Power Bar [racer8]Introduction: 🌟
The Power Bar indicator is a powerful volatility indicator that can detect power bars 💪. A power bar is just a really big price bar that forms after a price base. A price base is chart pattern consisting of many low volatility price bars (bars that have small ranges). To detect such powerful bars, the PB indicator uses the following formula:
PB = ( Absolute value of current close - previous close ) / ( Previous price range over n periods )
Looking at the formula, you can see that PB compares the current change in closing price to the n-period base pattern's range. Strong PB values are typically greater than a value of 1. If n periods = 10, the indicator will look back 11 periods. The 11 periods includes the 10-period base plus the current price bar. 10 periods is the default setting for the indicator.
After the calculation, PB is then plotted as a histogram. Along with the histogram, a horizontal dashed line is also plotted.
PB's other setting controls the dashed line's level. This level is preset at a default value of 1. The dashed line is just a way to filter out weak PB values, and to generate signals. A signal is generated when the PB histogram is above the dashed line.
Objective: 🤔
This indicator shall prove very useful to you if your main objective is to trade only the best chart pattern in the market...and the base pattern is one of the best, if not the best chart pattern that exists today. This indicator is a mechanical way of detecting the chart pattern.
Enjoy! 🥳
Recherche dans les scripts pour "美国11月非农数据"
MA+ADX+DMICOINBASE:BTCUSD
BINANCE:BTCUSDT
Use long and short moving average to look for a potential price in/out. (default as 14 and 7, bases on the history experience)
ADX and DMI to prevent the small volatility and tangling MA.
Test it in 4HR, "BINANCE:BTCUSDT"
From 12/1/2017- 11/1/2020 (Mixed Bull/Bear market)
Overall Profit: 560.89%
From 1/1/2018 - 1/1/2019 (Bear market)
Overall Profit: -2.19%
From 4/1/2020 - 11/1/2020 (Bull Market)
Overall Profit: 274.74%
Any suggestion is welcome to discuss.
Bullish and Bearish by NicolErazoFThis indicator changes the color of the candlesticks when there’s a change in the trend to the rising or falling trend.
BEARISH ENGULFING: Yellow candlestick. It is an engulfing falling trend reversal; you must make a sell decision.
BEARISH HARAMI: White candlestick. Indicates a possible falling trend change, you must be alert for a possible sale.
BULLISH ENGULFING: Black candlestick. It is a change in the engulfing rising trend, you must make a purchase decision.
BULLISH HARAMI: Blue candlestick. Indicates a possible rising trend change, you should be alert for a possible purchase.
On the chart, you can see the 4 candles, on September 11 the black candle appears indicating a change in the uptrend. But today, the white candle is seen, which appears on September 8, indicating a rebound with a possible change in trend to bearish.
Previous days, on August 26, you see the blue candle with a possible change in the upward trend, which then, on August 28, a yellow candle appears with a change in the downward trend.
The Engulfing indicator (yellow and black) says that the candle has an engulfing change that is radical.
On the other hand, the Harami (blue and white) indicates a possible change in trend that must be previously analyzed.
Harami candles are smaller than Engulfing candles, since Harami in a Japanese term that means pregnancy, where the previous candle is the woman and the next candle is the baby.
___________________________________________________________________________
ESPAÑOL
Este indicador cambia las velas de color cuando ocurre un cambio de tendencia ALCISTA o BAJISTA
BEARISH ENGULFING: Vela de color amarillo. Es una cambio de tendencia bajista envolvente, debes tomar una decisión de venta.
BEARISH HARAMI: Vela de color blanco. Indica un posible cambio de tendencia bajista, debes estar alerta para una posible venta.
BULLISH ENGULFING: Vela de color negro. Es un cambio de tendencia alcista envolvente, debes tomar una decisión de compra.
BULLISH HARAMI: Vela de color azul. Indica un posible cambio de tendencia alcista, debes estar alerta para una posible compra.
