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A.I.Driven TradersAI Model Trades for 20190612The entry and exit levels here are NOT derived from any specific indicator but are coming from our A.I. driven proprietary models. This is an attempt at exploring the trading community here at TradingView and sharing our daily trading plans published at our site with the community here in the form a Pine Script - just starting and learning this platform. Please help point out any obvious errors or gotchas committed in the scripts. Thanks and have a great trading day! **** The Trading Plan Published for today **** >>>> Medium-Frequency Models: <<<<< For today, Wednesday 06/12, our medium-frequency models indicate using the 2895 as a pivot point - opening a long on a break above 2895, and opening a short on a break below 2895 (wait for a close on at least a five minute chart to determine the break), both sides with a 9-point trailing stop.      Note: For the trades to trigger, the breaks should occur during the regular session hours starting at 9:30am ET. By design, these models do NOT open any new positions after 3:45pm. Only one open position at any given time. >>>>> Aggressive Intraday Models: <<<<< For today, Wednesday 06/12, our aggressive intraday models indicate going long on a break above 2892 or 2875 with an 6-point trailing stop, and going short on a break below 2887 or 2878 with an 8-point trailing stop.    Note: For the trades to trigger, the breaks should occur during regular session hours starting at 9:30am ET. Due to the intraday nature of these aggressive models, they indicate closing any open trades at 3:55pm and remaining flat into the session close. No opening of new positions after 3:45pm. Only one open position at any given time.
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PpSignal quantiles BandIn statistics and the theory of probability, quantiles are cutpoints dividing the range of a probability distribution into contiguous intervals with equal probabilities, or dividing the observations in a sample in the same way. There is one less quantile than the number of groups created. Thus quartiles are the three cut points that will divide a dataset into four equal-size groups (cf. depicted example). Common quantiles have special names: for instance quartile, decile (creating 10 groups: see below for more). The groups created are termed halves, thirds, quarters, etc., though sometimes the terms for the quantile are used for the groups created, rather than for the cut points. q-Quantiles are values that partition a finite set of values into q subsets of (nearly) equal sizes. There are q − 1 of the q-quantiles, one for each integer k satisfying 0 < k < q. In some cases the value of a quantile may not be uniquely determined, as can be the case for the median (2-quantile) of a uniform probability distribution on a set of even size. Quantiles can also be applied to continuous distributions, providing a way to generalize rank statistics to continuous variables. When the cumulative distribution function of a random variable is known, the q-quantiles are the application of the quantile function (the inverse function of the cumulative distribution function) to the values {1/q, 2/q, …, (q − 1)/q}
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PpSignal Random Walk Monte Carlo MethodRandom Walk Utility The random walk generator allows users of the Monte Carlo to further understand how the Monte Carlo projection is generated by creating a visual representation of individual random walks. Trends that occur on the random walks may correlate to the historical price action of the underlying security. Understanding the Random Walk Simulation This indicator randomly generates alternative price outcomes derived from the price movements of the underlying security. Monte Carlo methods rely on repeated random sampling to create a data set that has the same characteristics as the sample source, representing examples of alternate outcomes. The data set created using random sampling is called a “random walk”. First, every bar in the time stamp is measured logarithmically and put into a population. Then, a sample is drawn at random from the population and is used to determine the next price movement of the random walk. This process is repeated fifteen times to visualize whether the alternative outcomes lie above or beneath the current market price of the security.
