototype supervised learning classification task and trains its weights through a competitive learning algorithm. Algorithm: Initialize weights Train for 1 to N number of epochs - Select a training example - Compute the winning vector - Update the winning vector Classify test sample The LVQ algorithm offers a framework to test various indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others. Note: TradingViews's playback feature helps to see this strategy in action. The algo is tested with BTCUSD/1Hour. Warning: This is a preliminary version! Signals ARE repainting. ***Warning***: Signals LARGELY depend on hyperparams (lrate and epochs). Style tags: Trend Following, Trend Analysis Asset class: Equities, Futures, ETFs, Currencies and Commodities Dataset: FX Minutes/Hours+++/Days
Indicatore Pine Script®
di capissimo
6
Machine Learning: Logistic RegressionMulti-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
Indicatore Pine Script®
di capissimo
112
Machine Learning: Perceptron-based strategyPerceptron-based strategy Description: The Learning Perceptron is the simplest possible artificial neural network (ANN), consisting of just a single neuron and capable of learning a certain class of binary classification problems. The idea behind ANNs is that by selecting good values for the weight parameters (and the bias), the ANN can model the relationships between the inputs and some target. Generally, ANN neurons receive a number of inputs, weight each of those inputs, sum the weights, and then transform that sum using a special function called an activation function. The output of that activation function is then either used as the prediction (in a single neuron model) or is combined with the outputs of other neurons for further use in more complex models. The purpose of the activation function is to take the input signal (that’s the weighted sum of the inputs and the bias) and turn it into an output signal. Think of this activation function as firing (activating) the neuron when it returns 1, and doing nothing when it returns 0. This sort of computation is accomplished with a function called step function: f(z) = {1 if z > 0 else 0}. This function then transforms any weighted sum of the inputs and converts it into a binary output (either 1 or 0). The trick to making this useful is finding (learning) a set of weights that lead to good predictions using this activation function. Training our perceptron is simply a matter of initializing the weights to zero (or random value) and then implementing the perceptron learning rule, which just updates the weights based on the error of each observation with the current weights. This has the effect of moving the classifier’s decision boundary in the direction that would have helped it classify the last observation correctly. This is achieved via a for loop which iterates over each observation, making a prediction of each observation, calculating the error of that prediction and then updating the weights accordingly. In this way, weights are gradually updated until they converge. Each sweep through the training data is called an epoch. In this script the perceptron is retrained on each new bar trying to classify this bar by drawing the moving average curve above or below the bar. This script was tested with BTCUSD, USDJPY, and 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
Indicatore Pine Script®
di capissimo
33
Machine Learning: kNN-based StrategykNN-based Strategy (FX and Crypto) Description: This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms. To do a prediction of the next market move, the kNN algorithm uses the historic data, collected in 3 arrays - feature1, feature2 and directions, - and finds the k-nearest neighbours of the current indicator(s) values. The two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours, sees their state and thus classifies the current point. The kNN algorithm offers a framework to test all kinds of indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others. 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
Indicatore Pine Script®
di capissimo
149
Candlesticks ANN for Stock Markets TF : 1WHello, this script consists of training candlesticks with Artificial Neural Networks (ANN). In addition to the first series, candlesticks' bodies and wicks were also introduced as training inputs. The inputs are individually trained to find the relationship between the subsequent historical value of all candlestick values ​​1.(High,Low,Close,Open) The outputs are adapted to the current values ​​with a simple forecast code. Once the OHLC value is found, the exponential moving averages of 5 and 20 periods are used. Reminder : OHLC = (Open + High + Close + Low ) / 4 First version : Script is designed for S&P 500 Indices,Funds,ETFs, especially S&P 500 Stocks,and for all liquid Stocks all around the World. NOTE: This script is only suitable for 1W time-frame for Stocks. The average training error rates are less than 5 per thousand for each candlestick variable. (Average Error < 0.005 ) I've just finished it and haven't tested it in detail. So let's use it carefully as a supporter. Best regards !