En el gráfico, se pueden ver las 4 velas, el 11 de Septiembre aparece la vela negra que indica un cambio de tendencia alcista. Pero hoy, se ve la vela blanca, que aparece el 8 de septiembre, indicando un rebote con un posible cambio de tendencia a bajista.
Días anteriores, el 26 de Agosto, se ve la vela azul con un posible cambio de tendencia alcista, que luego, el 28 de agosto aparece una vela amarilla con cambio de tendencia bajista.
El indicador Engulfing (amarillo y negro) dice que la vela tiene un cambio envolvente que es radical.
En cambio, el Harami (azul y blanco) indica un posible cambio de tendencia que debe ser previamente analizado.
Las velas Harami son más pequeñas que las Engulfing , ya que Harami en un término japonés que significa embarazo, en donde la vela anterior es la mujer y la vela siguiente es el bebé.
KingEMA21-55ZoneI used the moving average with the habit of 21-55, so added two moving average
When the price runs above 55, it only looks for the buy signal.
When the price runs below 55, it only looks for sell signals.
The first step up through the 55 moving average after the first confirmation can buy homeoply,
The first pull down after crossing the 55 moving average for the first time confirms that it can be sold in line with the trend.
Price horizontal finishing, moving average frequently across the field observation.
The yellow area in the interval from 81to 55 is the homeopathic warehouse addition signal.
When the price is above the 55 moving average, the k-line closes below the 21-day moving average as a callback signal
Prices below the 55 ema close above the 21 - day ema as a rebound signal
After the correction and rebound signals come out, we should make half of the profit and the other half of the stop loss in the break-even place.
Moving average is very suitable for the trend of strong varieties, is not suitable for volatile market.
Only at the end of the shock market moving average upward or downward divergent when it is possible to be used.
1. Repeatedly entangle the mean line of horizontal disk stage and observe it from the field
2. Sell the three EMA moving averages when they can't exceed 89EMA with downward crossing
3, many times can not break the new low when prices go sideways profit
4. Buy when the price reaches 89EMA after the convergence of triangle 3 is broken
5, the Angle of price rise slowed and closed below the 21 moving average when profit
6. Left field observation during transverse oscillation.
Sit tight while news or data cause prices to fall quickly
8. Buy when the price triangle breaks through the 55 moving average upward
9, the price does not rise to slow down when the horizontal closed below the 21 moving average when profit
10, price horizontal shock finishing at the same time the average line also transverse finishing field observation
11, the price of the triangle after finishing through the 89 moving average to buy.At this point all the averages have turned up
12, the second time can not break through the new high when the negative line can profit
13, the price of the first time in the same period of time through 89 after the first step back can be re-bought.
中文翻译
价格在55上面运行时时只找买入信号、
价格在55下面运行时只寻找卖出信号、
第一次向上穿过55均线后的第一次回踩确认可以顺势买入、
第一次向下穿过55均线后的第一次回抽确认可以顺势卖出、
价格横盘整理，均线频繁穿越时离场观察。
21-55区间里面黄色区域为顺势加仓信号，
价格在55均线上面时K线收盘在21天均线下面时为回调信号
价格在55均线下面时K线收盘在21天均线上面时为反弹信号
在回调和反弹信号出来之后我们应该获利一半的头寸，另外一半止损放到盈亏平衡的地方。
均线非常适合趋势性很强的品种，并不适合震荡行情。
只有在震荡行情结束时均线向上或向下发散时才有被运用的可能。
1、横盘阶段均线反复纠缠，离场观察
2、三条EMA均线向下交叉回抽无法超越55EMA时卖出
3、多次不能破新低时价格走横时获利
4、价格在3处三角形收敛被突破后站上了55EMA时买入
5、价格上涨角度变缓并收盘在21均线下面时获利
6、横盘震荡时离场观察。
7、见死不救新闻或数据导致价格快速下跌时观望
8、价格三角形向上突破时穿过55均线时买入
9、价格不升减速走横时收盘于21均线下面时获利
10、价格横盘震荡整理同时均线也横向整理时离场观察
11、价格突破三角形整理后重新穿过89均线时买入。此时所有均线已经向上翘头
12、第二次不能突破新高时收阴线可以获利
13、价格在同一个时间周期内第一次穿过89以后的第一次回踩可以重新买入
14、21-55作为牛熊的分水岭。在21-55区域之下只考虑做空，21-55之上只考虑做多。如果21-55走横则以位置决定高位倾向空低位倾向多。
15、K线会因为指标的设置自动变成两个颜色块，绿色看涨，红色看跌。做趋势看K线颜色。牛市的红色可以当成入场K熊市绿色当成入场K
KingEMA21-55-89-144I used the moving average with the habit of 21-55, so added two moving average, one is the short line 8EMA, the other is the medium and long line 89ema
Explain the application of moving averages through the disk surface:
When the price runs above 89, it only looks for the buy signal.
