Arbitrary Price Point Probability (APPP)The "Arbitrary Price Point Probability" indicator is designed to calculate the probability of a given price point occurring within a certain range of prices. The indicator uses statistical analysis to determine the likelihood of a specific price point appearing based on the market data.
The indicator works by taking the input price, which is the price point for which the probability is being calculated. The indicator then calculates the mean and standard deviation of the prices over a certain period specified by the user. The length of the period for calculating the mean and standard deviation is also specified by the user.
Once the mean and standard deviation have been calculated, the indicator uses them to calculate the probability of the input price point occurring within the range of prices over the specified period. The indicator does this by calculating the z-score, which is the number of standard deviations between the input price point and the mean price. The z-score is then used to calculate the probability using a t-distribution probability density function.
The t-distribution probability density function used by the indicator is a mathematical function that describes the likelihood of obtaining a particular value from a t-distribution. A t-distribution is a statistical distribution used when the sample size is small, and the population standard deviation is unknown.
The indicator also uses a binary search algorithm to find the t-value for a given confidence level. The t-value is the number of standard deviations from the mean at which the confidence interval is set. The confidence level is set by the user, and the default value is 99%.
Overall, the "Arbitrary Price Point Probability" indicator is a useful tool for traders who want to determine the probability of a particular price point occurring within a certain range of prices. The indicator can be used in conjunction with other technical analysis tools to make more informed trading decisions.
在腳本中搜尋"binary"
Delta-Agnostic Correlation Coefficient (alt)Calculate a sort of correlation between two symbols based only on the sign of their changes, regardless of the amplitude of price change.
When positive, the two symbols tend to move together. When negative, the symbols move in opposite directions.
Since there is no significance calculation, and that the result is binary, keep in mind that correlation will always tend to go towards 1 or -1 even when there is no correlation. To reduce this issue, an EMA or SMA is applied to smooth out transitions: SMA smoothes over the selected length period but adds lag, whereas EMA smoothes amplitude without any additional lag. Hence, to know if the correlation is true or not, try to look at the amplitude and the number of consecutive days the correlation is maintained (both quantities are related), because when the correlation is spurious, it will tend to switch more or less alternatively between 1 and -1 and hence will hover around 0, whereas if the correlation is true, it will get further away from 0 and closer to 1 or -1.
In addition, since there is some time lag for the correlation to switch sign, the area is colored to know the current candle's correlation, regardless of past data's correlation: blue is a positive correlation (1), yellow is negative. The coloring can allow to know a trend reversal early on, but it's noisy.
Finally, symbols with closing days are better accounted for, with the correlation set to 0 on closed days (e.g., on week-ends), and the area is then colored in gray to signal that there is no new correlation data.
This is an improved fork over the original indicator by alexjvale, please show him some love if you like this work:
Candlestick Pattern Criteria and Analysis Indicator█ OVERVIEW
Define, then locate the presence of a candle that fits a specific criteria. Run a basic calculation on what happens after such a candle occurs.
Here, I’m not giving you an edge, but I’m giving you a clear way to find one.
IMPORTANT NOTE: PLEASE READ:
THE INDICATOR WILL ALWAYS INITIALLY LOAD WITH A RUNTIME ERROR. WHEN INITIALLY LOADED THERE NO CRITERIA SELECTED.
If you do not select a criteria or run a search for a criteria that doesn’t exist, you will get a runtime error. If you want to force the chart to load anyway, enable the debug panel at the bottom of the settings menu.
Who this is for:
- People who want to engage in TradingView for tedious and challenging data analysis related to candlestick measurement and occurrence rate and signal bar relationships with subsequent bars. People who don’t know but want to figure out what a strong bullish bar or a strong bearish bar is.
Who this is not for:
- People who want to be told by an indicator what is good or bad or buy or sell. Also, not for people that don’t have any clear idea on what they think is a strong bullish bar or a strong bearish bar and aren’t willing to put in the work.
Recommendation: Use on the candle resolution that accurately reflects your typical holding period. If you typically hold a trade for 3 weeks, use 3W candles. If you hold a trade for 3 minutes, use 3m candles.
Tldr; Read the tool tips and everything above this line. Let me know any issues that arise or questions you have.
█ CONCEPTS
Many trading styles indicate that a certain candle construct implies a bearish or bullish future for price. That said, it is also common to add to that idea that the context matters. Of course, this is how you end up with all manner of candlestick patterns accounting for thousands of pages of literature. No matter the context though, we can distill a discretionary trader's decision to take a trade based on one very basic premise: “A trader decides to take a trade on the basis of the rightmost candle's construction and what he/she believes that candle construct implies about the future price.” This indicator vets that trader’s theory in the most basic way possible. It finds the instances of any candle construction and takes a look at what happens on the next bar. This current bar is our “Signal Bar.”
█ GUIDE
I said that we vet the theory in the most basic way possible. But, in truth, this indicator is very complex as a result of there being thousands of ways to define a ‘strong’ candle. And you get to define things on a very granular level with this indicator.
Features:
1. Candle Highlighting
When the user’s criteria is met, the candle is highlighted on the chart.
The following candle is highlighted based on whether it breaks out, breaks down, or is an inside bar.