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PpSignal Composit Fractal volatilityThis strategy is based on a mix of indicators. Institutional trades enter the market following the rule: trend, volatility and volume. If we want to find the trend it is necessary to use the CFB composite fractal behavor, you can look for it within my indicators the explanation. the CFB will be in charge of finding the right trend. if we have the tendency in hand we go to look for volatility. Volatility is a cyclical effect, caused mainly by the aggressive entry or aggressive exit of money in the market or what is the same, by fear or enthusiasm. The changes in the perspective of the participants caused by news, crisis, natural disasters or simply by the entry or exit of large capitals, will cause the price to move quickly in short periods of time. example, if we are in the 1h chart and the closing price of the daily bar is higher than the opening price and this is accompanied by volatility we have a greater probability of entering at the right time. For this we use standard deviation, atr and W% smoothing. Once all this information is aligned we have a correct entry with trend and volatility. We leave when the price is inside the band atr. the system also has different individual alerts such as ATR, kc channel, open close mtf bar, w%, cfb. I recommend that you look for your best strategy according to the instument that you wish to trade.   Buy = when the cfb show buy signal and the price is above the atr. sell = when the cfb show sell signal when the price is below the lower band of atr. inside = exit.
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PpSignal Coppock CurveIntroduction The Coppock Curve is a momentum indicator developed by Edwin “Sedge” Coppock, who was an economist by training. Coppock introduced the indicator in Barron's in October 1965. The goal of this indicator is to identify long-term buying opportunities in the S&P 500 and Dow Industrials. The signal is very simple. Coppock used monthly data to identify buying opportunities when the indicator moved from negative territory to positive territory. Although Coppock did not use it for sell signals, many technical analysts consider a cross from positive to negative territory as a sell signal. Signals Using monthly data, this indicator will not trigger very many signals. A buy signal triggers with a cross into positive territory, while a sell signal triggers with a cross into negative territory. Unsurprisingly, there have been only five signals since the late 1980s. The chart below shows the last four signals. The first signal triggered in 1988, which was after the 1987 crash. Conclusion The Coppock Curve is simply a smoothed momentum oscillator. Even though it was originally designed for monthly charts and long-term analysis, it can be used on intraday, daily or weekly charts and the settings can be adjusted to suit one's style. The main signals are generated with crosses above and below the zero line. More aggressive chartists can consider looking for bullish and bearish divergences to anticipate such crossovers. Use caution, however. Divergences do not always result in trend reversals because the trend can simply slow and continue in the same direction. stockcharts.com
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PpSignal01 tarafından
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PpSignal Multi-Day VWAPThank to @mortdiggiddy original script: Chart the multi-day Volume Weighted Average Price ( VWAP ). Normally, the VWAP is tracked for the current day, from the first bar of the day (regular or extended session). The VWAP shows the current value of: -> sum(hlc3 * volume , barsForDay) / sum( volume , barsForDay), -> where 'barsForDay' is the total number bars that have elapsed during the day for the chart interval. The multi-day version tracks the VWAP for N days back, by averaging the previous N - 1 day bars VWAP and the current VWAP for the current bar (chart interval). This is very different that simply using a volume weighted moving average , since the closing VWAP values are used for the historical day bars. The results are interesting for intraday trades... especially for values of 1, 2, 3, 4, 5 ....to 21 days.
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PpSignal CFB AlarmWhat is the Theory Behind CFB ? CFB tells you how long the market has been in a quality trend. This value can be used to adjust the period length of other indicators, especially stochastic bands. In order to quantify the overall duration of a market's trend, we replaced classical cycle analysis methods (FFT, MEM, MESA) with a form of analysis that works even when no cycles exist. We accomplished this by examining a time series for specific fractal patterns of any size. We then gather all the patterns found and combine them into one overall index, CFB (Composite Fractal Behavior) Index. For good reason, CFB does not analyze time series data for dominant cycles. Classical cycle analysis examines data points (e.g. prices) and estimates the average presence of a cycle in the window. Now suppose a cycle with a period length of 9 days was strong for 50 days and then disappeared for the next 14 days. Because the cycle was present for 50 out of the last (50+14=64) days, the average presence of that cycle would be measured as "strong" even though it does not exist anymore! Does CFB find the