When the price runs below 89, it only looks for sell signals.
The first step up through the 89 moving average after the first confirmation can buy homeoply,
The first pull down after crossing the 89 moving average for the first time confirms that it can be sold in line with the trend.
Price horizontal finishing, moving average frequently across the field observation.
The yellow area in the interval from 8 to 21 is the homeopathic warehouse addition signal.
When the price is above the 89 moving average, the k-line closes below the 21-day moving average as a callback signal
Prices below the 89 ema close above the 21 - day ema as a rebound signal
After the correction and rebound signals come out, we should make half of the profit and the other half of the stop loss in the break-even place.
Moving average is very suitable for the trend of strong varieties, is not suitable for volatile market.
Only at the end of the shock market moving average upward or downward divergent when it is possible to be used.
1. Repeatedly entangle the mean line of horizontal disk stage and observe it from the field
2. Sell the three EMA moving averages when they can't exceed 89EMA with downward crossing
3, many times can not break the new low when prices go sideways profit
4. Buy when the price reaches 89EMA after the convergence of triangle 3 is broken
5, the Angle of price rise slowed and closed below the 21 moving average when profit
6. Left field observation during transverse oscillation.
Sit tight while news or data cause prices to fall quickly
8. Buy when the price triangle breaks through the 89 moving average upward
9, the price does not rise to slow down when the horizontal closed below the 21 moving average when profit
10, price horizontal shock finishing at the same time the average line also transverse finishing field observation
11, the price of the triangle after finishing through the 89 moving average to buy.At this point all the averages have turned up
12, the second time can not break through the new high when the negative line can profit
13, the price of the first time in the same period of time through 89 after the first step back can be re-bought.
中文翻译
价格在89上面运行时时只找买入信号、
价格在89下面运行时只寻找卖出信号、
第一次向上穿过89均线后的第一次回踩确认可以顺势买入、
第一次向下穿过89均线后的第一次回抽确认可以顺势卖出、
价格横盘整理，均线频繁穿越时离场观察。
8-21区间里面黄色区域为顺势加仓信号，
价格在89均线上面时K线收盘在21天均线下面时为回调信号
价格在89均线下面时K线收盘在21天均线上面时为反弹信号
在回调和反弹信号出来之后我们应该获利一半的头寸，另外一半止损放到盈亏平衡的地方。
均线非常适合趋势性很强的品种，并不适合震荡行情。
只有在震荡行情结束时均线向上或向下发散时才有被运用的可能。
1、横盘阶段均线反复纠缠，离场观察
2、三条EMA均线向下交叉回抽无法超越89EMA时卖出
3、多次不能破新低时价格走横时获利
4、价格在3处三角形收敛被突破后站上了89EMA时买入
5、价格上涨角度变缓并收盘在21均线下面时获利
6、横盘震荡时离场观察。
7、见死不救新闻或数据导致价格快速下跌时观望
8、价格三角形向上突破时穿过89均线时买入
9、价格不升减速走横时收盘于21均线下面时获利
10、价格横盘震荡整理同时均线也横向整理时离场观察
11、价格突破三角形整理后重新穿过89均线时买入。此时所有均线已经向上翘头
12、第二次不能突破新高时收阴线可以获利
13、价格在同一个时间周期内第一次穿过89以后的第一次回踩可以重新买入
14、21-55作为牛熊的分水岭。在21-55区域之下只考虑做空，21-55之上只考虑做多。如果21-55走横则以位置决定高位倾向空低位倾向多。
15、K线会因为指标的设置自动变成两个颜色块，绿色看涨，红色看跌。做趋势看K线颜色。牛市的红色可以当成入场K熊市绿色当成入场K
KingEMA8-21-55-89I used the moving average with the habit of 21-55, so added two moving average, one is the short line 8EMA, the other is the medium and long line 89ema
Explain the application of moving averages through the disk surface:
When the price runs above 89, it only looks for the buy signal.
