stratRsi_MiguelThis strategy was created with the aim of trying to predict upper moves and to protect from falling prices. It is intended to out perform going from bottom to top and to bottom again (ex: BTC from 10k to 20k to 10k again and remain with substancial profit from the rise). It will not outperform from bottom to top. It is advised to do not enter short positions with this strategy. Only long and close.
Adjust "Period in minutes" to your time frame. ex: 60 for 1h, 240 for 4h, 1440 for 1D.
This will adjust the lengh of ema (RSI).
Default value is 1440 for 1D time frame.
Long condition is triggered when a FastEma(RSI) is bigger then a MeddiumEma(RSI), the MeddiumRSI is bigger than the SlowerEma(RSI), the FastRSI is larger then the Highest(FastRSI) with a length equal to input "High Length"(default value=6) and ema(close) is rising.
The short Condition is triggered when rsi(close,14) is lower then lowest(FastRSI) with a lenght equal to input "Low Length" (default value=3) and ema(close) is falling.
You can set up the time range to evaluate the strategy from "Start date" (default: year=2018, month=1, day=1) to "End date" (default: year=2050, month=1, day=1). Default commission=0.075%. Default Initial capital = 100 USD
Tìm kiếm tập lệnh với "rsi"
Jigga - Nifty Bank JuniorHello Investors !!
We have seen many indicators for trading, But I was looking for indicator which can be use for long term investment, which gives less trade and yet result in profit.
1) We know trend is our friend. And best way to be in trend is to follow trend indicator. I have picked RSI to find out trend.
2) It’s difficult to handle volatility of stocks and hence I choose to make indicator which works only on Index. Especially Nifty, BankNifty and Nifty Junior.
Logic is simple
Bullish when RSI level > 55
Bearish when RSI level < 45
In this indicator you can change RSI look back period as per your convenience. Also, you can check back testing output by selecting “Back Testing” option.
General guideline to use this study
1) Use only on Nifty, Bank Nifty, Junior Nifty
2) ‘Daily’ period gives good result.
Please note this study may not be useful for Trading purpose. Kindly request to check RSI related others study on Trading View.
Happy Investing!!
Buy The Dips in Bull Market (by Coinrule)During a Bull market, beating the market, it's challenging. Trading strategies that buy the dips represent one of the best approaches to surf the trend and optimize the returns.
The main obstacle is to gauge the dip's magnitude properly and set up the take profit level accordingly. The RSI is an excellent tool to catch price drops as it adjusts the entry to the asset's current volatility. Nevertheless, using the RSI as an indicator for exit is not an optimal solution in trending markets as it may end up with two scenarios:
The price reverts before reaching overbought conditions. That is the case when the trend is not that strong at that moment. Leaving the position open could result in missed profit opportunities.
The price rebounds strongly, leading the RSI quickly in overbought conditions too soon so that the strategy sells too early.
One interesting option is to combine a trigger based on the RSI to catch the dip and then use two moving averages to spot the right time to seel when the price is entirely back on-trend.
The Setup
The entry-signal comes when the RSI is lower than 35 and the MA9 is above the MA200, indicating that the asset is currently in an uptrend.
The sell-signal comes when at the same time, the price is above the MA9, and the MA9 is above the MA50.
This setup was optimized on the 15-min time frame after over 150 backtests.
A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange.
888 BOT #backtest█ 888 BOT #backtest (open source)
This is an Expert Advisor 'EA' or Automated trading script for ‘longs’ and ‘shorts’, which uses only a Take Profit or, in the worst case, a Stop Loss to close the trade.
It's a much improved version of the previous ‘Repanocha’. It doesn`t use 'Trailing Stop' or 'security()' functions (although using a security function doesn`t mean that the script repaints) and all signals are confirmed, therefore the script doesn`t repaint in alert mode and is accurate in backtest mode.
Apart from the previous indicators, some more and other functions have been added for Stop-Loss, re-entry and leverage.
It uses 8 indicators, (many of you already know what they are, but in case there is someone new), these are the following:
1. Jurik Moving Average
It's a moving average created by Mark Jurik for professionals which eliminates the 'lag' or delay of the signal. It's better than other moving averages like EMA , DEMA , AMA or T3.
There are two ways to decrease noise using JMA . Increasing the 'LENGTH' parameter will cause JMA to move more slowly and therefore reduce noise at the expense of adding 'lag'
The 'JMA LENGTH', 'PHASE' and 'POWER' parameters offer a way to select the optimal balance between 'lag' and over boost.
Green: Bullish , Red: Bearish .
2. Range filter
Created by Donovan Wall, its function is to filter or eliminate noise and to better determine the price trend in the short term.
First, a uniform average price range 'SAMPLING PERIOD' is calculated for the filter base and multiplied by a specific quantity 'RANGE MULTIPLIER'.
The filter is then calculated by adjusting price movements that do not exceed the specified range.
Finally, the target ranges are plotted to show the prices that will trigger the filter movement.
Green: Bullish , Red: Bearish .
3. Average Directional Index ( ADX Classic) and ( ADX Masanakamura)
It's an indicator designed by Welles Wilder to measure the strength and direction of the market trend. The price movement is strong when the ADX has a positive slope and is above a certain minimum level 'ADX THRESHOLD' and for a given period 'ADX LENGTH'.
The green color of the bars indicates that the trend is bullish and that the ADX is above the level established by the threshold.
The red color of the bars indicates that the trend is down and that the ADX is above the threshold level.
The orange color of the bars indicates that the price is not strong and will surely lateralize.
You can choose between the classic option and the one created by a certain 'Masanakamura'. The main difference between the two is that in the first it uses RMA () and in the second SMA () in its calculation.
4. Parabolic SAR
This indicator, also created by Welles Wilder, places points that help define a trend. The Parabolic SAR can follow the price above or below, the peculiarity that it offers is that when the price touches the indicator, it jumps to the other side of the price (if the Parabolic SAR was below the price it jumps up and vice versa) to a distance predetermined by the indicator. At this time the indicator continues to follow the price, reducing the distance with each candle until it is finally touched again by the price and the process starts again. This procedure explains the name of the indicator: the Parabolic SAR follows the price generating a characteristic parabolic shape, when the price touches it, stops and turns ( SAR is the acronym for 'stop and reverse'), giving rise to a new cycle. When the points are below the price, the trend is up, while the points above the price indicate a downward trend.
5. RSI with Volume
This indicator was created by LazyBear from the popular RSI .
The RSI is an oscillator-type indicator used in technical analysis and also created by Welles Wilder that shows the strength of the price by comparing individual movements up or down in successive closing prices.
LazyBear added a volume parameter that makes it more accurate to the market movement.
A good way to use RSI is by considering the 50 'RSI CENTER LINE' centerline. When the oscillator is above, the trend is bullish and when it is below, the trend is bearish .
6. Moving Average Convergence Divergence ( MACD ) and ( MAC-Z )
It was created by Gerald Appel. Subsequently, the histogram was added to anticipate the crossing of MA. Broadly speaking, we can say that the MACD is an oscillator consisting of two moving averages that rotate around the zero line. The MACD line is the difference between a short moving average 'MACD FAST MA LENGTH' and a long moving average 'MACD SLOW MA LENGTH'. It's an indicator that allows us to have a reference on the trend of the asset on which it is operating, thus generating market entry and exit signals.
We can talk about a bull market when the MACD histogram is above the zero line, along with the signal line, while we are talking about a bear market when the MACD histogram is below the zero line.
There is the option of using the MAC-Z indicator created by LazyBear, which according to its author is more effective, by using the parameter VWAP ( volume weighted average price ) 'Z-VWAP LENGTH' together with a standard deviation 'STDEV LENGTH' in its calculation.
7. Volume Condition
Volume indicates the number of participants in this war between bulls and bears, the more volume the more likely the price will move in favor of the trend. A low trading volume indicates a lower number of participants and interest in the instrument in question. Low volumes may reveal weakness behind a price movement.
With this condition, those signals whose volume is less than the volume SMA for a period 'SMA VOLUME LENGTH' multiplied by a factor 'VOLUME FACTOR' are filtered. In addition, it determines the leverage used, the more volume , the more participants, the more probability that the price will move in our favor, that is, we can use more leverage. The leverage in this script is determined by how many times the volume is above the SMA line.
The maximum leverage is 8.
8. Bollinger Bands
This indicator was created by John Bollinger and consists of three bands that are drawn superimposed on the price evolution graph.
The central band is a moving average, normally a simple moving average calculated with 20 periods is used. ('BB LENGTH' Number of periods of the moving average)
The upper band is calculated by adding the value of the simple moving average X times the standard deviation of the moving average. ('BB MULTIPLIER' Number of times the standard deviation of the moving average)
The lower band is calculated by subtracting the simple moving average X times the standard deviation of the moving average.
the band between the upper and lower bands contains, statistically, almost 90% of the possible price variations, which means that any movement of the price outside the bands has special relevance.
In practical terms, Bollinger bands behave as if they were an elastic band so that, if the price touches them, it has a high probability of bouncing.
Sometimes, after the entry order is filled, the price is returned to the opposite side. If price touch the Bollinger band in the same previous conditions, another order is filled in the same direction of the position to improve the average entry price, (% MINIMUM BETTER PRICE ': Minimum price for the re-entry to be executed and that is better than the price of the previous position in a given %) in this way we give the trade a chance that the Take Profit is executed before. The downside is that the position is doubled in size. 'ACTIVATE DIVIDE TP': Divide the size of the TP in half. More probability of the trade closing but less profit.
█ STOP LOSS and RISK MANAGEMENT.
A good risk management is what can make your equity go up or be liquidated.