2. User-Defined Criteria
Criteria that you define include:
Candle Type: Bull bars, Bear bars, or both
Candle Attributes
Average Size based on Standard Deviation or Average of all potential bars in price history
Search within a specific price range
Search within a specific time range
Clarify time range using defined sessions and with or without weekends
3. Strike Lines on Candle
Often you want to know how price reacts when it gets back to a certain candle. Also it might be true that candle types cluster in a price region. This can be identified visually by adding lines that extend right on candles that fit the criteria.
4. User-Defined Context
Labeled “Alternative Criteria,” this facet of the script allows the user to take the context provided from another indicator and import it into the indicator to use as a overriding criteria. To account for the fact that the external indicator must be imported as a float value, true (criteria of external indicator is met) must be imported as 1 and false (criteria of external indicator is not met) as 0. Basically a binary Boolean. This can be used to create context, such as in the case of a traditional fractal, or can be used to pair with other signals.
If you know how to code in Pinescript, you can save a copy and simply add your own code to the section indicated in the code and set your bull and bear variables accordingly and the code should compile just fine with no further editing needed.
Included with the script to maximize out-of-the-box functionality, there is preloaded as alternative criteria a code snippet. The criteria is met on the bull side when the current candle close breaks out above the prior candle high. The bear criteria is met when the close breaks below the prior candle. When Alternate Criteria is run by itself, this is the only criteria set and bars are highlighted when it is true. You can qualify these candles by adding additional attributes that you think would fit well.
Using Alternative Criteria, you are essentially setting a filter for the rest of the criteria.
5. Extensive Read Out in the Data Window (right side bar pop out window).
As you can see in the thumbnail, there is pasted a copy of the Data Window Dialogue. I am doubtful I can get the thumbnail to load up perfectly aligned. Its hard to get all these data points in here. It may be better suited for a table at this point. Let me know what you think.
The primary, but not exclusive, purpose of what is in the Data Window is to talk about how often your criteria happens and what happens on the next bar. There are a lot of pieces to this.
Red = Values pertaining to the size of the current bar only
Blue = Values pertaining or related to the total number of signals
Green = Values pertaining to the signal bars themselves, including their measurements
Purple = Values pertaining to bullish bars that happen after the signal bar
Fuchsia = Values pertaining to bearish bars that happen after the signal bar
Lime = Last four rows which are your percentage occurrence vs total signals percentages
The best way I can explain how to understand parts you don’t understand otherwise in the data window is search the title of the row in the code using ‘ctrl+f’ and look at it and see if it makes more sense.
█ [b}Available Candle Attributes
Candle attributes can be used in any combination. They include:
[*}Bodies
[*}High/Low Range
[*}Upper Wick
[*}Lower Wick
[*}Average Size
[*}Alternative Criteria
Criteria will evaluate each attribute independently. If none is set for a particular attribute it is bypassed.
Criteria Quantity can be in Ticks, Points, or Percentage. For percentage keep in mind if using anything involving the candle range will not work well with percentage.
Criteria Operators are “Greater Than,” “Less Than,” and “Threshold.” Threshold means within a range of two numbers.
█ Problems with this methodology and opportunities for future development:
#1 This kind of work is hard.
If you know what you’re doing you might be able to find success changing out the inputs for loops and logging results in arrays or matrices, but to manually go through and test various criteria is a lot of work. However, it is rewarding. At the time of publication in early Oct 2022, you will quickly find that you get MUCH more follow through on bear bars than bull bars. That should be obvious because we’re in the middle of a bear market, but you can still work with the parameters and contextual inputs to determine what maximizes your probability. I’ve found configurations that yield 70% probability across the full series of bars. That’s an edge. That means that 70% of the time, when this criteria is met, the next bar puts you in profit.
#2 The script is VERY heavy.
Takes an eternity to load. But, give it a break, it’s doing a heck of a lot! There is 10 unique arrays in here and a loop that is a bit heavy but gives us the debug window.
#3 If you don’t have a clear idea its hard to know where to start.
There are a lot of levers to pull on in this script. Knowing which ones are useful and meaningful is very challenging. Combine that with long load times… its not great.
#4 Your brain is the only thing that can optimize your results because the criteria come from your mind.
Machine learning would be much more useful here, but for now, you are the machine. Learn.
#5 You can’t save your settings.
So, when you find a good combo, you’ll have to write it down elsewhere for future reference. It would be nice if we could save templates on custom indicators like we can on some of the built in drawing tools, but I’ve had no success in that. So, I recommend screenshotting your settings and saving them in Notion.so or some other solid record keeping database. Then you can go back and retrieve those settings.
#6 no way to export these results into conditions that can be copy/pasted into another script.
Copy/Paste of labels or tables would be the best feature ever at this point. Because you could take the criteria and put it in a label, copy it and drop it into another strategy script or something. But… men can dream.
█ Opportunities to PineCoders Learn:
1. In this script I’m importing libraries, showing some of my libraries functionality. Hopefully that gives you some ideas on how to use them too.
The price displacement library (which I love!)
Creative and conventional ways of using debug()
how to display arrays and matrices on charts
I didn’t call in the library that holds the backtesting function. But, also demonstrating, you can always pull the library up and just copy/paste the function out of there and into your script. That’s fine to do a lot of the time.
2. I am using REALLY complicated logic in this script (at least for me). I included extensive descriptions of this ? : logic in the text of the script. I also did my best to bracket () my logic groups to demonstrate how they fit together, both for you and my future self.
3. The breakout, built-in, “alternative criteria” is actually a small bit of genius built in there if you want to take the time to understand that block of code and think about some of the larger implications of the method deployed.