When the price runs below 89, it only looks for sell signals.
The first step up through the 89 moving average after the first confirmation can buy homeoply,
The first pull down after crossing the 89 moving average for the first time confirms that it can be sold in line with the trend.
Price horizontal finishing, moving average frequently across the field observation.
The yellow area in the interval from 8 to 21 is the homeopathic warehouse addition signal.
When the price is above the 89 moving average, the k-line closes below the 21-day moving average as a callback signal
Prices below the 89 ema close above the 21 - day ema as a rebound signal
After the correction and rebound signals come out, we should make half of the profit and the other half of the stop loss in the break-even place.
Moving average is very suitable for the trend of strong varieties, is not suitable for volatile market.
Only at the end of the shock market moving average upward or downward divergent when it is possible to be used.
1. Repeatedly entangle the mean line of horizontal disk stage and observe it from the field
2. Sell the three EMA moving averages when they can't exceed 89EMA with downward crossing
3, many times can not break the new low when prices go sideways profit
4. Buy when the price reaches 89EMA after the convergence of triangle 3 is broken
5, the Angle of price rise slowed and closed below the 21 moving average when profit
6. Left field observation during transverse oscillation.
Sit tight while news or data cause prices to fall quickly
8. Buy when the price triangle breaks through the 89 moving average upward
9, the price does not rise to slow down when the horizontal closed below the 21 moving average when profit
10, price horizontal shock finishing at the same time the average line also transverse finishing field observation
11, the price of the triangle after finishing through the 89 moving average to buy.At this point all the averages have turned up
12, the second time can not break through the new high when the negative line can profit
13, the price of the first time in the same period of time through 89 after the first step back can be re-bought.
中文翻译
价格在89上面运行时时只找买入信号、
价格在89下面运行时只寻找卖出信号、
第一次向上穿过89均线后的第一次回踩确认可以顺势买入、
第一次向下穿过89均线后的第一次回抽确认可以顺势卖出、
价格横盘整理，均线频繁穿越时离场观察。
8-21区间里面黄色区域为顺势加仓信号，
价格在89均线上面时K线收盘在21天均线下面时为回调信号
价格在89均线下面时K线收盘在21天均线上面时为反弹信号
在回调和反弹信号出来之后我们应该获利一半的头寸，另外一半止损放到盈亏平衡的地方。
均线非常适合趋势性很强的品种，并不适合震荡行情。
只有在震荡行情结束时均线向上或向下发散时才有被运用的可能。
1、横盘阶段均线反复纠缠，离场观察
2、三条EMA均线向下交叉回抽无法超越89EMA时卖出
3、多次不能破新低时价格走横时获利
4、价格在3处三角形收敛被突破后站上了89EMA时买入
5、价格上涨角度变缓并收盘在21均线下面时获利
6、横盘震荡时离场观察。
7、见死不救新闻或数据导致价格快速下跌时观望
8、价格三角形向上突破时穿过89均线时买入
9、价格不升减速走横时收盘于21均线下面时获利
10、价格横盘震荡整理同时均线也横向整理时离场观察
11、价格突破三角形整理后重新穿过89均线时买入。此时所有均线已经向上翘头
12、第二次不能突破新高时收阴线可以获利
13、价格在同一个时间周期内第一次穿过89以后的第一次回踩可以重新买入
14、21-55作为牛熊的分水岭。在21-55区域之下只考虑做空，21-55之上只考虑做多。如果21-55走横则以位置决定高位倾向空低位倾向多。
15、K线会因为指标的设置自动变成两个颜色块，绿色看涨，红色看跌。做趋势看K线颜色。牛市的红色可以当成入场K熊市绿色当成入场K
Bernoulli Process - Binary Entropy FunctionThis indicator is the Bernoulli Process or Wikipedia - Binary Entropy Function . Within Information Theory, Entropy is the measure of available information, here we use a binary variable 0 or 1 (P) and (1-P) (Bernoulli Function/Distribution), and combined with the Shannon Entropy measurement. As you can see below, it produces some wonderful charts and signals, using price, volume, or both summed together. The chart below shows you a couple of options and some critical details on the indicator. The best part about this is the simplicity, all of this information in a couple of lines of code.