Indicatore Pine Script®
di Noldo
2
ANN Next Coming Candlestick Forecast SPX 1D v1.0WARNING: Experimental and incomplete. Script is open to development and will be developed. This is just version 1.0 STRUCTURE This script is trained according to the open, close, high and low values ​​of the bars. It is tried to predict the future values ​​of opening, closing, high and low values. A few simple codes were used to correlate expectation with current values. (You can see between line 129 - 159 ) Therefore, they are all individually trained. You can see in functions. The average training error of each variable is less than 0.011. NOTE : This script is designed for experimental use on S & P 500 and connected instruments only on 1-day bars. The Plotcandle function is inspired by the following script of alexgrover : Since we estimate the next values, our error rates should be much lower for all candlestick values. This is just first version to show logic. I will continue to look for other variables to reach average error = 0.001 - 0.005 for each candlestick status. Feel free to use and improve , this is open-source. Best regards.
Indicatore Pine Script®
di Noldo
5
ANN BTC MTF CM Sling Shot SystemHi all, this script was created as a result of ANN training in all time frames of bitcoin data. Trained data is built on Chris Moody's Sling Shot system. CM Sling Shot System : This system automatically generates the ANN output for all time periods. Therefore, it has multi-time-frame feature. Artificial Neural Networks training details: Average Errors 1 minute = 0.005570 3 minutes = 0.006674 5 minutes = 0.007067 15 minutes = 0.010000 30 minutes = 0.009398 45 minutes = 0.010000 1 Hour = 0.006848 2 Hours = 0.006901 3 Hours = 0.009608 4 Hours = 0.009774 1 Day = 0.010000 1 Week = 0.010000 The results look good (All Average Error <= 0.01 ), the Sling Shot Method is also good, but you can also refer to historically slower period averages to filter these arrows a bit more. I leave the decision to you. Best regards.
Indicatore Pine Script®
di Noldo
13
Macroeconomic Artificial Neural Networks This script was created by training 20 selected macroeconomic data to construct artificial neural networks on the S&P 500 index. No technical analysis data were used. The average error rate is 0.01. In this respect, there is a strong relationship between the index and macroeconomic data. Although it affects the whole world,I personally recommend using it under the following conditions: S&P 500 and related ETFs in 1W time-frame (TF = 1W SPX500USD, SP1!, SPY, SPX etc. ) Macroeconomic Parameters Effective Federal Funds Rate (FEDFUNDS) Initial Claims (ICSA) Civilian Unemployment Rate (UNRATE) 10 Year Treasury Constant Maturity Rate (DGS10) Gross Domestic Product , 1 Decimal (GDP) Trade Weighted US Dollar Index : Major Currencies (DTWEXM) Consumer Price Index For All Urban Consumers (CPIAUCSL) M1 Money Stock (M1) M2 Money Stock (M2) 2 - Year Treasury Constant Maturity Rate (DGS2) 30 Year Treasury Constant Maturity Rate (DGS30) Industrial Production Index (INDPRO) 5-Year Treasury Constant Maturity Rate (FRED : DGS5) Light Weight Vehicle Sales: Autos and Light Trucks (ALTSALES) Civilian Employment Population Ratio (EMRATIO) Capacity Utilization (TOTAL INDUSTRY) (TCU) Average (Mean) Duration Of Unemployment (UEMPMEAN) Manufacturing Employment Index (MAN_EMPL) Manufacturers' New Orders (NEWORDER) ISM Manufacturing Index (MAN : PMI) Artificial Neural Network (ANN) Training Details : Learning cycles: 16231 AutoSave cycles: 100 Grid Input columns: 19 Output columns: 1 Excluded columns: 0 Training example rows: 998 Validating example rows: 0 Querying example rows: 0 Excluded example rows: 0 Duplicated example rows: 0 Network Input nodes connected: 19 Hidden layer 1 nodes: 2 Hidden layer 2 nodes: 0 Hidden layer 3 nodes: 0 Output nodes: 1 Controls Learning rate: 0.1000 Momentum: 0.8000 (Optimized) Target error: 0.0100 Training error: 0.010000 NOTE : Alerts added . The red histogram represents the bear market and the green histogram represents the bull market. Bars subject to region changes are shown as background colors. (Teal = Bull , Maroon = Bear Market ) I hope it will be useful in your studies and analysis, regards.