As always, a big thank you to TradingView and the Pinescript community, the Pinescript pros who have mentored me, and all of you who I am privileged to help in their Pinescripting journey.
"Those who stay will become champions" - Bo Schembechler
HexLibrary "Hex"
Hex String Utility
intToHex(_n)
helper Binary half octet to hex character
Parameters:
_n : Digits to convert
fromDigits(_input, _buffer)
Digits to Hex String output
Parameters:
_input : Integer Input
_buffer : Number of 0's to pad Hex with
Returns: string output hex character value buffered to desired length (00-ff default)
FunctionKellyCriterionLibrary "FunctionKellyCriterion"
Kelly criterion methods.
the kelly criterion helps with the decision of how much one should invest in
a asset as long as you know the odds and expected return of said asset.
simplified(win_p, rr)
simplified version of the kelly criterion formula.
Parameters:
win_p : float, probability of winning.
rr : float, reward to risk rate.
Returns: float, optimal fraction to risk.
usage:
simplified(0.55, 1.0)
partial(win_p, loss_p, win_rr, loss_rr)
general form of the kelly criterion formula.
Parameters:
win_p : float, probability of the investment returns a positive outcome.
loss_p : float, probability of the investment returns a negative outcome.
win_rr : float, reward on a positive outcome.
loss_rr : float, reward on a negative outcome.
Returns: float, optimal fraction to risk.
usage:
partial(0.6, 0.4, 0.6, 0.1)
from_returns(returns)
Calculate the fraction to invest from a array of returns.
Parameters:
returns : array trade/asset/strategy returns.
Returns: float, optimal fraction to risk.
usage:
from_returns(array.from(0.1,0.2,0.1,-0.1,-0.05,0.05))
final_f(fraction, max_expected_loss)
Final fraction, eg. if fraction is 0.2 and expected max loss is 10%
then you should size your position as 0.2/0.1=2 (leverage, 200% position size).
Parameters:
fraction : float, aproximate percent fraction invested.
max_expected_loss : float, maximum expected percent on a loss (ex 10% = 0.1).
Returns: float, final fraction to invest.
usage:
final_f(0.2, 0.5)
hpr(fraction, trade, biggest_loss)
Holding Period Return function
Parameters:
fraction : float, aproximate percent fraction invested.
trade : float, profit or loss in a trade.
biggest_loss : float, value of the biggest loss on record.
Returns: float, multiplier of effect on equity so that a win of 5% is 1.05 and loss of 5% is 0.95.
usage:
hpr(fraction=0.05, trade=0.1, biggest_loss=-0.2)
twr(returns, rr, eps)
Terminal Wealth Relative, returns a multiplier that can be applied
to the initial capital that leadds to the final balance.
Parameters:
returns : array, list of trade returns.
rr : float , reward to risk rate.
eps : float , minimum resolution to void zero division.
Returns: float, optimal fraction to invest.
usage:
twr(returns=array.from(0.1,-0.2,0.3), rr=0.6)
ghpr(returns, rr, eps)
Geometric mean Holding Period Return, represents the average multiple made on the stake.
Parameters:
returns : array, list of trade returns.
rr : float , reward to risk rate.
eps : float , minimum resolution to void zero division.
Returns: float, multiplier of effect on equity so that a win of 5% is 1.05 and loss of 5% is 0.95.
usage:
ghpr(returns=array.from(0.1,-0.2,0.3), rr=0.6)
run_coin_simulation(fraction, initial_capital, n_series, n_periods)
run multiple coin flipping (binary outcome) simulations.
Parameters:
fraction : float, fraction of capital to bet.
initial_capital : float, capital at the start of simulation.
n_series : int , number of simulation series.
n_periods : int , number of periods in each simulation series.
Returns: matrix(n_series, n_periods), matrix with simulation results per row.
usage:
run_coin_simulation(fraction=0.1)
run_asset_simulation(returns, fraction, initial_capital)
run a simulation over provided returns.
Parameters:
returns : array, trade, asset or strategy percent returns.
fraction : float , fraction of capital to bet.
initial_capital : float , capital at the start of simulation.
Returns: array, array with simulation results.
usage:
run_asset_simulation(returns=array.from(0.1,-0.2,0.-3,0.4), fraction=0.1)
strategy_win_probability()
calculate strategy() current probability of positive outcome in a trade.
strategy_avg_won()
calculate strategy() current average won on a trade with positive outcome.
strategy_avg_loss()
calculate strategy() current average lost on a trade with negative outcome.
Overloaded Volume-Canddle v1This indicator will detect the candle has volume "too strong" base on "n" previous candle.
The Yellow Line is avg volume base n candle previous.
The Red line show over power volume of canlde.
Important : This indicator can use for forex, but i recommend it for binary options only.
Om Boy CandlesUsed by me to play binary option.
A little project to help my nephew decide which candle to refer to make SNR lines
pandas_taLibrary "pandas_ta"
Level: 3
Background
Today is the first day of 2022 and happy new year every tradingviewers! May health and wealth go along with you all the time. I use this chance to publish my 1st PINE v5 lib : pandas_ta
This is not a piece of cake like thing, which cost me a lot of time and efforts to build this lib. Beyond 300 versions of this script was iterated in draft.