Using the indicator:
The longer the Entropy measurement the more information you are capturing, so the analogy is, the shorter the signal, the less information you have available to utilize. You'll run into your Nyquist frequencies below a length of 5. I've found values between 9 and 22 work well to gather enough measurements. You also have an averaging summation that measures the weight or importance of the information over the summation period. This is also used for highlighting when you have an information signal above the 5% level (2 sigma) and then can be adjusted using the Percent Rank Variable. Finally, you can plot the individual signals (Price or Volume) to get another set of measurements to utilize. As can be seen in the chart below, the volume moves before price (but hopefully you already knew that)
At its core, this is taking the Binary Entropy measurement (using a Bernoulli distribution) for price and volume. I've subtracted the volume from the price so that you can use it like a MACD, also for shorter time frames (7, 9, 11) you can get divergences on the histogram. These divergences are primarily due to the weekly nature of the markets (5 days, 10 days is two weeks,...so 9 is measuring the last day of the past two weeks...so 11 is measuring the current day and the past two weeks).
Here are a couple of other examples, assuming you just love BTC, Stocks, or FOREX. I fashioned up a strategy to show the potential of the indicator.
BTC-Strategy
Stock-Strategy (#loveyouNFLX)
FOREX - (for everyone hopped up on 40X leverage)
Divergence Histogram for Many IndicatorHello Traders,
This script analyses divergences for 11 predefined indicators and then draws column on the graph. Red columns for negatif divergence (means prices may go down or trend reversal), Lime columns for positive divergences (means prices may go up or trend reversal)
The script uses Pivot Points and on each bar it checks divergence between last Pivot Point and current High/Low and if it finds any divergence then immediately draws column. There is no Latency/Lag.
There are predefined 11 indicators in the script, which are RSI , MACD , MACD Histogram, Stochastic , CCI , Momentum, OBV, Diosc, VWMACD, CMF and MFI.
Smaller Pivot Point Period check smaller areas and if you use smaller numbers it would be more sensitive and may give alerts very often. So you should set it accordingly.
There is "Check Cut-Through in indicators" option, I recomment you to enable it. it checks that there is cut-through in indicators or not, if no cut-through then it's shown as valid divergence.
You should see following one as well if you haven't yet:
Enjoy!
candlestick patternsCleaning up and updating vcsWo8mh-Candlestick-Patterns-Identified-updated-3-11-15 .
As I learn more candlestick patterns I'll add them in.
Please post requests and any potential implementations I could port to pine script.
I'm applying autopep8 as best I can for readability.
MAC-Z & MACD Leader signal [ChuckBanger]This is a combination of my MACD Leader script and MAC-Z with option to add Laguerre filter. The advantage of the MAC-Z over MACD is that it is a more accurate and “assumption-free” indicator that can more accurately describe how a market actually perform. But you can use this as a regular MACD indicator.