Dominant Cycle ? No! Consider the following discussion about the MYTH of exploiting dominant cycles. It is true that the market does have predictable cycles due to its "structural" or physical nature. For example, quarterly earning cycles, triple witching cycles, Federal Reserve meetings, weekly cycles, political election year cycles, the annual end-of-year stock dumping cycle, sunspot cycles, and the slow Kitchin (3-5 years), Juglar (7-11 years), Kuznet (15-25 years) and Kondratieff (45-60 years) cycles. They are very predictable and the markets readily discount their presence as far ahead in time as is reasonable. So there's not much left with regard to those cycles for you to exploit. What traders see as cycles on an hourly chart, for example, is a different matter. The big, obvious cycles you see on price charts are actually the result of a combination of many weak cyclic forces that sometimes line up in phase to produce APPARENT dominant cycles that suggest the presence of a strong structural cycle that, in fact, does not exist. The slightest shifting in phase of any one component (due to crowd psychology, unscheduled events, etc.) will significantly alter the structure of the apparent dominant wave. This may drive the cycle into a "null" or random period, then reappear, completely out of phase. Now you see it ... and now you don't. The transitory nature of these apparent dominant cycles makes their automated detection difficult and forecast unreliable. Sometimes cycle forecasting tools appear accurate and other times they are totally off mark. The reason is that tools designed to spot dominant cycles will announce whatever they find, even if they are only apparent (not structural) and transitory. For example, such tools would have no problem detecting cycles in the six charts below. But there is just one problem --- the slow cyclic price action in the six charts below is *impossible* to project into the future with any reasonable accuracy! Why? Because we produced these six charts by simply adding consecutive random price changes. That's right!! These charts are nothing more than RANDOM WALKS. And by definition, they cannot be forecasted, no matter how impressive their apparent cyclic behavior may be! The chart above does not "prove" market cycles are non-existent. Indeed, discretionary traders can learn to spot and use periodic price events, and take time to "understand" their causes, in order to verify whether the relevant triggers have actually occurred. This demonstration does show, however, that cycle-finding tools like FFT, MESA and periodigrams, which have no understanding of market cause-effect relationships, can be easily fooled into seeing ghosts. In contrast, our CFB tool was designed to measure market trending action without assuming the existance of cycles. This makes CFB more reliable. How would I use CFB's results ? CFB produces a value proportional to a time series' trend duration. This value is in units of TIME, as measured in bars on a chart. Because CFB's output is in units of time and not price, CFB offers a unique window into a new dimension for representing signal behavior. Investors have discovered many profitable ways to apply CFB: * To auto-adjust the lookback of classical indicators, such as RSI * To auto-adjust the lookback depth of breakout channels in trending markets * To auto-adjust the minimum amount of retracement needed to reverse position Making a profit in the market requires your finding a unique niche that very few other people are exploiting. CFB offers this unique perspective. Do I specify a "period length" for CFB? In CFB, period length determines how many bars (time slices) are examined for specific fractal patterns. Due to the complexity of the algorithm, CFB permits only four period lengths: 24, 48, 96, 192. The 24-bar version can see trend fractals up to 24 bars wide, and so on. You get all four versions when ordering CFB. ________________________________
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PpSignal01 tarafından
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PpSignal PerPro V6 Indicator Version with alarmThis is the of PerPro V6 indicator version with alarm. Remember in trend changes the indicator may repaint the signal. Please use other indicators to accompany the entries. in small periods, such as 1, 3 and 5 minutes and pairs or high volatility stock we have seen a repaint until confirms the trend. if you want to use it in 1, 3 or 5 minutes condigurelo in tf 1h or 240 up. in 1h charts we recommend 480, 720 or D in forex, in stock 1h tf W, in 1D graph set in W or M. It may be at the beginning of a new signal repaint. I'll leave it free for 3 months then I'll change the code and make it private.
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PpSignal ADX Moving Average Trading in the direction of a strong trend reduces risk and increases profit potential. The average directional index (ADX) is used to determine when the price is trending strongly. In many cases, it is the ultimate trend indicator. After all, the trend may be your friend, but it sure helps to know who your friends are. In this article, we'll examine the value of ADX as a trend strength indicator. Please watch the next web for more infomation www.investopedia.com we managed to reproduce the adx mobile average in the price or overlay chart
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