The % risk is the percentage of our capital that we are willing to lose by operation. This is recommended to be between 1-5%.
% Risk: (% Stop Loss x % Equity per trade x Leverage) / 100
First the strategy is calculated with Stop Loss, then the risk per operation is determined and from there, the amount per operation is calculated and not vice versa.
In this script you can use a normal Stop Loss or one according to the ATR. Also activate the option to trigger it earlier if the risk percentage is reached. '% RISK ALLOWED'
'STOP LOSS CONFIRMED': The Stop Loss is only activated if the closing of the previous bar is in the loss limit condition. It's useful to prevent the SL from triggering when they do a ‘pump’ to sweep Stops and then return the price to the previous state.
█ BACKTEST
The objective of the Backtest is to evaluate the effectiveness of our strategy. A good Backtest is determined by some parameters such as:
- RECOVERY FACTOR: It consists of dividing the 'net profit' by the 'drawdown’. An excellent trading system has a recovery factor of 10 or more; that is, it generates 10 times more net profit than drawdown.
- PROFIT FACTOR: The ‘Profit Factor’ is another popular measure of system performance. It's as simple as dividing what win trades earn by what loser trades lose. If the strategy is profitable then by definition the 'Profit Factor' is going to be greater than 1. Strategies that are not profitable produce profit factors less than one. A good system has a profit factor of 2 or more. The good thing about the ‘Profit Factor’ is that it tells us what we are going to earn for each dollar we lose. A profit factor of 2.5 tells us that for every dollar we lose operating we will earn 2.5.
- SHARPE: (Return system - Return without risk) / Deviation of returns.
When the variations of gains and losses are very high, the deviation is very high and that leads to a very poor ‘Sharpe’ ratio. If the operations are very close to the average (little deviation) the result is a fairly high 'Sharpe' ratio. If a strategy has a 'Sharpe' ratio greater than 1 it is a good strategy. If it has a 'Sharpe' ratio greater than 2, it is excellent. If it has a ‘Sharpe’ ratio less than 1 then we don't know if it is good or bad, we have to look at other parameters.
- MATHEMATICAL EXPECTATION: (% winning trades X average profit) + (% losing trades X average loss).
To earn money with a Trading system, it is not necessary to win all the operations, what is really important is the final result of the operation. A Trading system has to have positive mathematical expectation as is the case with this script: ME = (0.87 x 30.74$) - (0.13 x 56.16$) = (26.74 - 7.30) = 19.44$ > 0
The game of roulette, for example, has negative mathematical expectation for the player, it can have positive winning streaks, but in the long term, if you continue playing you will end up losing, and casinos know this very well.
PARAMETERS
'BACKTEST DAYS': Number of days back of historical data for the calculation of the Backtest.
'ENTRY TYPE': For '% EQUITY' if you have $ 10,000 of capital and select 7.5%, for example, your entry would be $ 750 without leverage. If you select CONTRACTS for the 'BTCUSDT' pair, for example, it would be the amount in 'Bitcoins' and if you select 'CASH' it would be the amount in $ dollars.
'QUANTITY (LEVERAGE 1X)': The amount for an entry with X1 leverage according to the previous section.
'MAXIMUM LEVERAGE': It's the maximum allowed multiplier of the quantity entered in the previous section according to the volume condition.
The settings are for Bitcoin at Binance Futures (BTC: USDTPERP) in 15 minutes.
For other pairs and other timeframes, the settings have to be adjusted again. And within a month, the settings will be different because we all know the market and the trend are changing.
HFT Fibonacci Bands BacktesterDefault Settings are meant to be used in XBT/USD chart on 15 min time frame. If you want to use for another asset on another time frame YOU MUST CHANGE THE SETTINGS
This is a Fibonacci bands based trading strategy developed by HFT Research. It is a highly customizable strategy and provides endless opportunities to find profitable trades in the market.
Use Fib BB
This is the main decision maker of the strategy. Tuning the settings of this portion of the strategy will change the outcome the most. We have provided default settings. However, they are only good for 15min chart on Bitcoin . Please adjust accordingly.
Fib BB Length: This setting adjusts the middle line of your Fibonacci Bands. It is the moving average that you take it as base for your Fibonacci bands. Default value is currently 20.
Fib Level to Use for Entry: Here, you adjust which one of the Fibonacci Ratio levels you would like to use for your entry. You can only choose one of the following options.
Fibonacci Ratio 1
This is your Fib ratio level 1 and you can put any number here you would like
Fibonacci Ratio 2
This is your Fib ratio level 2 and you can put any number here you would like
Fibonacci Ratio 3
This is your Fib ratio level 3 and you can put any number here you would like
Please keep in mind that Ratio 1 should be higher than Ratio 2 and Ratio 2 should be higher than Ratio 3.
Use RSI
You can also turn on and off the RSI as well. Alternatively, there is an option to use RSI on a different time frame than you are currently on. For example, if you are looking at the 5min chart to use Bollinger bands but you would like to look at the RSI value on the 15min chart. You can do so by selecting the custom RSI timeframe as well as adjusting the Oversold and Overbought value.
Use CCI
Commodity Channel Index is an indicator developed by Donald Lambert. It is a momentum-based oscillator used to help determine when an investment vehicle is reaching conditions of being overbought or oversold. It also used to asses price trend direction and strength. Default settings are usually the safest and the best fit.
Use VWAP
VWAP stands for volume weighted average price . It is an extremely useful indicator when trading intra-day. It does reset every trading session which is at 00:00 UTC . Instead of looking at x number of candles and providing an average price, it will take into consideration volume that’s traded at a certain price and weigh it accordingly.
Use ADX
ADX stands for average directional index . It is an indicator that measures volatility in the market. Unfortunately, the worst market condition for this strategy is sideways market. ADX becomes a useful tool since it can detect trend. If the volatility is low and there is no real price movement, ADX will pick that up and will not let you get in trades during a sideways market. It will allow you to enter trades only when the market is trending.
Use MA Filters
Lookback: It is an option to look back x number of candles to validate the price crossing. If the market is choppy and the price keeps crossing up and down the moving average you have chosen, it will generate a lot of “noisy” signals. This option allows you to confirm the cross by selecting how many candles the price needs to stay above or below the moving average. Setting it 0 will turn it off.
MA Filter Type: There is a selection of moving averages that is available on TradingView currently. You can choose from 14 different moving average types to detect the trend as accurate as possible.
Filter Length: You can select the length of your moving average. Most commonly used length being 50,100 and 200.
Filter Type: This is our propriety smoothing method in order to make the moving averages lag less and influence the way they are calculated slightly. Type 1 being the normal calculation and type 2 being the secret sauce
Reverse MA Filter: This option allows you to use the moving average in reverse. For example, the strategy will go long when the price is above the moving average. However, if you use the reserve MA Filter, you will go short when the price is above the moving average. This method works best in sideways market where price usually retraces back to the moving average. So, in an anticipation of price reverting back to the moving average, it is a useful piece of option to use during sideway markets.
The backtester assumes the following;
- 1000$ capital
- 0.06% commission based on binance
- 1% risk meaning 100% equity on cross leverage
- Backtest results are starting from 2020
If you want to get access to this indicator please DM me or visit our website.
Quickie (Free) BacktesterQuickie is a free tradingview Indicator developed by HFT Research. It works in sideways and trending markets depending the way you set it as well as both on short time frame and long time frame. It comes with backtesting abilities on tradingview.
BITMEX:XBTUSD
Use Bollinger Bands
This piece of the settings will turn and off Bollinger band’s input in the decision making. BB Length will determine the Moving average you are using to take the standard deviation off of which is named as BB Multiplier. Default settings will use 20 moving average and take standard deviation of 2 to create lower and upper bands. Increasing the Multiplier will give you fewer but safer entries
Use RSI
You can also turn on and off the RSI as well. Alternatively, there is an option to use RSI on a different time frame than you are currently on. For example, if you are looking at the 5min chart to use Bollinger bands but you would like to look at the RSI value on the 15min chart. You can do so by selecting the custom RSI timeframe as well as adjusting the Oversold and Overbought value.
Use MA Filter
Lookback: The indicator has an option to look back x number of candles to validate the price crossing. If the market is choppy and the price keeps crossing up and down the moving average you have chosen, it will generate a lot of “noisy” signals. This option allows you to confirm the cross by selecting how many candles the price needs to stay above or below the moving average. Setting it 0 will turn it off.
MA Filter Type: There is a selection of moving averages that is available on TradingView currently. You can choose from 14 different moving average types to detect the trend as accurate as possible.
Filter Length: You can select the length of your moving average. Most commonly used length being 50,100 and 200.
Filter Type: This is our propriety smoothing method in order to make the moving averages lag less and influence the way they are calculated slightly. Type 1 being the normal calculation and type 2 being the secret sauce.
Reverse MA Filter: This option allows you to use the moving average in reverse. For example, the strategy will go long when the price is above the moving average. However, if you use the reserve MA Filter, you will go short when the price is above the moving average. This method works best in sideways market where price usually retraces back to the moving average. So, in an anticipation of price reverting back to the moving average, it is a useful piece of option to use during sideway markets.
For more information please check out our website
inwCoin Bullish/Bearish Divergence - Risk% StrategyEnglish
=========
inwCoin RSI Bullish/ Bearish Divergence Startegy.
RSI Bullish and Bearish divergence is a popular strategy that most people use to find the "reversal pattern" and bet on it.
...But is it really profitable in long run?
To find the answer, I write this strategy to test this hypothesis and the result is interesting.
------
How it work?
------
As you know, the main logic of bullish / bearish divergence are..