Crossovers signals
The MAC-Z line and signal line can be utilized in the same way as a stochastic oscillator, with the crossover between the two lines providing buy and sell signals. As with most crossover strategies, a buy signal comes when the shorter-term, more reactive line – in this case the MAC-Z line (blue line) crosses above the slower signal line (orange line). For example, when the MAC-Z line crosses below the signal line it provides a bearish sell signal.
Zero line crossing
The zero cross strategy is based on either of the lines crossing the zero line. If the MAC-Z crosses the zero line from below, it is a signal for a possible new uptrend, while the MAC-Z crossing from above is a signal that a new downtrend may be starting. This is special powerful if the lines has a fast up or down movement but the price action doesn't reflect that movement.
Divergences
Bearish and bullish divergences is my favorite signals. When price action and oscillators follow the same path it is called Convergences, when they don’t, it’s called a Divergence. Don't confuse the two because they have not the same meaning. But be aware that for example during consolidation or low liquidity, some small divergences between price and indicators might form, but that doesn't mean we should consider them as real divergences.
There is many different types of divergences. It is easier to show a picture then explaining it so I recommend you to check out the link below. Especially the top image. It sums this up very well
medium.com
MACD Leader
The MACD leader is only showing the crossing of MACD as a vertical line
Green vertical line = MACD Leader Bullish Cross
Red vertical line = MACD Leader Bearish Cross
MACD Leader:
MAC-Z:
More Information
cssanalytics.wordpress.com
en.wikipedia.org
drive.google.com
Edward EMA 8-21-89-144Explain the application of moving averages through the disk surface:
When the price runs above 89, it only looks for the buy signal.
When the price runs below 89, it only looks for sell signals.
The first step up through the 89 moving average after the first confirmation can buy homeoply,
The first pull down after crossing the 89 moving average for the first time confirms that it can be sold in line with the trend.
Price horizontal finishing, moving average frequently across the field observation.
The yellow area in the interval from 8 to 21 is the homeopathic warehouse addition signal.
When the price is above the 89 moving average, the k-line closes below the 21-day moving average as a callback signal
Prices below the 89 ema close above the 21 - day ema as a rebound signal
After the correction and rebound signals come out, we should make half of the profit and the other half of the stop loss in the break-even place.
Moving average is very suitable for the trend of strong varieties, is not suitable for volatile market.
Only at the end of the shock market moving average upward or downward divergent when it is possible to be used.
1. Repeatedly entangle the mean line of horizontal disk stage and observe it from the field
2. Sell the three EMA moving averages when they can't exceed 89EMA with downward crossing
3, many times can not break the new low when prices go sideways profit
4. Buy when the price reaches 89EMA after the convergence of triangle 3 is broken
5, the Angle of price rise slowed and closed below the 21 moving average when profit
6. Left field observation during transverse oscillation.
Sit tight while news or data cause prices to fall quickly
8. Buy when the price triangle breaks through the 89 moving average upward
9, the price does not rise to slow down when the horizontal closed below the 21 moving average when profit
10, price horizontal shock finishing at the same time the average line also transverse finishing field observation
11, the price of the triangle after finishing through the 89 moving average to buy.At this point all the averages have turned up
12, the second time can not break through the new high when the negative line can profit
13, the price of the first time in the same period of time through 89 after the first step back can be re-bought.