Indicatore Pine Script®
di Noldo
40
Blockchain Artificial Neural NetworksI found a very high correlation in a research-based Artificial Neural Networks.(ANN) Trained only on daily bars with blockchain data and Bitcoin closing price. NOTE: It does not repaint strictly during the weekly time frame. (TF = 1W) Use only for Bitcoin . Blockchain data can be repainted in the daily time zone according to the description time. Alarms are available. And you can also paint bar colors from the menu by region. After making reminders, let's share the details of this interesting research: INPUTS : 1. Average Block Size 2. Api Blockchain Size 3. Miners Revenue 4. Hash Rate 5. Bitcoin Cost Per Transaction 6. Bitcoin USD Exchange Trade Volume 7. Bitcoin Total Number of Transactions OUTPUTS : 1. One day next price close (Historical) TRAINING DETAILS : Learning cycles: 1096436 AutoSave cycles: 100 Grid : Input columns: 7 Output columns: 1 Excluded columns: 0 Training example rows: 446 Validating example rows: 0 Querying example rows: 0 Excluded example rows: 0 Duplicated example rows: 0 Network : Input nodes connected: 7 Hidden layer 1 nodes: 5 Hidden layer 2 nodes: 0 Hidden layer 3 nodes: 0 Output nodes: 1 Controls : Learning rate: 0.1000 Momentum: 0.8000 Target error: 0.0100 Training error: 0.010571 The average training error is really low, almost worth the target. Without using technical analysis data, we established Artificial Neural Networks with blockchain data. Interesting!
Indicatore Pine Script®
di Noldo
16
Open Source Ichimoku Kinkō hyō Keizen 改善Open Source Ichimoku Kinkō hyō Keizen 改善 First of all, thank you for using my work, making changes and continuing to share it for free. I chose as indicator name Ichimoku Kinkō hyō Keizen logically simply by what the word Keizen reflects a Japanese method which means continuous improvement and quality. The goal here is to correct already any offset faults that should not be present and to try to bring the indicator of new things that can serve and advance and provide additional support for the decision. A continuation will surely be planned which will bring its batch of new elements which will come naturally be grafted to this tool. Possibility of adding or not adding new lines Jun (76), Kan (226), Junkan A, B (676). - Junakan To be calculated on the average of Jun / Kan 151 periods. - Junkan B calculate over the 676 periods. - Possibility of adding or not adding a new kinkohoyo calculate on Jun (76) and Kan (226). - Possibility of adding or not adding a new Kumo calculate over 676 periods. - Possibility of coloring or not the kumos or the kinkohyos. - Possibility of adding or not Chikō 9,26,52,76,226,676. - Ability to display or not passive lines in the future, 5 shots ahead of the movement of lines, provided that the extremes are not broken. Thank you: Thanks to TomQSD to have tested all the different versions of this project and to have contributed his criticism that helped me a lot to develop the tool. Thanks to Herveo for having agreed to share his code on the passive lines. Remarks: Not all options are activated at the same time. Please do not uncheck the boxes in style, only change the colors. To enable or disable lines, go to input parameters. ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// Open Source Ichimoku Kinkō hyō Keizen 改善 Tous d'abord, merci d'utiliser mon travail, d'apporter des modification et de continuer à le partager gratuitement. J'ai choisi comme nom d’indicateur Ichimoku Kinkō hyō Keizen en toute logique tout simplement par ce que le mot Keizen reflète une méthode Japonaise qui veut dire amélioration en continue et qualité. Le but ici est de corriger déjà tout les défauts de décalage qui ne devraient pas être présents et d’essayer d’apporter sans trop charger l’indicateur des choses nouvelles qui peuvent servir et faire progresser et apporter une aide supplémentaire à la décision. Une suite sera sûrement prévue qui apportera son lot d'éléments nouveaux qui viendront naturellement se greffer à cet outil. - Possibilité d’ajout ou non de nouvelles lignes Jun (76) , Kan (226) , Junkan A,B (676). - Junakan A calculer sur la moyenne de Jun/Kan 151 périodes. - Junkan B calculer sur les 676 périodes. - Possibilité d’ajout ou non d’un nouveau kinkohoyo calculer sur le Jun (76) et le Kan (226). - Possibilité d’ajout ou non d’un nouveau Kumo calculer sur 676 périodes. - Possibilité de colorier ou non le ou les kumos et le ou les kinkohyos. - Possibilité d’ajout ou non de Chikō 9,26,52,76,226,676. - Possibilité d’afficher ou non de lignes passives dans le future, 5 coups d’avance sur les déplacement des lignes, a condition que les extrêmes ne soient pas rompu. Remerciement : Merci à TomQSD d'avoir tester toutes les différentes versions de ce projet et d’avoir apporter sa critique qui m'a beaucoup aidé à développer l’outil. Merci à Herveo pour sont accord d’avoir bien voulu partager son code sur les lignes passives. Remarques: Toutes les options ne sont pas toutes à activer en même temps. Merci de ne pas décocher les cases dans style, modifiez seulement les couleurs. Pour activer ou désactiver des lignes, allez dans paramètres en entrée.