Buy Signal : RSI higher low in Oversold zone and price lower low
Sell Signal : RSI lower high in Overbought zone and price lower high
I also add some parameters to my strategy
1) Use stop loss + specific stop loss level
2) lookback period = RSI / Price lookback period to find divergence
----------
The result
----------
Not working at all.
It working ok in some period of time like in sideway market
But when uptrend established, it can't make any profit ( well, it's mean reversion strategy after all haha )
Also, when market keep crashing like in Nov 2018.
This strategy got stop out so many times before you can make 1 profitable trade....
But that trade won't last long because you have to take profit when you got bearish divergence signal.
----------
Conclusion
----------
Combine with trend following strategy.
This strategy might be able to fill the gap of sideway market.
But don't depend solely on this strategy because in long run, it can't beat the market.
GoombX backtest publicGoombX is an MA and stoch RSI based indicator which looks for particular crosses to identify strong trends.
It produces clear signals for:
- LONG ENTRY when it detects a significant MA cross and the right stoch RSI conditions
- LONG EXIT when certain stoch RSI conditions are met
- LONG STOP when price moves x% below entry (default 10%)
- SHORT ENTRY when it detects a significant MA cross and the right stoch RSI conditions
- SHORT EXIT when certain stoch RSI conditions are met
- SHORT STOP when price moves x% above entry (default 10%)
It is best fitted for 1D charts
NOTES
This is the Strategy version of GoombX for backtesting purpose only (stops in October 2019)
I strongly recommend backtesting with fees if you plan on using GoombX for automated trading
A signal is only definitive once the trigger candle has closed
To learn how to backtest, please look here:
backtest-rookies.com
and here:
backtest-rookies.com
EMA Mega Cross StrategyBased on Anvamsi's script which uses 12/26 EMA crosses for entry/exit signals. I also add the following features:
* Optimized default parameters for ETH 4hr chart
* Use EMA 55/200 relationship to filter out signals
* Use RSI vs EMA of RSI to filter out signals
* Use 26/55 EMA relationship to filter out signals
* Use volume climax technique as an additional exit strategy
* Uses bull/bear RSI divs as an additional exit strategy
* Adds bull RSI div quick flip plays when nothing else is going on for extra $$
This very experimental and my first major script. I've kept it invite only because the only people using this should have a direct line of communication open with me at this point.
NOTE #1:
You can get 2018 ETH trade profitability to reach 100% if you change line 97 from:
if (shortEMA and (rsi1 <= ema(rsi1,RSIEMALength)) and shorttrend and (ema(close,26) < ema(close,55)))
to:
if (shortEMA and (rsi1 <= ema(rsi1,RSIEMALength)) and shorttrend)
Basically, you remove an extra filter from the short strategy. It's novel to see profitability hit 100% but if you look at performance from 2017, it increases the max draw down by a lot!
NOTE #2:
I couldn't get RSI bear div quick flips to work so they are disabled. The remaining short strategy is in effect.
NOTE #3:
The profitability is good for long-only, if you check Strategy Tester->Performance Summary.
NOTE #4:
I am not an expert trader (mainly due to psychological factors i think) but i can program and have a good understanding of signal processing from working with analog synthesizers. Use this at your own risk. I am not liable if you lose all of your money!
NOTE #5:
Code is really messy. Old code commented out everywhere. :/
SB_CM_RSI_2_Strategy_Version 1.0The strategy is based on the indicator posted by @ChrisMoody "CM RSI-2 Strategy Lower Indicator" which is based on "Larry Connors RSI-2 Strategy - Lower RSI"
In this strategy the longs are placed when a green color is encountered in the rsi and short when red color is encountered in the rsi.
Although the profits can be booked at different interval.
Just message in the script if you have any different idea regarding this indicator.
For the original indicator you can refer to :
For Tips to continue :) :
BTC: 1BjswGcRR6c23pka7qh5t5k56j46cuyyy2
ETH: 0x64fed71c9d6c931639c7ba4671aeb6b05e6b3781
LTC: LKT2ykQ8QSzzfTDB6Tnsf12xwYPjgq95h4
Cowabunga System from babypips.comPlease do read the information below as well, especially if you are new to Forex.
The Cowabunga System is a type of Mechanical Trading System that filters trades based on the trend of the 4 hour chart with EMAs and some other familiar indicators (RSI, Stochastics and MACD) while entering trades base on 15 minute chart.
I have coded (quite amateurishly) the basic system onto a 15 minute chart (the 4 hour settings are coded as well). The author says the system is to be traded off the 15 minute chart with the 4 hour chart only as a reference for trend direction.
4 Hour Chart Settings
5 EMA
10 EMA
Stochastics (10,3,3)
RSI (9)
Then we move onto the 15 minute chart, where he gives us the trade entry rules.
15 Minute Chart Settings
5 EMA
10 EMA
Stochastics (10,3,3)
RSI (9)
MACD (12,26,9)
Entry Rules - long entry rules used, obviously reverse these for shorting.
1. EMA must cross above the 10 EMA.
2. RSI must be greater than 50 and not overbought.
3. Stochastic must be headed up and not be in overbought territory.
4. MACD histogram must go from negative to positive OR be negative and start to increase in value.
What I did.
1. Set the RSI and Stochastic levels to avoid entries when they indicate overbought conditions for long and oversold conditions for short (80 and 20 levels).
2. Users can input specific times they want to backtest.
3. User's can configure profit targets, trailing stops and stops. Default is set it to was 100 pips profit target with a 40 pip trailing stop. (Note, when you are changing these values, please note that each pip is worth 10, so 100 pips is entered as 1000.)
The Cowabunga System from babypips.com is another popular and active system. The author, Pip Surfer, continues to post wins and losses with this system. It shows there is a lot of honesty and integrity with this system if the author keeps up to date even 10 years later and is not afraid of sharing the times the system causes losses.
As an example of this, here is post he shared just last week . It's almost like a journal, he gives specific times and reasons why he entered, lets the readers know when he was stopped out, etc. I think that what he does is equally important as his system.
To read more about this system, visit the thread on babypips.com, click here.
OPTIMISED FOR 15Min on certain FOREX Ichimoku & Friends Strategy
Timeframe
15-Minute Chart
Entry Rules
Required Conditions ALL Must Be True
For LONG Entries:
Trend: Price is above EMA 200 (purple line)
Ichimoku: Tenkan (blue) is above Kijun (red)
Price Position: Close is above BOTH Tenkan AND Kijun
ADX: Must be above 22 (shows strong trend)
RSI: Between 50 and 70 (has momentum, not overbought)
Cooldown: At least 12 bars since last trade closed
For SHORT Entries:
Trend: Price is below EMA 200 (purple line)
Ichimoku: Tenkan (blue) is below Kijun (red)
Price Position: Close is below BOTH Tenkan AND Kijun
ADX: Must be above 22 (shows strong trend)
RSI: Between 30 and 50 (has momentum, not oversold)
Cooldown: At least 12 bars since last trade closed
Entry Signals Any ONE of These
Signal Type 1: Cross (C)
Long: Tenkan crosses above Kijun AND price closes above Kijun
Short: Tenkan crosses below Kijun AND price closes below Kijun
Wait 1 bar to confirm the cross holds
Signal Type 2: Bounce (B) - Most Reliable
Long: Price touches/dips to Kijun, then bounces up with strong bullish candle
Short: Price touches/spikes to Kijun, then rejects down with strong bearish candle
Must occur within last 3 bars
Signal Type 3: Breakout (K)
Long: Price breaks above Kijun with strong bullish momentum candle
Short: Price breaks below Kijun with strong bearish momentum candle
Candle body must be at least 40% of ATR
Risk Management
Stop Loss Placement
Placed at the lower of:
Recent swing low (last 5 bars) for longs
Kijun minus 0.5 ATR for longs
Minimum distance: 2.5 x ATR
FOR SHORTS: Mirror logic using swing highs
Take Profit
2x the stop loss distance
Example: If stop is 20 pips away, target is 40 pips
Position Size
100% of equity per trade (as per current settings)
Adjust based on your risk tolerance
Trade Management
When to Enter
Only when ALL entry conditions are met
Check that background is shaded (green for long, red for short)
Small letter markers (C, B, K) show which signal type triggered
When to Exit
Take Profit hit (2x R:R ratio)
Stop Loss hit (smart placement protects capital)
Strategy closes position (conditions reverse)
Cooldown Period
Wait 12 bars (3 hours on 15m chart) after any trade closes
Prevents revenge trading and overtrading
Visual Indicators on Chart
Lines
Blue (Tenkan): 9-period conversion line
Red (Kijun): 26-period base line
Purple (EMA 200): Long-term trend line
Orange (EMA 50): Not used in current rules
Signals
Large Green Triangle Up: LONG entry
Large Red Triangle Down: SHORT entry
Small Letters (C/B/K): Which signal type triggered
Background Colors
Light Green: Conditions favorable for LONG (ADX good, uptrend)
Light Red: Conditions favorable for SHORT (ADX good, downtrend)
No Color: Not safe to trade
Top Right Display
ADX Value: Green = above threshold, Red = below
Win Rate: Shows current performance
Quick Checklist Before Entry
LONG Trade Checklist:
Price above purple EMA 200
Blue line above red line
Price above both blue AND red lines
ADX number is green (above 22)
RSI between 50-70
Background is light green
At least 12 bars since last trade
Signal marker appeared (triangle or letter)
SHORT Trade Checklist:
Price below purple EMA 200
Blue line below red line
Price below both blue AND red lines
ADX number is green (above 22)
RSI between 30-50
Background is light red
At least 12 bars since last trade
Signal marker appeared (triangle or letter)
Tips for Success
Best Signal Type: Bounce (B) signals typically have highest win rate
ADX is Critical: Do not trade when ADX is red - wait for trends
Be Patient: 2-3 trades per day on 15m is normal and healthy
Trust the System: Do not second-guess the signals
Respect Cooldown: Waiting prevents emotional trading
Monitor Win Rate: Keep above 50% for profitability with 2:1 R:R
Adjustable Settings
If you want to modify strategy performance:
For Higher Win Rate Fewer Trades:
Increase "Minimum ADX" to 25
Increase "Cooldown Bars" to 15
Turn OFF breakout signals
For More Trades Slightly Lower Win Rate:
Decrease "Minimum ADX" to 20
Decrease "Cooldown Bars" to 8
Keep all signal types enabled
For Better Risk:Reward:
Increase "Risk:Reward Ratio" to 2.5 or 3.0
This means bigger targets, letting winners run more
What NOT to Do
Do not trade without ADX confirmation (when number is red)
Do not enter during cooldown period
Do not trade when price is chopping around EMA 200
Do not override the stop loss - let it work
Do not take signals when Tenkan and Kijun are flat/parallel
Do not force trades - wait for all conditions
Do not trade if you see no background shading
Notes
Current Performance: 67% win rate (2/3 trades)
Timeframe: 15-minute (3 hours = 12 bars cooldown)
Profit Factor Target: Above 1.5 is excellent
Strategy works best during: European and US trading sessions when volatility is higher
DYOR NFA
Kuytrade - Super Scalping Pro V1.0📘 Strategy "Kuytrade - Super Scalping Pro"
What is this strategy?