Function
Library "pandas_ta"
PINE v5 Counterpart of Pandas TA - A Technical Analysis Library in Python 3 at github.com
The Original Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns.
I realized most of indicators except Candlestick Patterns because tradingview built-in Candlestick Patterns are even more powerful!
I use this to verify pandas_ta python version indicators for myself, but I realize that maybe many may need similar lib for pine v5 as well.
Function Brief Descriptions (Pls find details in script comments)
bton --> Binary to number
wcp --> Weighted Closing Price (WCP)
counter --> Condition counter
xbt --> Between
ebsw --> Even Better SineWave (EBSW)
ao --> Awesome Oscillator (AO)
apo --> Absolute Price Oscillator (APO)
xrf --> Dynamic shifted values
bias --> Bias (BIAS)
bop --> Balance of Power (BOP)
brar --> BRAR (BRAR)
cci --> Commodity Channel Index (CCI)
cfo --> Chande Forcast Oscillator (CFO)
cg --> Center of Gravity (CG)
cmo --> Chande Momentum Oscillator (CMO)
coppock --> Coppock Curve (COPC)
cti --> Correlation Trend Indicator (CTI)
dmi --> Directional Movement Index(DMI)
er --> Efficiency Ratio (ER)
eri --> Elder Ray Index (ERI)
fisher --> Fisher Transform (FISHT)
inertia --> Inertia (INERTIA)
kdj --> KDJ (KDJ)
kst --> 'Know Sure Thing' (KST)
macd --> Moving Average Convergence Divergence (MACD)
mom --> Momentum (MOM)
pgo --> Pretty Good Oscillator (PGO)
ppo --> Percentage Price Oscillator (PPO)
psl --> Psychological Line (PSL)
pvo --> Percentage Volume Oscillator (PVO)
qqe --> Quantitative Qualitative Estimation (QQE)
roc --> Rate of Change (ROC)
rsi --> Relative Strength Index (RSI)
rsx --> Relative Strength Xtra (rsx)
rvgi --> Relative Vigor Index (RVGI)
slope --> Slope
smi --> SMI Ergodic Indicator (SMI)
sqz* --> Squeeze (SQZ) * NOTE: code sufferred from very strange error, code was commented.
sqz_pro --> Squeeze PRO(SQZPRO)
xfl --> Condition filter
stc --> Schaff Trend Cycle (STC)
stoch --> Stochastic (STOCH)
stochrsi --> Stochastic RSI (STOCH RSI)
trix --> Trix (TRIX)
tsi --> True Strength Index (TSI)
uo --> Ultimate Oscillator (UO)
willr --> William's Percent R (WILLR)
alma --> Arnaud Legoux Moving Average (ALMA)
xll --> Dynamic rolling lowest values
dema --> Double Exponential Moving Average (DEMA)
ema --> Exponential Moving Average (EMA)
fwma --> Fibonacci's Weighted Moving Average (FWMA)
hilo --> Gann HiLo Activator(HiLo)
hma --> Hull Moving Average (HMA)
hwma --> HWMA (Holt-Winter Moving Average)
ichimoku --> Ichimoku Kinkō Hyō (ichimoku)
jma --> Jurik Moving Average Average (JMA)
kama --> Kaufman's Adaptive Moving Average (KAMA)
linreg --> Linear Regression Moving Average (linreg)
mgcd --> McGinley Dynamic Indicator
rma --> wildeR's Moving Average (RMA)
sinwma --> Sine Weighted Moving Average (SWMA)
ssf --> Ehler's Super Smoother Filter (SSF) © 2013
supertrend --> Supertrend (supertrend)
xsa --> X simple moving average
swma --> Symmetric Weighted Moving Average (SWMA)
t3 --> Tim Tillson's T3 Moving Average (T3)
tema --> Triple Exponential Moving Average (TEMA)
trima --> Triangular Moving Average (TRIMA)
vidya --> Variable Index Dynamic Average (VIDYA)
vwap --> Volume Weighted Average Price (VWAP)
vwma --> Volume Weighted Moving Average (VWMA)
wma --> Weighted Moving Average (WMA)
zlma --> Zero Lag Moving Average (ZLMA)
entropy --> Entropy (ENTP)
kurtosis --> Rolling Kurtosis
skew --> Rolling Skew
xev --> Condition all
zscore --> Rolling Z Score
adx --> Average Directional Movement (ADX)
aroon --> Aroon & Aroon Oscillator (AROON)
chop --> Choppiness Index (CHOP)
xex --> Condition any
cksp --> Chande Kroll Stop (CKSP)
dpo --> Detrend Price Oscillator (DPO)
long_run --> Long Run
psar --> Parabolic Stop and Reverse (psar)
short_run --> Short Run
vhf --> Vertical Horizontal Filter (VHF)
vortex --> Vortex
accbands --> Acceleration Bands (ACCBANDS)
atr --> Average True Range (ATR)
bbands --> Bollinger Bands (BBANDS)
donchian --> Donchian Channels (DC)
kc --> Keltner Channels (KC)
massi --> Mass Index (MASSI)
natr --> Normalized Average True Range (NATR)
pdist --> Price Distance (PDIST)
rvi --> Relative Volatility Index (RVI)
thermo --> Elders Thermometer (THERMO)
ui --> Ulcer Index (UI)
ad --> Accumulation/Distribution (AD)
cmf --> Chaikin Money Flow (CMF)
efi --> Elder's Force Index (EFI)
ecm --> Ease of Movement (EOM)
kvo --> Klinger Volume Oscillator (KVO)
mfi --> Money Flow Index (MFI)
nvi --> Negative Volume Index (NVI)
obv --> On Balance Volume (OBV)
pvi --> Positive Volume Index (PVI)
dvdi --> Dual Volume Divergence Index (DVDI)
xhh --> Dynamic rolling highest values
pvt --> Price-Volume Trend (PVT)
Remarks
I also incorporated func descriptions and func test script in commented mode, you can test the functino with the embedded test script and modify them as you wish.