通过盘面讲解均线运用：
价格在89上面运行时时只找买入信号、
价格在89下面运行时只寻找卖出信号、
第一次向上穿过89均线后的第一次回踩确认可以顺势买入、
第一次向下穿过89均线后的第一次回抽确认可以顺势卖出、
价格横盘整理，均线频繁穿越时离场观察。
8-21区间里面黄色区域为顺势加仓信号，
价格在89均线上面时K线收盘在21天均线下面时为回调信号
价格在89均线下面时K线收盘在21天均线上面时为反弹信号
在回调和反弹信号出来之后我们应该获利一半的头寸，另外一半止损放到盈亏平衡的地方。
均线非常适合趋势性很强的品种，并不适合震荡行情。
只有在震荡行情结束时均线向上或向下发散时才有被运用的可能。
1、横盘阶段均线反复纠缠，离场观察
2、三条EMA均线向下交叉回抽无法超越89EMA时卖出
3、多次不能破新低时价格走横时获利
4、价格在3处三角形收敛被突破后站上了89EMA时买入
5、价格上涨角度变缓并收盘在21均线下面时获利
6、横盘震荡时离场观察。
7、见死不救新闻或数据导致价格快速下跌时观望
8、价格三角形向上突破时穿过89均线时买入
9、价格不升减速走横时收盘于21均线下面时获利
10、价格横盘震荡整理同时均线也横向整理时离场观察
11、价格突破三角形整理后重新穿过89均线时买入。此时所有均线已经向上翘头
12、第二次不能突破新高时收阴线可以获利
13、价格在同一个时间周期内第一次穿过89以后的第一次回踩可以重新买入
14、89-144作为牛熊的分水岭。在89-144区域之下只考虑做空，89-144只考虑做多。如果89-144走横则以位置决定高位倾向空低位倾向多。
15、K线会因为指标的设置自动变成两个颜色块，绿色看涨，红色看跌。做趋势看K线颜色。牛市的红色可以当成入场K熊市绿色当成入场K
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
[astropark] MACD, RSI+, AO, DMI, ADX, OBV, ADI//******************************************************************************
// Copyright by astropark v4.1.0
// MACD, RSI+, Awesome Oscillator, DMI, ADX, OBV, ADI
// 24/10/2018 Added RSI with Center line to have clear glue of current trend
// 10/12/2018 Added MACD
// 13/12/2018 Added multiplier for MACD in order to make it clearly visible over RSI graph
// 11/01/2019 Added Awesome Ascillator (AO)
// 11/01/2019 Added Directional Movement Index (DMI) with ADX
// 14/01/2019 Added On Balance Volume (OBV)
// 14/01/2019 Added Accelerator Decelerator Indicator (ADI)
//******************************************************************************
[astropark] MACD, RSI+, Awesome Oscillator, DMI, ADX, OBV//******************************************************************************
// Copyright by astropark v4.0.0
// MACD, RSI+, Awesome Oscillator, DMI, ADX, OBV
// 24/10/2018 Added RSI with Center line to have clear glue of current trend
// 10/12/2018 Added MACD
// 13/12/2018 Added multiplier for MACD in order to make it clearly visible over RSI graph
// 11/01/2019 Added Awesome Oscillator (AO)
// 11/01/2019 Added Directional Movement Index (DMI) with ADX
// 14/01/2019 Added On Balance Volume (OBV)
//******************************************************************************
[astropark] MACD, RSI+, Awesome Oscillator, DMI with ADX//******************************************************************************
// Copyright by astropark v3.1.0
// MACD, RSI+, Awesome Oscillator, DMI, ADX
// 24/10/2018 Added RSI with Center line to have clear glue of current trend
// 10/12/2018 Added MACD
// 13/12/2018 Added multiplier for MACD in order to make it clearly visible over RSI graph
// 11/01/2019 Added Awesome Ascillator (AO)
// 11/01/2019 Added Directional Movement Index (DMI) with ADX
//******************************************************************************
King 4EMA TraderKing 4EMA trader 8/21/89EMA+(233)V3.3
Explain the application of moving averages through the disk surface:
When the price runs above 89, it only looks for the buy signal.
When the price runs below 89, it only looks for sell signals.
The first step up through the 89 moving average after the first confirmation can buy homeoply,
The first pull down after crossing the 89 moving average for the first time confirms that it can be sold in line with the trend.
Price horizontal finishing, moving average frequently across the field observation.
The yellow area in the interval from 8 to 21 is the homeopathic warehouse addition signal.
When the price is above the 89 moving average, the k-line closes below the 21-day moving average as a callback signal
Prices below the 89 ema close above the 21 - day ema as a rebound signal
After the correction and rebound signals come out, we should make half of the profit and the other half of the stop loss in the break-even place.