This is a scalping trading that helps you catch quick profits from short-term price movements. It's perfect for traders who want to make multiple small wins throughout the day.
How does it work?
The strategy uses a 3-level filter system to find high-quality trading signals:
Level 1: CORE Indicators (Must Pass)
- EMA (Moving Averages): Checks if the trend is going up or down
- MACD: Confirms momentum is building in the right direction
Level 2: MOMENTUM Indicators
- RSI: Looks for oversold (ready to bounce up) or overbought (ready to drop) conditions
- Stochastic: Finds reversal points where price might change direction
Level 3: BOOST Indicators
- RSI Divergence: Spots hidden opportunities when price and momentum disagree
- Strong Candles: Identifies powerful price movements
- ATR Filter: Makes sure the market is active enough to trade
Trading Setup
Each Signal Opens 3 Orders:
Order 1: Closes at TP1 (quick small profit)
Order 2: Closes at TP2 (medium profit)
Order 3: Closes at TP3 (big profit target)
Default Settings:
TP1: 1,000 points
TP2: 1,500 points
TP3: 2,500 points
Stop Loss: 1,200 points
Lot Size: 0.01 per order (3 orders total)
Smart Features
- Trailing Stop Loss
- When TP1 hits, the Stop Loss for TP3 automatically moves to breakeven + 150 points, protecting your profit!
- Auto Asset Detection
The strategy automatically recognizes what you're trading:
- Forex pairs (EURUSD, GBPUSD, etc.)
- Gold, Silver, Platinum
- Crypto (Bitcoin, Ethereum)
- Stock Indices (US30, NASDAQ, S&P500)
Indicators:
You can enable/disable each indicator level
Mix and match to find what works for your style
Visuals:
Show/Hide TP/SL lines
Show/Hide entry boxes
Mobile view for smaller screens
When to Use This Strategy?
✅ Best for:
Active markets (London/NY sessions)
Lower timeframes (1m, 5m, 15m)
Volatile pairs with clear trends
❌ Avoid during:
Major news releases
Very quiet markets
Weekends/holidays
----------------------------------------
กลยุทธ์นี้คืออะไร?
Scalping ที่ออกแบบมาให้ช่วยทำกำไรเล็กๆ จากการเคลื่อนไหวของราคาระยะสั้น เหมาะสำหรับเทรดเดอร์ที่ต้องการทำกำไรเล็กน้อยบ่อยๆ ตลอดทั้งวัน
ทำงานยังไง?
กลยุทธ์ใช้ระบบกรองสัญญาณ 3 ระดับ เพื่อหาจุดเข้าที่มีคุณภาพสูง
Level 1: ตัวบ่งชี้หลัก (ต้องผ่าน)
- EMA (เส้นค่าเฉลี่ย): เช็คว่าเทรนด์กำลังขึ้นหรือลง
- MACD: ยืนยันว่าแรงซื้อ/ขายกำลังมาถูกทาง
Level 2: ตัวบ่งชี้โมเมนตัม
- RSI: หาจุด Oversold (ราคาถูกเกินไป พร้อมกลับตัว) หรือ Overbought (ราคาแพงเกิน พร้อมลง)
- Stochastic: หาจุดกลับตัวที่ราคาอาจจะเปลี่ยนทิศ
Level 3: ตัวบ่งชี้เสริม
- RSI Divergence: เจอโอกาสแอบแฝงเมื่อราคาและโมเมนตัมไม่สอดคล้องกัน
- Strong Candles: จับแท่งเทียนที่แรงมาก
- ATR Filter: ตรวจว่าตลาดมีความผันผวนพอจะเทรดไหม
การตั้งค่าการเทรด
แต่ละสัญญาณเปิด 3 ออเดอร์:
ออเดอร์ 1: ปิดที่ TP1 (กำไรเล็กเร็ว)
ออเดอร์ 2: ปิดที่ TP2 (กำไรกลางๆ)
ออเดอร์ 3: ปิดที่ TP3 (กำไรใหญ่)
ค่าเริ่มต้น:
TP1: 800 จุด
TP2: 1,500 จุด
TP3: 2,500 จุด
Stop Loss: 1,200 จุด
ขนาดล็อต: 0.01 ต่อออเดอร์ (รวม 3 ออเดอร์)
ฟีเจอร์พิเศษ
- Trailing Stop Loss (ขยับ SL ตาม)
- เมื่อ TP1 โดน SL ของ TP3 จะเลื่อนมาที่ราคาเข้า + 150 จุด ทำให้คุณไม่ขาดทุน!
- ตรวจจับสินทรัพย์อัตโนมัติ
กลยุทธ์จะจำคู่เงินที่คุณเทรดได้เอง:
คู่เงิน Forex (EURUSD, GBPUSD ฯลฯ)
- ทองคำ, เงิน, แพลตตินั่ม
- คริปโต (Bitcoin, Ethereum)
- ดัชนีหุ้น (US30, NASDAQ, S&P500)
แดชบอร์ดผลงาน (ล่างซ้าย)
- แสดง Win Rate แต่ละ TP
- ติดตามกำไร/ขาดทุนรวม
- แสดงสถิติทั้งหมด
แดชบอร์ดสถานะ Level (บนขวา)
สถานะตัวบ่งชี้แบบเรียลไทม์
เขียว = สัญญาณพร้อม
แดง = รอเงื่อนไข
ตั้งค่าที่ปรับได้
คุณภาพสัญญาณ:
เปิด "Strict Filter" = สัญญาณน้อยแต่คุณภาพสูง
ปิด "Strict Filter" = สัญญาณเยอะแต่อาจเสี่ยงขึ้น
ตัวบ่งชี้:
- เปิด/ปิดแต่ละ Level ได้
- ผสมผสานหาสูตรที่เหมาะกับคุณ
การแสดงผล:
- แสดง/ซ่อนเส้น TP/SL
- แสดง/ซ่อนกล่องข้อมูล Entry
- โหมดมือถือสำหรับจอเล็ก
เมื่อไหร่ควรใช้กลยุทธ์นี้?
✅ เหมาะกับ:
- ตลาดที่คึกคัก (เซสชั่นลอนดอน/นิวยอร์ก)
- ไทม์เฟรมเล็ก (1m, 5m, 15m)
- คู่เงินที่มีความผันผวนและเทรนด์ชัด
❌ หลีกเลี่ยง:
- ช่วงมีข่าวเศรษฐกิจสำคัญ
- ตลาดเงียบมาก
- วันหยุดสุดสัปดาห์
Quantellics: NQ Reverse From EMA [Strategy]//@version=5
// © 2025 Quantellics. All rights reserved.