This is a Level 3 free and open source indicator library.
Feedbacks are appreciated.
This is not the end of pandas_ta lib publication, but it is start point with pine v5 lib function and I will add more and more funcs into this lib for my own indicators.
Function Name List:
bton()
wcp()
count()
xbt()
ebsw()
ao()
apo()
xrf()
bias()
bop()
brar()
cci()
cfo()
cg()
cmo()
coppock()
cti()
dmi()
er()
eri()
fisher()
inertia()
kdj()
kst()
macd()
mom()
pgo()
ppo()
psl()
pvo()
qqe()
roc()
rsi()
rsx()
rvgi()
slope()
smi()
sqz_pro()
xfl()
stc()
stoch()
stochrsi()
trix()
tsi()
uo()
willr()
alma()
wcx()
xll()
dema()
ema()
fwma()
hilo()
hma()
hwma()
ichimoku()
jma()
kama()
linreg()
mgcd()
rma()
sinwma()
ssf()
supertrend()
xsa()
swma()
t3()
tema()
trima()
vidya()
vwap()
vwma()
wma()
zlma()
entropy()
kurtosis()
skew()
xev()
zscore()
adx()
aroon()
chop()
xex()
cksp()
dpo()
long_run()
psar()
short_run()
vhf()
vortex()
accbands()
atr()
bbands()
donchian()
kc()
massi()
natr()
pdist()
rvi()
thermo()
ui()
ad()
cmf()
efi()
ecm()
kvo()
mfi()
nvi()
obv()
pvi()
dvdi()
xhh()
pvt()
Ranging Market Detector [AstrideUnicorn]Determining if the market is in a trend or a range regime is a very complex problem. And knowing the answer can be, in some situations, the real holy grail. If the trader knows when the market is in a range regime, they can avoid overtrading and make moving average crossover strategies more profitable. A regime switch from a trend to a range can be a signal to close open positions. It can also be helpful when trading such instruments as short-term binary options. When the market is ranging directional moves are not expected, and the trader should be careful as opening a position in such conditions is, by some degree, a random outcome game. Range breakouts trading is one more example when knowing the market regime is critical.
We have created an indicator that predicts the current market regime. It smooths the price using the Kalman filter and analyzes the curve's slope. If the absolute value of the slope is low, then the market is in range mode and vice versa. To distinguish between the two regimes, the algorithm compares the absolute value of the slope with its long-term average.
HOW TO USE
The indicator shows the difference between the absolute slope value and its long-term average as a histogram. When a bar of the histogram is higher than the threshold level presented by the red line, the market is in a trending regime. In this regime, the background of the indicator is blue. When the market is in a range regime, the indicator background turns red.
The threshold level helps to control the lag. The greater it is, the more lagging the indicator will be. By default, this value is set to a negative value. It means that the indicator switches from range to trend a little bit earlier than the slope gets higher than the average slope. You can use the value of zero or low negative values to find the optimal tradeoff between the strength of the signals and their lag.
SETTINGS
The indicator has one input parameter called Threshold. It sets the threshold level described above. Its value should be close to zero. The less the value is, the less is the indicator's lag, but at the same time, the less confirmed the regime-switching signals are.
The use cases can be very different. And as the code is open, you can also use the indicator as a building block for your custom trading strategies.
Let us know your thoughts and suggestions!
Multi-TimeFrame SignalsI trade binary options on reversals. I use this as a confluence to prove that my analysis is correct. I use this on smaller time frames given the type of trading I do.
I do not use this as an entry trigger, so you should not blindly enter trades based on this solely . You were warned
This indicator (on default settings) will let you know when the 1 minute, 3 minute, and 5 minute timeframes RSI & Stochastics are all in an overbought or oversold state. It shows this by changing the background color on your chart to red (overbought) or green (oversold).
All of these settings are changeable. #winning
I left this as open-source as I figured out how to code this by using other open-source scripts as reference. I am a programmer that uses other languages.
I only published this script as a request from a close friend.
Financial Astrology Indexes ML Daily TrendDaily trend indicator based on financial astrology cycles detected with advanced machine learning techniques for some of the most important market indexes: DJI, UK100, SPX, IBC, IXIC, NI225, BANKNIFTY, NIFTY and GLD fund (not index) for Gold predictions. The daily price trend is forecasted through planets cycles (angular aspects, speed phases, declination zone), fast cycles are based on Moon, Mercury, Venus and Sun and Mid term cycles are based on Mars, Vesta and Ceres . The combination of all this cycles produce a daily price trend prediction that is encoded into a PineScript array using binary format "0 or 1" that represent sell and buy signals respectively. The indicator provides signals since 2021-01-01 to 2022-12-31, the past months signals purpose is to support backtesting of the indicator combined with other technical indicator entries like MAs, RSI or Stochastic . For future predictions besides 2022 a machine learning models re-train phase will be required.