Indicatore Pine Script®
di Canop2e_T
5
ANN GOLD WORLDWIDE This script consists of converting the value of 1 gram and / or 1 ounce of gold according to the national currencies into a system with artificial neural networks. Why did I feel such a need? Even though the printed products in the market are digitally circulated, only precious metals are available in full or near full. Silver is difficult to carry because you have to buy too much because the unit price is low. Platinum is very difficult to find and used in industry. Gold is both practical and has less volatile movements, even more balanced than dollars, to preserve the value of money. Uncertainty and tensions benefit gold. Obviously this is my own opinion and is not worth the investment advice: If there is to be an economic crisis, it is obvious that the dollar will rise against the emerging currencies, but I expect a crisis where gold and the dollar will rise together. The world has been on a mercantilist line more than ever! Spot gold can be bought from goldsmiths and banks. I think this command will benefit people everywhere but in economies that are subject to developing currencies. Now we can look at the details: All you have to do is load the appropriate chart and select it from the menu. Thus, the system will adjust itself to that instrument. MENU and Tickers : "GOLD" : XAUUSD or GC1! or GOLD (Average error = 0.0128) "GOLDSILVER" : XAUXAG or GOLDSILVER (Gold Silver Ratio ) ( Average error : 0.01 ) "GOLD CZK " : XAUUSD/USDCZK ( 1 Ounce Gold Czech Koruna) ( Average error = 0.010879 ) "GOLD NZD " : XAUUSD/USDNZD ( 1 Ounce Gold New Zealand Dollar ) (Average error = 0.010736 ) "GOLD EURO" : XAUUSD/USDEUR ( 1 Ounce Gold Euro) ( Average error = 0.010000 ) "GOLD HUF " : XAUUSD/USDHUF ( 1 Ounce Gold Hungarian Forint ) ( Average error = 0.010000 ) "GOLD INR " : XAUUSD/USDINR (1 Ounce Gold Indian Rupee ) (Average error = 0.010458 ) "GOLD DKK" : XAUUSD/USDDKK (1 Ounce Gold Danish Krone) (Average error = 0.010671 ) "GOLD CHF" : XAUUSD/USDCHF (1 Ounce Gold Swiss Franc ) (Average error = 0.010967 ) "GOLD CNH" : XAUUSD/USDCNH(1 Ounce Gold Offshore RMB) (Average error = 0.012017 ) "GOLD MXN" : XAUUSD/USDMXN(1 Ounce Gold Mexican Peso) (Average error = 0.010000 ) "GOLD PLN" : XAUUSD/USDPLN (1 Ounce Gold Polish Zloty ) (Average error = 0.010173 ) "GOLD ZAR" : XAUUSD/USDZAR (1 Ounce Gold South African Rand (Average error = 0.010484 ) "GOLD NOK" : XAUUSD/USDNOK (1 Ounce Gold Norwegian Krone ) (Average error = 0.010842 ) "GOLD TRY" : XAUUSD/USDTRY (1 Ounce Gold Turkish Lira ) (Average error = 0.010000 ) "GOLD THB" : XAUUSD/USDTHB (1 Ounce Gold Thai Baht ) (Average error = 0.011747 ) Important note : XAUUSD/USDCUR = 1 Ounce Gold , XAUUSD/31.1*USDCUR = 1 gram Gold (CUR = Currency ) If you want to physically hold it, look gram value, because as far as I know, all goldsmiths and jewelleries in the world are selling gram gold. I think that this command is the most useful and the concrete one that I have ever written. I end my sentences with this anonymous proverb : "Even if gold falls into the mud, it's still gold ! "
Indicatore Pine Script®
di Noldo