strategy("Quantellics: NQ Reverse From EMA ", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)
// Inputs
emaLen = input.int(60, "EMA Length", minval = 1)
rsiLen = input.int(14, "RSI Length", minval = 1)
lb = input.int(10, "Lookback Candles", minval = 1)
entryOff = input.float(75.0, "Entry Offset ($)", minval = 0, step = 1)
slDollar = input.float(50.0, "Stop Loss ($)", minval = 0, step = 1)
tpDollar = input.float(50.0, "Take Profit ($)", minval = 0, step = 1)
trailAct = input.float(30.0, "Trail Activation ($)", minval = 0, step = 1)
trailOff = input.float(30.0, "Trail Offset ($)", minval = 0, step = 1)
trailDelay = input.int(2, "Trail Delay (Candles)", minval = 0, step = 1)
ssH = input.int(9, "Session Start Hour (ET)", minval = 0, maxval = 23)
ssM = input.int(30, "Session Start Minute (ET)", minval = 0, maxval = 59)
seH = input.int(12, "Session End Hour (ET)", minval = 0, maxval = 23)
seM = input.int(0, "Session End Minute (ET)", minval = 0, maxval = 59)
// Session calc
int h = hour(time, "America/New_York")
int m = minute(time, "America/New_York")
sStart = ssH * 60 + ssM
sEnd = seH * 60 + seM
nowMin = h * 60 + m
inSess = nowMin >= sStart and nowMin < sEnd
eos = nowMin >= sEnd
// Indicators
ema60 = ta.ema(close, emaLen)
rsi = ta.rsi(close, rsiLen)
hiN = ta.highest(high, lb)
loN = ta.lowest(low, lb)
// Levels
longLvl = hiN - entryOff
shortLvl = loN + entryOff
// Conditions
longOk = high > ema60 and rsi > 50 and strategy.position_size == 0 and inSess and not eos
shortOk = low < ema60 and rsi < 50 and strategy.position_size == 0 and inSess and not eos
// State
var float ePrice = na
var float slLvl = na
var float tpLvl = na
var int bars = 0
if strategy.position_size != 0
bars += 1
else
bars := 0
// Orders
if longOk
strategy.entry("Long", strategy.long, limit = longLvl)
else
strategy.cancel("Long")
if shortOk
strategy.entry("Short", strategy.short, limit = shortLvl)
else
strategy.cancel("Short")
if strategy.position_size > 0
if bars > trailDelay
strategy.exit("Long Exit", "Long", stop = strategy.position_avg_price - slDollar, limit = strategy.position_avg_price + tpDollar, trail_points = trailAct, trail_offset = trailOff)
else
strategy.exit("Long Exit", "Long", stop = strategy.position_avg_price - slDollar, limit = strategy.position_avg_price + tpDollar)
if strategy.position_size < 0
if bars > trailDelay
strategy.exit("Short Exit", "Short", stop = strategy.position_avg_price + slDollar, limit = strategy.position_avg_price - tpDollar, trail_points = trailAct, trail_offset = trailOff)
else
strategy.exit("Short Exit", "Short", stop = strategy.position_avg_price + slDollar, limit = strategy.position_avg_price - tpDollar)
// EOS flat
if eos and strategy.position_size != 0
strategy.close_all(comment = "EOS Exit")
if eos
strategy.cancel_all()
// Tracking
if strategy.position_size > 0 and strategy.position_size <= 0
ePrice := strategy.position_avg_price
slLvl := ePrice - slDollar
tpLvl := ePrice + tpDollar
if strategy.position_size < 0 and strategy.position_size >= 0
ePrice := strategy.position_avg_price
slLvl := ePrice + slDollar
tpLvl := ePrice - tpDollar
// Plots
plot(ema60, color = color.blue, title = "EMA 60", linewidth = 2)
plot(hiN, color = color.new(color.green, 50), title = "Lookback High", linewidth = 1, style = plot.style_stepline)
plot(loN, color = color.new(color.red, 50), title = "Lookback Low", linewidth = 1, style = plot.style_stepline)
plot(longLvl, color = color.new(color.orange, 30), title = "Long Entry", linewidth = 2)
plot(shortLvl, color = color.new(color.purple, 30), title = "Short Entry", linewidth = 2)
Trend Signal MomentumOVERVIEW
Signal Trend Momentum is a hybrid strategy that combines multiple confirmations and filters to obtain better potential trading signals. Each confirmation and filter in Signal Trend Momentum aims to avoid possible false and trap signals.
HYBRID CONCEPTS
Smart Money Concept – This indicator forms market structure and Bullish & Bearish Order Block areas to make it easier to identify market trends and strong areas where price reversals often occur. Its purpose is to simplify recognizing market direction and serve as the first confirmation.
MSS + BOS (Market Structure Shift + Break of Structure) – This indicator serves as additional confirmation for the Smart Money Concept. With the presence of two types of market structure, the market trend direction becomes clearer and more convincing.
RSI Momentum Signal – This indicator becomes the third confirmation. When the Market Trend is clear and convincing, supported by the formation of Bearish and Bullish Order Blocks, the role of the Momentum Signal here becomes crucial as it provides trend momentum based on overbought and oversold areas.
Momentum Position – This indicator becomes the next confirmation based on buyer and seller VOLUME in the market. If buyer volume is higher, the momentum position will be depicted on the chart with an upward arrow, and conversely, if seller volume is higher, it will be depicted with a downward arrow.
SnR (Support and Resistance) – This final indicator is Support and Resistance, which will serve as the last and more convincing confirmation. Support and Resistance will strengthen the Order Block areas formed by the Smart Money Concept indicator. A Bullish Order Block + Support creates a higher possibility for an upward trend in the market, conversely, a Bearish Order Block + Resistance creates a higher possibility for a downward trend in the market.
The combination of these several indicators will provide a strong market direction + persistent buyer and seller areas, as well as depict momentum based on volume + RSI which serve as additional confirmations.
These additional confirmations will produce stronger signals and help avoid false and trap signals in the market.
HOW TO USE
A SHORT SIGNAL will be strong if there is a Downtrend Market Structure + Bearish Order Block + Resistance + Oversold RSI Momentum + Strong Seller Volume Momentum.
A LONG SIGNAL will be strong if there is an Uptrend Market Structure + Bullish Order Block + Support + Overbought RSI Momentum + Strong Buyer Volume Momentum.
CONCLUSION
Signal Trend Momentum is a combination of several powerful indicators designed to produce stronger, clearer, and easier-to-read signals.
This strategy is highly suitable for traders seeking more convincing trade signals based on multiple confirmations from the combined indicators, thereby creating a strong signal with a higher probability.
TrendSight📌 TrendSight — The All-in-One Multi-Timeframe Trend Engine
Key Features & Logic
Multi-Timeframe Trend Confirmation:
Entries are filtered by confirming bullish/bearish alignment across three distinct Supertrend timeframes (e.g., 5-min, 15-min, 45-min, etc.), combined with an EMA and volatility filter, to ensure high-conviction trades that's a powerful combination! Designing the entire strategy around the 15-minute timeframe (M15) and focusing on high-volatility coins maximizes the strategy's effectiveness .
Guaranteed Single-Entry per Signal:
The strategy uses a powerful manual flag and counter system to ensure trades fire only once when a new signal begins. It absolutely prevents immediate re-entry if the signal remains true, waiting instead for the entire trend condition to reset to false.
Dynamic Trailing Stop Loss:
The Stop Loss is set to a moving Supertrend line (current_supertrend), ensuring tight risk management that trails the price as the trade moves into profit.Guaranteed Take Profit (4% Run-up): Uses a precise Limit Order via strategy.exit() to capture profits instantly at a 4% run-up. This ensures accurate profit capture, even on sudden spikes (wicks).
Automated Risk Management:
Position size is dynamically calculated based on a fixed risk percentage (default 2% of equity) relative to the distance to the trailing stop.
🔥 Core Components
1. Adaptive Multi-Timeframe SuperTrend Dashboard
The backbone of mTrendSight is a fully customizable SuperTrend system, enhanced with a multi-timeframe confirmation table displaying ST direction & value.
This compact “Trend Dashboard” provides instant clarity on higher-timeframe direction, trend strength, and market bias.
2. Dynamic Support & Resistance Channels
Automatically detects the strongest support/resistance zones using pivot clustering.
Key Features:
Clustered S/R Channels instead of thin lines
Adaptive width based on recent swings
Breakout markers (optional) for continuation signals
Helps identify structural zones, retest areas, and liquidity pockets
3. Multi-Timeframe Color-Coded EMAs
Plot up to three EMAs, each optionally pulled from a higher timeframe.
Benefits:
Instant visual trend alignment
Bullish/Bearish dynamic color shifts
Precision EMA value table for trade planning
Works perfectly with ST & RSI for multi-layer confirmation
4. Linear Regression Trend Channel
A statistically driven trend channel that measures the most probable path of price action.
Highlights:
Uses Pearson’s R to determine trend reliability
Provides a Confidence Level to judge whether trend slope is credible
Ideal for determining over-extension and mean-reversion zones
5. ATR Volatility Analyzer
A lightweight but powerful volatility classifier using ATR.
Features:
Detects High, Low, or Normal volatility
Clean table display
Helps filter entries during low-energy markets
Strengthens trend-following filters when volatility expands
6. RSI Momentum & Trend Classifier
A significantly improved RSI with multi-layer smoothing and structure-based classification.
Provides:
Bullish / Bearish / Neutral momentum states
Short-term momentum vs long-term RSI trend
Perfect for early trend shifts, pullback entries, and momentum confirmation
⚙️ How the Strategy Works (Execution Logic)
📌 Multi-Timeframe Supertrend + EMA + Volatility Confirmation
Entries are only triggered when:
Multiple Supertrend timeframes align (e.g., 5m + 15m + 45m)
EMA direction aligns with the trend
Volatility conditions (ATR filter) is not Low allow high-probability moves
This ensures strong directional confluence before every trade.
📌 Guaranteed Single-Entry Logic
The strategy uses a flag + counter system to ensure:
Only one entry is allowed per trend signal
Re-entries do not happen until the entire trend condition resets
The Strategy Tester remains clean, without duplicate overlapping trades
This eliminates revenge trades, repeated fills, and choppy overtrading.
📌 Dynamic Supertrend Trailing Stop
Stop Loss is anchored to current Supertrend value, creating:
Automatic trailing
Tight downside control
Protection against deep pullbacks
High responsiveness during volatility expansions
📌 Precision Take-Profit (4% Run-Up Capture)
A dedicated global exit block ensures:
Take Profit triggers exactly at 4% price run-up
Uses strategy.exit() with limit orders to catch spikes (wicks)
Works consistently on all timeframes & assets
📌 Automated Position Sizing (2% Risk Default)
Position size is dynamically calculated based on:
Account Equity
Distance to trailing stop
Configured risk %
This enforces proper risk management without manual adjustments.
📈 How to Interpret Results
Reliable Exits: All exits are globally managed, so stops and take profits trigger accurately on every bar.