When the signal moving average is increasing from 0 to 1 indicates an increase of buy force, when is decreasing from 1 to 0 indicates an increase in sell force, finally, when is sideways around the 0.4-0.6 area predicts a period of buy/sell forces equilibrium, traders indecision which result in a price congestion within a narrow price range.
We also have published same indicator for Crypto-Currencies research portfolio:
DISCLAIMER: This indicator is experimental and don’t provide financial or investment advice, the main purpose is to demonstrate the predictive power of financial astrology. Any allocation of funds following the documented machine learning model prediction is a high-risk endeavour and it’s the users responsibility to practice healthy risk management according to your situation.
Financial Astrology Crypto ML Daily TrendThis daily trend indicator is based on financial astrology cycles detected with advanced machine learning techniques for the crypto-currencies research portfolio: ADA, BAT, BNB, BTC, DASH, EOS, ETC, ETH, LINK, LTC, XLM, XMR, XRP, ZEC and ZRX. The daily price trend is forecasted through this planets cycles (angular aspects, speed, declination), fast ones are based on Moon, Mercury, Venus and Sun and Mid term cycles are based on Mars, Vesta and Ceres. The combination of all this cycles produce a daily price trend prediction that is encoded into a PineScript array using binary format "0 or 1" that represent sell and buy signals respectively. The indicator provides signals since 2021-01-01 to 2022-12-31, the past months signals purpose is to support backtesting of the indicator combined with other technical indicator entries like MAs, RSI or Stochastic. For future predictions besides 2022 a machine learning models re-train phase will be required.
The resolution of this indicator is 1D, you can tune a parameter where you can determine how many future bars of daily trend are plotted and adjust an hours shift to anticipate future signals into current bar in order to produce a leading indicator effect to anticipate the trend changes with some hours of anticipation. Combined with technical analysis indicators this daily trend is very powerful because can help to produce approximately 60% of profitable signals based on the backtesting results. You can look at our open source Github repositories to validate accuracy using the backtesting strategies we have implemented in Jesse Crypto Trading Framework as proof of concept of the predictive potential of this indicator. Alternatively, we have implemented a PineScript strategy that use this indicator, just consider that we are pending to do signals update to the period July 2021 to December 2022: This strategy have accumulated more than 110 likes and many traders have validated the predictive power of Financial Astrology.
DISCLAIMER: This indicator is experimental and don’t provide financial or investment advice, the main purpose is to demonstrate the predictive power of financial astrology. Any allocation of funds following the documented machine learning model prediction is a high-risk endeavour and it’s the users responsibility to practice healthy risk management according to your situation.
I Ching Hexagrams Part 2IMPORTANT!
Get the other indicator I Ching Hexagrams Part 1 for the full set!
Here is the first half of the I Ching's hexagrams.
Each candle generates a hexagram at close, based on that candle and the previous 5.
A green candle is yang and a red candle is yin.
In this way, price action generates a hexagram.
Use this to generate a hexagram for the day or even to find interesting patterns to trade.
Have fun!
I Ching Hexagrams Part 1IMPORTANT!
Get the other indicator I Ching Hexagrams Part 2 for the full set!
Here is the first half of the I Ching's hexagrams.
Each candle generates a hexagram at close, based on that candle and the previous 5.
A green candle is yang and a red candle is yin.
In this way, price action generates a hexagram.
Use this to generate a hexagram for the day or even to find interesting patterns to trade.
Have fun!
Technical Ratings Pro - Pump WaveThis script uses the built in Technical Ratings indicator but interprets the data visually. It plots the results for "total", "MA" and "other" as pump waves. It uses MA to plot a trend line (can be turned off in settings) . Candles are colored to the rating strength and a percentage number was added to the results. For more informations on the Technical Ratings indicator please refer to official documentation.
YouKnowTheVibesThis is mainly used for trading binary options but can be used for regular forex trading as well. If price is above BOTH EMAs and PSAR then you're looking for BUY opportunities. If price is below then you're looking for sell opportunities. Wait for price to break the most recent fractal high/low and retest; wait for a confirmation candle then enter a call/put and the expiry time is THREE candles.
Technical checklistNo one indicator is perfect. People always have their favorite indicators and maintain a bias on weighing them purely on psychological reasons other than mathematical. This technical checklist indicator collected 20 common indicators and custom ones to address the issue of a bias weighted decision.
Here, I apply machine learning using a simple sigmoid neuron network with one hidden layer and a single node to avoid artifacts. For the ease of data collection, the indicator matrix is first shown as a heatmap. Once an uptrend signal window is selected manually, an indicator matrix can be recorded in a binary format (i.e., 1 0 0 1 1 0, etc.).
For example, the following indicator matrix was retrieved from the MRNA chart (deciscion: first 5 rows, buying; last 5 rows, no buying):
1 1 0 0 0 1 1 1 1 1 0 1 0 0 1 1 0 1 1 1
1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 1
0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0 1 0 0
1 1 0 0 0 1 1 1 1 1 1 0 1 0 0 1 0 1 0 0
0 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0
1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 1
1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1
0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1
0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1
This matrix is then used as an input to train the machine learning network. With a correlated buying decision matrix as an output:
1
1
1
1
1
0
0
0
0
0
After training, the corrected weight matrix can be applied back to the indicator. And the display mode can be changed from a heatmap into a histogram to reveal buying signals visually.