Moving average is very suitable for the trend of strong varieties, is not suitable for volatile market.
Only at the end of the shock market moving average upward or downward divergent when it is possible to be used.
1. Repeatedly entangle the mean line of horizontal disk stage and observe it from the field
2. Sell the three EMA moving averages when they can't exceed 89EMA with downward crossing
3, many times can not break the new low when prices go sideways profit
4. Buy when the price reaches 89EMA after the convergence of triangle 3 is broken
5, the Angle of price rise slowed and closed below the 21 moving average when profit
6. Left field observation during transverse oscillation.
Sit tight while news or data cause prices to fall quickly
8. Buy when the price triangle breaks through the 89 moving average upward
9, the price does not rise to slow down when the horizontal closed below the 21 moving average when profit
10, price horizontal shock finishing at the same time the average line also transverse finishing field observation
11, the price of the triangle after finishing through the 89 moving average to buy.At this point all the averages have turned up
12, the second time can not break through the new high when the negative line can profit
13, the price of the first time in the same period of time through 89 after the first step back can be re-bought.
通过盘面讲解均线运用：
价格在89上面运行时时只找买入信号、
价格在89下面运行时只寻找卖出信号、
第一次向上穿过89均线后的第一次回踩确认可以顺势买入、
第一次向下穿过89均线后的第一次回抽确认可以顺势卖出、
价格横盘整理，均线频繁穿越时离场观察。
8-21区间里面黄色区域为顺势加仓信号，
价格在89均线上面时K线收盘在21天均线下面时为回调信号
价格在89均线下面时K线收盘在21天均线上面时为反弹信号
在回调和反弹信号出来之后我们应该获利一半的头寸，另外一半止损放到盈亏平衡的地方。
均线非常适合趋势性很强的品种，并不适合震荡行情。
只有在震荡行情结束时均线向上或向下发散时才有被运用的可能。
1、横盘阶段均线反复纠缠，离场观察
2、三条EMA均线向下交叉回抽无法超越89EMA时卖出
3、多次不能破新低时价格走横时获利
4、价格在3处三角形收敛被突破后站上了89EMA时买入
5、价格上涨角度变缓并收盘在21均线下面时获利
6、横盘震荡时离场观察。
7、见死不救新闻或数据导致价格快速下跌时观望
8、价格三角形向上突破时穿过89均线时买入
9、价格不升减速走横时收盘于21均线下面时获利
10、价格横盘震荡整理同时均线也横向整理时离场观察
11、价格突破三角形整理后重新穿过89均线时买入。此时所有均线已经向上翘头
12、第二次不能突破新高时收阴线可以获利
13、价格在同一个时间周期内第一次穿过89以后的第一次回踩可以重新买入。
Bitfinex Longs/Shorts Multi-Coin [acatwithcharts]This script plots the longs/shorts ratio derived from Bitfinex for BTCUSDLONGS, BTCUSDSHORTS, and similar for 11 top cryptocurrencies chosen selected based on marketcap, trading volume on Bitfinex, and the maximum number of times that TradingView would let me call the "security" function in one script. Included coins:
BTC, ETH, LTC, BCH, XRP, EOS, IOT (IOTA), ETC, ZEC, NEO, XMR
In addition to just plotting the ratios for the individual coins, this script also calculates for a customizable selection of the 11 coins both the average ratio and a weighted average weighted by (USD price of coin * sum of long and short positions).
I wrote it both to use both for a big picture overview of leveraged positions across major coins and to use as a Swiss army knife of longs/shorts ratio indicators for individual coins, most of which do not currently have individual scripts published.
I'm an amateur and you definitely shouldn't take anything I say or use any of my scripts as financial advice. I'd appreciate any feedback.
Stochastic Momentum IndexStochastic Momentum Index indicator script. This indicator was originally developed by William Blau (Stocks & Commodities V. 11:1 (11-18)).
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
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