Clean Trade History: Because of single-entry logic, backtests show one trade per valid signal.
Consistency: Multi-timeframe logic ensures only high-quality, structured trades.
V15.0 Adaptive Chameleon [Pro]
# **V15.0 Adaptive Chameleon – Strategy Description**
**Adaptive Chameleon** is a fully automated TradingView strategy powered by a signal engine based on multi-timeframe trend analysis, adaptive moving averages, and a volatility filter. The goal is to trade in the direction of a strong and confirmed trend, avoid opening trades in weak or manipulative price zones, and establish positions with a clearly defined risk/reward ratio.
---
## **1. General Logic and Philosophy**
The strategy divides tasks between two timeframes:
* **4-Hour Chart → Trend Manager (Boss)**
Determines the direction and strength of the trend.
* **4-Minute Chart → Entry Trigger (Operating Unit)**
Generates the ideal entry signal in the direction of the trend.
Thanks to this structure, the strategy both follows the long-term main direction and finds clear entries with low lag on smaller timeframes.
---
## **2. Trend Detection (4H)**
The strategy uses **KAMA (Kaufman Adaptive Moving Average)** and **ADX** to identify trends on the higher timeframe.
### **KAMA – Adaptive Trend Line**
* The KAMA is much more "smart" than traditional moving averages.
* It accelerates during price movements and decelerates during sideways movements.
* This allows for much clearer detection of trend direction.
### **ADX – Trend Strength Meter**
The strategy only opens trades when **trend strength** is rising (above the ADX average).
This prevents unnecessary trades when the trend is weak.
### **Trend Rules**
* Price above the KAMA → **Uptrend**
* Price below the KAMA → **Downtrend**
* ADX widening → **Trend strong**
The entry trigger is activated when these three conditions are met together.
---
## **3. Entry Engine (45m)**
On the 45-minute timeframe, the system uses the following components:
### **AlphaTrend (MFI + ATR-Based Adaptive Line)**
* Measures market flow direction with MFI (Money Flow Index),
* Measures price level breakouts with ATR (Volatility).
AlphaTrend detects whether the price is likely to reverse upwards or downwards.
### **Entry Signal**
* **Buy signal:** If the AlphaTrend has reversed upwards based on recent bars
* **Sell signal:** If the AlphaTrend has broken downwards
### **Pivot Points (For Stop)**
* The **pivotLow** and **pivotHigh** levels of the last 10 bars are calculated.
* These are used to determine the most logical stop distance.
---
## **4. Protection Shields**
The strategy uses two main filters to protect against the most dangerous conditions in the crypto market:
### **1. Pump/Dump Filter**
* A candlestick length greater than 4% is considered a "pump bar."
* Never open a trade on these bars.
The goal: to avoid sudden manipulation candlesticks.
### **2. RSI Filter**
* Long trades: RSI > 45 (open long on weak momentum)
* Short trades: RSI < 55 (open short on extremely strong momentum)
These filters provide more balanced entries.
---
## **5. Final Entry Conditions**
### **All conditions are required simultaneously for long:**
1. 4H trend up
2. ADX trend strength increasing
3. 45m AlphaTrend issued a "buy" signal
4. RSI > 45
5. No candlestick pump
6. Date range is suitable
### **All conditions apply in the opposite direction for short.**
---
## **6. Exit Mechanism (Stop, TP, Trailing)**
The strategy uses a three-layer structure on the exit side:
### **1. Pivot-Based Stop**
* Stop distance = Entry price − Pivot Low (for long)
* Minimum stop distance = **1% of the price**
Provides both structural and mathematical security.
### **2. Fixed R:R (Default 1:2)**
* TP = Entry + Stop Distance × R:R
The default 2R target is ideal for trend systems.
### **3. Optional Trailing Stop**
* Dynamic trailing stop that follows the price by a certain percentage.
* Allows trend trades to yield greater profits.
---
## **7. Chart Displays**
* Purple line:** 4H WEDGE (main trend line)
* Yellow background:** Pump protection is active (trades will not be opened on that bar)
---
## **8. Practical Effect of the Strategy**
This system has an adaptive structure based on trend variations.
**Strengths:**
* Very high accuracy (76–80% in SOL and ETH tests)
* Low drawdown (approximately 6–7%)
* Safe entries thanks to pump/dump and extreme momentum filters
* Clearly defined stop and target structure
* Low noise thanks to multi-timeframe compatibility
**Weaknesses:**
* Performance may decrease in sideways markets without trends
* Overtrading may occur if the ADX filter is closed
* Very small stops can sometimes cause unnecessary triggers
---
## **9. Conclusion**
**Adaptive Chameleon** is a trend-based and highly stable strategy with well-established risk management, manipulation filtering, and entry into lower timeframes with clear trend direction detection and low-latency signals.
SOL and ETH demonstrated strong and balanced performance in backtests with metrics such as:
* **600+ trades**
* **30–37% profit**
* **76–80% win rate**
* **Low max drawdown**
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning
RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System
RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention .
Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve.
Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume.
The Problem This System Solves
Traditional Technical Analysis Limitations
Most trading systems suffer from three fundamental challenges :
Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes.
Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions.
Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution.
How RPD Machine Learning Addresses These Challenges
Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation.
Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly.
Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success.
Machine Learning Architecture: What Makes This "Real" ML
Component 1: Contextual Multi-Armed Bandits (LinUCB)
What Is a Multi-Armed Bandit Problem?
Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies.
Academic Foundation:
RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems.
How It Works:
Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains:
Reward History: Tracks wins/losses for each policy
Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume)
Uncertainty Estimates: Confidence in each policy's performance
UCB Formula: predicted_reward + α × uncertainty
The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies.
Why This Matters:
Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics.
Component 2: Random Fourier Features (RFF)
The Non-Linearity Challenge:
Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions.
Academic Foundation:
RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading.
How It Works:
The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations:
Input: 8 base features
Projection: Through random Gaussian weights
Transformation: cos(W×features + b)
Output: 16 RFF dimensions
This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space.
Why This Matters:
Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection.
Component 3: Reinforcement Learning Stack
Beyond bandits, RPD implements a complete RL framework :
Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades.
TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades.
Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction.
Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases.
Why This Matters:
Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm.
Component 4: Policy Momentum Tracking (v2 Feature)
The Recency Challenge:
Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance.
RPD's Solution:
Each policy maintains a ring buffer of the last 10 outcomes. The system calculates:
Momentum: recent_win_rate - global_win_rate (range: -1 to +1)
Confidence: consistency of recent results (1 - variance)
Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty.
Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits.
Signal Generation: The Core Algorithm
Multi-Timeframe Fractal Detection
RPD identifies reversal points using three complementary methods :
1. Quantum State Analysis:
Divides price range into discrete states (default: 6 levels)
Peak signals require price in top states (≥ state 5)
Valley signals require price in bottom states (≤ state 1)
Prevents mid-range signals that may struggle in strong trends
2. Fractal Geometry:
Identifies swing highs/lows using configurable fractal strength
Confirms local extremum with neighboring bars
Validates reversal only if price crosses prior extreme
3. Multi-Timeframe Confirmation:
Analyzes higher timeframe (4× default) for alignment
MTF confirmation adds probability bonus
Designed to reduce false signals while preserving valid setups
Probability Scoring System
Each signal receives a dynamic probability score (40-99%) based on:
Base Components:
Trend Strength: EMA(velocity) / ATR × 30 points
Entropy Quality: (1 - entropy) × 10 points
Starting baseline: 40 points
Enhancement Bonuses:
Divergence Detection: +20 points (price/momentum divergence)
RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys)
Volume Confirmation: +5 points (volume >1.2× average)
Adaptive Momentum: +10 points (strong directional velocity)
MTF Alignment: +12 points (higher timeframe confirms)
Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment)
Regime Bonus: +8 points (trending ADX >25 with directional agreement)
Penalties:
High Entropy: -5 points (entropy >0.85, chaotic price action)
Consolidation Regime: -10 points (ADX <20, no directional conviction)
Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%)
Entropy-Based Quality Filter
What Is Entropy?
Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions.
Calculation:
Count up/down price changes over adaptive period
Calculate probability: p = ups / total_changes
Shannon entropy: -p×log(p) - (1-p)×log(1-p)
Normalized to 0-1 range
Application:
Entropy <0.5: Highly ordered (ELITE signals possible)
Entropy 0.5-0.75: Mixed (GOOD signals)
Entropy >0.85: Chaotic (signals blocked or heavily penalized)
Why This Matters:
Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable.
Regime Detection & Market Microstructure - ADX-Based Regime Classification
RPD uses Wilder's Average Directional Index to classify markets:
Bull Trend: ADX >25, +DI > -DI (directional conviction bullish)
Bear Trend: ADX >25, +DI < -DI (directional conviction bearish)
Consolidation: ADX <20 (no directional conviction)
Transitional: ADX 20-25 (forming direction, ambiguous)
Filter Logic:
Blocks all signals during Transitional regime (avoids trading during uncertain conditions)
Blocks Consolidation signals unless ADX ≥ Min Trend Strength
Adds probability bonus during strong trends (ADX >30)
Effect: Designed to reduce signal frequency while focusing on higher-quality setups.
Divergence Detection
Bearish Divergence:
Price makes higher high
Velocity (price momentum) makes lower high
Indicates weakening upward pressure → SHORT signal quality boost
Bullish Divergence:
Price makes lower low
Velocity makes higher low
Indicates weakening downward pressure → LONG signal quality boost
Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing.