Usage:
python stock_ml.py mrna_input.txt output.txt
Weight matrix output:
1.37639407
1.67969656
1.0162141
1.3184323
-1.88888442
8.32928588
-5.35777295
3.08739916
3.06464844
0.82986227
-0.53092333
-1.95045383
4.14441698
2.99179435
-0.08379438
1.70379704
0.4173048
-1.51870972
-2.14284707
-2.08513252
Corresponding indicators to the weight matrix:
1. Breakout
2. Reversal
3. Crossover of ema20 and ema60
4. Crossover of ema20 and ema120
5. MACD golden cross
6. Long cycle (MACD crossover 0)
7. RSI not overbought
8. KD not overbought and crossover
9. OBV uptrend
10. Bullish gap
11. High volume
12. Breakout up fractal
13. Rebounce of down fractal
14. Convergence
15. Turbulence reversal
16. Low resistance
17. Bullish trend (blue zone)
18. Bearish trend (red zone)
19. VIX close above ema20
20. SPY close below ema20
PS. It is recommended not to use default settings but to train your weight matrix based on underlying and timeframe.
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
TrendLine ChannelsAbstract
This script provides a set of Trendline Channels which can be tighter than Donchian Channels.
This script computes the most suitable active upper and lower trendlines and updates them as soon as possible.
We can use it for breakout signals earlier than Donchian Channels.
Introduction
Channels are tools which can mark area of values of ranging markets.
Once the price leaves the original region, range traders may start admitting they are wrong and trigger trend.
Donchian Channels (misheard as Dungeon Channels) use the highest and the lowest price to define area of values.
When the price breakouts, it just like it got released from a dungeon.
However, waiting for the price making even higher or lower may be too late and risk reward ratio may be lower.
Trendlines can make the channels tighter and we can find earlier breakout signals.
Computing Trendline Channels
(1) Find the most active trendlines
In this script, a valid trendline connects two high values or two low values.
A high value means the highest value in a trading day.
A low value means the lowest value in a trading day.
In this script, every trendline does not crossover any bar but can exactly touch them.
The most flat trendline is taken. (one for upper and one for lower)
(2) Continue to use or Change a trendline
If there is a tighter trendline available, this script changes to use it.
If the previous active trendline is broken, this script re-computes the trendline available.
When this case happens, this script display with an another color.
Otherwise, this script continues using the previous trendline.
(3) Middle line
The middle line indicates the middle value between the upper and the lower.
Parameters
Length : how many days are used for computation. The default value is 16 just because 16=4*4, using binary characteristic.
x_go_on : If the previous trendline is not breakout and there are no tighter trendlines available, we continue use it.
Color Options
(1) Upper trendline (no update or tighter)
(2) Upper trendline (changed due to breakout)
(3) Middle line
(4) Lower trendline (no update or tighter)
(5) Lower trendline (changed due to breakout)
Conclusion
Trendline Channels can be tighter than Donchian Channels and evaluate earlier breakout signals.
Comparing to known auto trendline scripts, Trendline Channels is continuous.
Once a trendline is broken, Trendline Channels can instantly point out the next available one.
If you think the price movement is boring or you cannot have good risk reward ratio, you can go to an another timeframe.
Reference
How to trade with Donchian Channels
How to trade with Trendlines
HFX Kung Fu Pips Candlestick Identifier by Trader ZThis indicator identifies the following candle stick patterns:
Bullish and Bearish Engulfing
Hammer/Hanging Man, Shooting Star
Tweezer Tops and Bottoms
Doji Star
Doji Gravestone
Made for trading Forex Binary Options but can be used in any market. When the candlestick pattern emerges the script will label the pattern. A helpful visual tool for your analysis.
Enjoy!!!!
Price MovementPrice indicator that shows the trend based on price movement.
The indicator determinants when an asset price is getting higher or lower by comparing the previous price highs and lows to the current price.
==Explanation==
In case current HL2 exceeds the previous HL2 HIGH then the columns will turn green.
In case current HL2 fails the previous HL2 LOW then the columns will turn red.
The trick here is that the trend CONTINUES to show the greens and reds, until a reversal happens to the opposite side.
This can be used to determinate trends and reversals.
Note: Bar colors are disabled by default.
You can set the lookback period at the indicator settings as well as the asset source (HL2,CLOSE, etc..). default is HL2
Quote that i like: “It is impossible to produce superior performance unless you do something different from the majority.” – John Templeton
Enjoy and like if you like :)
quantized pin bar indicator with ATRAbstract
This script computes the strength of pin bars.
This script uses the corrent and the previous two bars to compute the strength of pin bars.
The strength of pin bars can be also comared with average true range, so we can evaluate those pin bars are strong or weak.
Introduction
Pin bar is a popular price action trading strategy.
It is based on quick price rejection.
Most of existing pin bar scripts only determine if a bar is a pin bar or not.
However, evaluating the strength of pin bars is important.
If price rejection is too weak, it is difficult to trigger trend reversal.
If a pin bar is too strong, we may enter the trade too late and cannot have good profit.
In this script, it provides a method to compute to strength of pin bars.
After the strength of pin bars are quantized, they can compare with average true range, price range and trend strength, which can help us to determine where are worthy for us to open trades.
Computation
Bullish hammer : current low is lower than ( previous high or current open ) and current close.
Bearish gravestone : current high is higher than ( previous low or current open ) and current close.
Bullish engulfing and harami : ( current low or previous low ) is lower than ( previous 2nd high or previous open ) and current close.
Bearish engulfing and harami : ( current high or previous high ) is higher than ( previous 2nd low or previous open ) and current close.
Parameters
Smoothing : the type of computing average.
Length of ATR : determines the number of true ranges for computing average true range.
ATR multiplier line : the threshould that a pin bar is strong enough. For example, if this value is 0.5, it means a pin bar with 0.5*atr or more is considered a strong pin bar.
one direction pinbar : set to 1 if you want the strength of bullish pin bars and bearish pin bars are cancelled. Set to 0 if you want to keep both strength of bullish pin bars and bearish pin bars; in this case, you may need to change the plot style to make both strength visible.
Trading Suggestions
Evaluate the strength of trend against pin bars. After all, a single reverse pin bar may be too weak to reverse the trend.
Timeframe : if atr is higher than 4*spread, the timeframe is high enough. However, if strong pin bars appear too frequent or price range is too small, going to higher fimeframe may be more safe.
Entry and exit : according to personal flavors.
Conclusion
The strength of pin bars can be quantized.
With this indicator, we can find more potential pin bars which human eyes and binary pattern detectors were leaked.
In my opinion, 0.5*atr is the most suitable streng of a pin bar for my trade entry but I still need to consider the direction of the trend.
You are welcome to share your settings and related trading strategy.
References
Most of related knowledge can be searched from the internet.
I cannot say the exact references because they may violate the rules of Tradingview.
Dynamic Dots Dashboard (a Cloud/ZLEMA Composite)The purpose of this indicator is to provide an easy-to-read binary dashboard of where the current price is relative to key dynamic supports and resistances. The concept is simple, if a dynamic s/r is currently acting as a resistance, the indicator plots a dot above the histogram in the red box. If a dynamic s/r is acting as support, a dot is plotted in the green box below.
There are some additional features, but the dot graphs are king.
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KEY:
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Currently the dynamic s/r's being used in the dot plots are:
Ichimoku Cloud:
Tenkan (blue)
Kijun (pink)
Senkou A (red)
Senkou B (green)
ZLEMA (Zero Lag Exponential Moving Average)
99 ZLEMA (lavender)
200 ZLEMA (salmon)
You'll see a dashed line through the middle of the resistances section (red) and supports section (green). Cloud indicators are plotted above the dashed line, and ZLEMA's are below.
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How it Works - Visual
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As stated in the intro - if a dynamic s/r is currently above the current price and acting as a resistance, the indicator plots a dot above the histogram in the red box. If a dynamic s/r is acting as support, a dot is plotted in the green box below. Additionally, there is an optional histogram (default is on) that will further visualize this relationship. The histogram is a simple summation of the resistances above and the supports below.
Here's a visual to assist with what that means. This chart includes all of those dynamic s/r's in the dynamic dot dashboard (the on-chart parts are individually added, not part of this tool).
You can see that as a dynamic support is lost, the corresponding dot is moved from the supports section at the bottom (green), to the resistances section at the top (red). The opposite being true as resistances are being overtaken (broken resistances are moved to the support section (red)). You can see that the raw chart is just... a mess. Which kinda of accentuates one of the key goals of this indicator: to get all that dynamic support info without a mess of a chart like that.
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How To Use It
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There are a lot of ways to use this information, but the most notable of which is to detect shifts in the market cycle.
For this example, take a look at the dynamic s/r dots in the resistances category (red background). You can see clearly that there are distinctive blocks of high density dots that have clear beginnings and ends. When we transition from a high density of dots to none in resistances, that means we are flipping them as support and entering a bull cycle. On the other hand, when we go from low density of dots as resistances to high density, we're pivoting to a bear cycle. Easy as that, you can quickly detect when market cycles are beginning or ending.
Alternatively, you can add your preferred linear SR's, fibs, etc. to the chart and quickly glance at the dashboard to gauge how dynamic SR's may be contributing to the risk of your trade.
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Who It's For
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New traders: by looking at dot density alone, you can use Dot Dynamics to spot transitionary phases in market cycles.
Experienced traders: keep your charts clean and the information easy to digest.
Developers: I created this originally as a starting point for more complex algos I'm working on. One algo is reading this dot dashboard and taking a position size relative to the s/r's above and below. Another cloud algo is using the results as inputs to spot good setups.
Colored Bars
There is an option (off by default, shown in the headline image above) to fill the bar colors based on how many dynamic s/r's are above or below the current price. This can make things easier for some users, confusing for others. I defaulted them to off as I don't want colors to confuse the primary value proposition of the indicators, which is the dot heat map. You can turn on colored bars in the settings.
One thing to note with the colored bars: they plot the color purely by the dot densities. Random spikes in the gradient colors (i.e. red to lime or green) can be a useful thing to notice, as they commonly occur at places where the price is bouncing between dynamic s/r's and can indicate a paradigm shift in the market cycle.
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Timeframes and Assets
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This can be used effectively on all assets (stocks, crypto, forex, etc) and all time frames. As always with any indicator, the higher TF's are generally respected more than lower TF's.
Thanks for checking it out! I've been trading crypto for years and am just now beginning to publish my ideas, secret-sauce scripts and handy tools (like this one). If you enjoyed this indicator and would like to see more, a like and a follow is greatly appreciated 😁.