Hierarchical Policy System - The Six Trading Policies
1. AggressiveTrend (Policy 0):
Probability Threshold: 60% (trades more frequently)
Entropy Threshold: 0.70 (tolerates moderate chaos)
Stop Multiplier: 2.5× ATR (wider stops for trends)
Target Multiplier: 5.0R (larger targets)
Entry Mode: Pyramid (scales into winners)
Best For: Strong trending markets, breakouts, momentum continuation
2. ConservativeRange (Policy 1):
Probability Threshold: 75% (more selective)
Entropy Threshold: 0.60 (requires order)
Stop Multiplier: 1.8× ATR (tighter stops)
Target Multiplier: 3.0R (modest targets)
Entry Mode: Single (one-shot entries)
Best For: Range-bound markets, low volatility, mean reversion
3. VolatilityBreakout (Policy 2):
Probability Threshold: 65% (moderate)
Entropy Threshold: 0.80 (accepts high entropy)
Stop Multiplier: 3.0× ATR (wider stops)
Target Multiplier: 6.0R (larger targets)
Entry Mode: Tiered (splits entry)
Best For: Compression breakouts, post-consolidation moves, gap opens
4. EntropyScalp (Policy 3):
Probability Threshold: 80% (very selective)
Entropy Threshold: 0.40 (requires extreme order)
Stop Multiplier: 1.5× ATR (tightest stops)
Target Multiplier: 2.5R (quick targets)
Entry Mode: Single
Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns
5. DivergenceHunter (Policy 4):
Probability Threshold: 70% (quality-focused)
Entropy Threshold: 0.65 (balanced)
Stop Multiplier: 2.2× ATR (moderate stops)
Target Multiplier: 4.5R (balanced targets)
Entry Mode: Tiered
Best For: Divergence-confirmed reversals, exhaustion moves, trend climax
6. AdaptiveBlend (Policy 5):
Probability Threshold: 68% (balanced)
Entropy Threshold: 0.75 (balanced)
Stop Multiplier: 2.0× ATR (standard)
Target Multiplier: 4.0R (standard)
Entry Mode: Single
Best For: Mixed conditions, general trading, fallback when no clear regime
Policy Clustering (Advanced/Extreme Modes)
Policies are grouped into three clusters based on regime affinity:
Cluster 1 (Trending): AggressiveTrend, DivergenceHunter
High regime affinity (0.8): Performs well when ADX >25
Moderate vol affinity (0.6): Works in various volatility
Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend
Low regime affinity (0.3): Better suited for ADX <20
Low vol affinity (0.4): Optimized for calm markets
Cluster 3 (Breakout): VolatilityBreakout
Moderate regime affinity (0.6): Works in multiple regimes
High vol affinity (0.9): Requires high volatility for optimal characteristics
Hierarchical Selection Process:
Calculate cluster scores based on current regime and volatility
Select best-matching cluster
Run UCB selection within chosen cluster
Apply momentum boost/penalty
This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster.
Risk Management & Position Sizing
Dynamic Kelly Criterion Sizing (Optional)
Traditional Fixed Sizing Challenge:
Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions.
Kelly Formula:
f = (p × b - q) / b
Where:
p = win probability (from signal score)
q = loss probability (1 - p)
b = win/loss ratio (average_win / average_loss)
f = fraction of capital to risk
RPD Implementation:
Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits.
Enhancements:
Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier
Divergence Bonus: Additional sizing on divergence signals
Regime Bonus: Additional sizing during strong trends (ADX >30)
Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction
Safety Rails:
Minimum: 1 contract (floor)
Maximum: User-defined cap (default 10 contracts)
Portfolio Heat: Max total risk across all positions (default 4% equity)
Multi-Mode Stop Loss System
ATR Mode (Default):
Stop = entry ± (ATR × base_mult × policy_mult)
Consistent risk sizing
Ignores market structure
Best for: Futures, forex, algorithmic trading
Structural Mode:
Finds swing low (long) or high (short) over last 20 bars
Identifies fractal pivots within lookback
Places stop below/above structure + buffer (0.1× ATR)
Best for: Stocks, instruments that respect structure
Hybrid Mode (Intelligent):
Attempts structural stop first
Falls back to ATR if:
Structural level is invalid (beyond entry)
Structural stop >2× ATR away (too wide)
Best for: Mixed instruments, adaptability
Dynamic Adjustments:
Breakeven: Move stop to entry + 1 tick after 1.0R profit
Trailing: Trail stop 0.8R behind price after 1.5R profit
Timeout: Force close after 30 bars (optional)
Tiered Entry System
Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality.
Solution:
Tier 1 (40% of size): Enters immediately on all signals
Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%)
Example:
Calculated optimal size: 10 contracts
Signal probability: 72%
Tier 2 trigger: 75%
Result: Enter 4 contracts only (Tier 1)
Same signal at 80% probability
Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2)
Effect: Automatically scales size to signal quality, optimizing capital allocation.
Performance Optimization & Learning Curve
Warmup Phase (First 50 Trades)
Purpose: Ensure all policies get tested before system focuses on preferred strategies.
Modifications During Warmup:
Probability thresholds reduced 20% (65% becomes 52%)
Entropy thresholds increased 20% (more permissive)
Exploration rate stays high (30%)
Confidence width (α) doubled (more exploration)
Why This Matters:
Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies.
Curriculum Learning
Phase 1 (Trades 1-50): Exploration
Warmup active
All policies tested
High exploration (30%)
Learning fundamental patterns
Phase 2 (Trades 50-100): Refinement
Warmup ended, thresholds normalize
Exploration decaying (30% → 15%)
Policy preferences emerging
Meta-learning optimizing
Phase 3 (Trades 100-200): Specialization
Exploration low (15% → 8%)
Clear policy preferences established
Momentum tracking fully active
System focusing on learned patterns
Phase 4 (Trades 200+): Maturity
Exploration minimal (8% → 5%)
Regime-policy relationships learned
Auto-adaptation to market shifts
Stable performance expected
Convergence Indicators
System is learning well when:
Policy switch rate decreasing over time (initially ~50%, should drop to <20%)
Exploration rate decaying smoothly (30% → 5%)
One or two policies emerge with >50% selection frequency
Performance metrics stabilizing over time
Consistent behavior in similar market conditions
System may need adjustment when:
Policy switch rate >40% after 100 trades (excessive exploration)
Exploration rate not decaying (parameter issue)
All policies showing similar selection (not differentiating)
Performance declining despite relaxed thresholds (underlying signal issue)
Highly erratic behavior after learning phase
Advanced Features
Attention Mechanism (Extreme Mode)
Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally.
Solution:
Each RFF dimension has an importance weight . After each trade:
Calculate correlation: sign(feature - 0.5) × sign(reward)
Update importance: importance += correlation × 0.01
Clamp to range
Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes.
Temporal Context (Extreme Mode)
Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information.
Solution:
Includes 3-period historical context with exponential decay (0.85):
Current features (weight 1.0)
1 bar ago (weight 0.85)
2 bars ago (weight 0.72)
Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves.
Transfer Learning via Episodic Memory
Short-Term Memory (STM):
Last 20 trades
Fast adaptation to immediate regime
High learning rate
Long-Term Memory (LTM):
Condensed historical patterns
Preserved knowledge from past regimes
Low learning rate
Transfer Mechanism:
When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch.
Practical Implementation Guide - Recommended Settings by Instrument
Futures (ES, NQ, CL):
Adaptive Period: 20-25
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.5%
Stop Mode: ATR or Hybrid
Timeframe: 5-15 min
Forex Majors (EURUSD, GBPUSD):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.0-1.5%
Stop Mode: ATR
Timeframe: 5-30 min
Cryptocurrency (BTC, ETH):
Adaptive Period: 20-25
ML Mode: Extreme (handles non-stationarity)
RFF Dimensions: 32 (captures complexity)
Policies: 6
Base Risk: 1.0% (volatility consideration)
Stop Mode: Hybrid
Timeframe: 15 min - 4 hr
Stocks (Large Cap):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 5-6
Base Risk: 1.5-2.0%
Stop Mode: Structural or Hybrid
Timeframe: 15 min - Daily
Scaling Strategy
Phase 1 (Testing - First 50 Trades):
Max Contracts: 1-2
Goal: Validate system on your instrument
Monitor: Performance stabilization, learning progress
Phase 2 (Validation - Trades 50-100):
Max Contracts: 2-3
Goal: Confirm learning convergence
Monitor: Policy stability, exploration decay
Phase 3 (Scaling - Trades 100-200):
Max Contracts: 3-5
Enable: Kelly sizing (1/4 Kelly)
Goal: Optimize capital efficiency
Monitor: Risk-adjusted returns
Phase 4 (Full Deployment - Trades 200+):
Max Contracts: 5-10
Enable: Full momentum tracking
Goal: Sustained consistent performance
Monitor: Ongoing adaptation quality
Limitations & Disclaimers
Statistical Limitations
Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior.
Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment).
Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing.
Technical Limitations
Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded.
Pine Script Constraints:
No true matrix inversion (uses diagonal approximation for LinUCB)
No cryptographic RNG (uses market data as entropy)
No proper random number generation for RFF (uses deterministic pseudo-random)
These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications.
Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality.
Forward-Looking Bias Disclaimer
Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108.
Why This is Disclosed:
Entry signals do NOT look ahead - decisions use only data up to entry bar
Forward data used for learning only, not signal generation
In live trading, system learns identically as bars unfold in real-time
Simulates natural learning process (outcomes are only known after trades complete)
Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if:
- Positions held longer than 8 bars
- Slippage/commissions differ from backtest settings
- Market microstructure changes (wider spreads, different execution quality)
Risk Warnings
No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events.
Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations.
Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses.
Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance.
System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards.
Appropriate Use
This System Is:
✅ A machine learning framework for adaptive strategy selection
✅ A signal generation system with probabilistic scoring
✅ A risk management system with dynamic sizing
✅ A learning system designed to adapt over time
This System Is NOT:
❌ A price prediction system (does not forecast exact prices)
❌ A guarantee of profits (can and will experience losses)
❌ A replacement for due diligence (requires monitoring and understanding)
❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals)
Recommended Use:
Paper trade for 100 signals before risking capital
Start with minimal position sizing (1-2 contracts) regardless of calculated size
Monitor learning progress via dashboard
Scale gradually over several months only after consistent results
Combine with fundamental analysis and broader market context
Set account-level risk limits (e.g., maximum drawdown threshold)
Never risk more than you can afford to lose
What Makes This System Different
RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization:
✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.)
✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht)
✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook
✅ Meta-Learning - Learning rate adaptation based on feature correlation
✅ Online Learning - Real-time updates from streaming data
✅ Hierarchical Policies - Two-stage selection with clustering
✅ Momentum Tracking - Recent performance analysis for faster adaptation
✅ Attention Mechanism - Feature importance weighting
✅ Transfer Learning - Episodic memory consolidation
Key Differentiators:
Actually learns from trade outcomes (not just parameter optimization)
Updates model parameters in real-time (true online learning)
Adapts to changing market regimes (not static rules)
Improves over time through reinforcement learning
Implements published ML algorithms with proper citations
Conclusion
RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market.
The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches.
After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static.
Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Tristan's Multi-Indicator Reversal StrategyMulti-Indicator Reversal Strategy - Buy Low, Sell High
A comprehensive reversal detection system that combines multiple proven technical indicators to identify high-probability entry points for catching reversals at market extremes.
📊 Strategy Overview
This strategy is designed for traders who want to buy at lows and sell at highs by detecting when stocks are overextended and ready to reverse. It works by requiring multiple technical indicators to align before generating a signal, significantly reducing false entries.
Best Used On:
Timeframe: 1-hour charts (also works on 15min, 30min, 4hour)
Session: NY Trading Session (9:30 AM - 4:00 PM ET)
Assets: Stocks, ETFs, Crypto (particularly volatile tech stocks like ZM, TSLA, AAPL)
Trading Style: Swing trading, Intraday reversals
🔧 Technical Components
The strategy combines FIVE powerful technical indicators:
1. RSI (Relative Strength Index)
2. MACD (Moving Average Convergence Divergence)
3. Williams %R
4. Bollinger Bands
5. Volume Analysis
6. Divergence Detection (Optional)
🎨 Visual Signals
Entry Signals:
🟢 Green Triangle (below candle) = BUY LONG signal
🔴 Red Triangle (above candle) = SELL SHORT signal
Exit Signals:
🟣 Purple Label = Position closed (shows "x2", "x3" if multiple entries)
Additional Indicators:
💎 Aqua Diamond = Bullish divergence detected
💎 Fuchsia Diamond = Bearish divergence detected
🔵 Blue Background = NY Session active
🟡 Yellow Bar Tint = Volume spike detected
⚪ Small Circles = Near-signal conditions (2+ indicators aligned)
Live Counter:
Top corner shows: "Bull: X/4" and "Bear: X/4"
Indicates how many indicators currently align
⚙️ How to Use This Strategy
For Beginners (More Signals):
Set "Min Indicators Aligned" to 2
Turn OFF "Require Divergence"
Turn OFF "Require Volume Spike"
Turn OFF "Require Reversal Candle Pattern"
Keep "Allow Multiple Entries" OFF
This gives you more frequent signals to learn from.
For Advanced Traders (High Probability):
Set "Min Indicators Aligned" to 3 or 4
Turn ON "Require Divergence"
Turn ON "Require Volume Spike"
Turn ON "Require Reversal Candle Pattern"
Adjust stop loss to your risk tolerance
This filters for only the highest-quality setups.
Recommended Settings for 1-Hour Charts:
Min Indicators Aligned: 3
Stop Loss: 2.5%
Take Profit: 5.0%
RSI Length: 14
Williams %R Length: 14
Volume Multiplier: 1.5x
Session: NY only (for stocks)
BUY SIGNAL generated when:
2-4 indicators show oversold/bullish conditions:
RSI < 30 and turning up
MACD crossing bullish or histogram positive
Williams %R < -80 and turning up
Price at/below lower Bollinger Band
Optional confirmations (if enabled):
Bullish divergence detected
Volume spike present
Bullish reversal candle pattern
Session filter: Signals only during NY trading hours
SELL SIGNAL Generated When:
2-4 indicators show overbought/bearish conditions:
RSI > 70 and turning down
MACD crossing bearish or histogram negative
Williams %R > -20 and turning down
Price at/above upper Bollinger Band
Optional confirmations (if enabled):
Bearish divergence detected
Volume spike present
Bearish reversal candle pattern
🛡️ Risk Management Features
Automatic Stop Loss: Protects capital (default 2.5%)
Take Profit Target: Locks in gains (default 5.0%)
Pyramiding Control: Toggle to prevent position stacking
Session Filter: Avoids overnight risk and low-liquidity periods
Position Flipping: Automatically reverses when opposite signal appears
💡 Best Practices
✅ DO:
Wait for candle close before entering (built into strategy)
Use on volatile assets with clear trends
Combine with your own analysis and risk management
Backtest on your specific assets and timeframes
Start with paper trading to learn the signals
Adjust indicator requirements based on market conditions
❌ DON'T:
Use on very low timeframes (<5 min) without adjustment
Ignore the session filter on stocks
Use maximum leverage - these are reversal trades
Trade during major news events or earnings
Expect 100% win rate - focus on risk/reward ratio
📊 Performance Notes
This strategy prioritizes quality over quantity. With default settings, you may see:
2-5 signals per week on 1-hour charts
Higher win rate with stricter settings (3-4 indicators aligned)
Best performance during trending markets with clear reversals
Reduced performance in choppy, sideways markets
Tip: Adjust "Min Indicators Aligned" based on market conditions:
Trending markets: Use 3-4 (fewer but stronger signals)
Range-bound markets: Use 2 (more signals, but watch for false breakouts)
ALMASTO – Pro Trend & Momentum (v1.1)ALMASTO — Pro Trend & Momentum Strategy
Description:
This strategy is designed for precision trading in both Forex (FX) and Crypto markets.
It combines multi-timeframe trend confirmation (EMA200), momentum filters (RSI, MACD, ADX), and ATR-based dynamic risk management.
ALMASTO — Pro Trend & Momentum Strategy automatically manages take-profit levels, stop-loss, and breakeven adjustments once TP1 is reached — providing a structured and emotion-free trading approach.
Optimal Use
Works best on lower timeframes (5m–15m) with strong liquidity sessions.
Optimized for pairs like EURUSD, XAUUSD, and BTCUSDT.
Built for trend-following setups and momentum reversals with high volatility confirmation.
Recommended Settings
🔹 Forex – 5m
EMA Fast = 34, EMA Slow = 200, HTF = 1H
RSI (14): Long ≥ 55 / Short ≤ 45
MACD (8 / 21 / 5), ADX Len 10 / Min 27
ATR Len 7, Stop Loss = ATR × 2.1
TP1 = 1.1 RR, TP2 = 2.3 RR
Session = 07:00–11:00 & 12:30–16:00 (Exchange Time)
Risk = 0.8% per trade
🔹 Forex – 15m
EMA Fast = 50, EMA Slow = 200, HTF = 4H
RSI (14): Long ≥ 53 / Short ≤ 47
MACD (12 / 26 / 9), ADX Min 24
ATR Len 10, SL = ATR × 1.9
TP1 = 1.2 RR, TP2 = 2.6 RR
Risk = 1.0% per trade
🔹 Crypto – 5m (BTC/USDT)
EMA Fast = 34, EMA Slow = 200, HTF = 4H
RSI (14): Long ≥ 56 / Short ≤ 44
MACD (8 / 21 / 5), ADX Min 30
ATR Len 7, SL = ATR × 2.2
TP1 = 1.0 RR, TP2 = 2.5 RR
Session = 00:00–06:00 & 12:00–22:00 (UTC)
Risk = 0.5% per trade
Core Features
✅ Auto breakeven after TP1
✅ Dual take-profit system (1:1 & 1:2 RR)
✅ ATR-based stop & trailing logic
✅ Filters for session time, volume, and volatility
✅ Candle-body vs ATR size filter to avoid noise
✅ Optional cooldown between trades
Important Notes
Use bar close confirmation only (barstate.isconfirmed) to avoid repainting on lower timeframes.
Adjust commission (0.01–0.03%) and slippage (1–2 ticks) in Strategy Tester for realistic results.
Avoid low-liquidity hours (after 21:00 UTC for FX / after midnight for crypto).
Backtest using realistic broker data (e.g., BlackBull Markets / Bybit / Binance Futures).
Best results occur during London & New York sessions with moderate volatility.
⚠️ Disclaimer
This script is for educational and research purposes only.
It does not constitute financial advice.
Use proper risk management and test thoroughly before using on live accounts.
Developed by KING FX Labs
Built and optimized by Yousef Almasto — combining advanced price-action logic, multi-timeframe EMA structure, and volatility-adaptive ATR management.
Tested across Forex, Gold, and Crypto markets to ensure consistent performance and minimal drawdown.
📈 “Precision Trading. Zero Emotion. Pure Momentum.”
DNSE VN301!, ADX Momentum StrategyDiscover the tailored Pine Script for trading VN30F1M Futures Contracts intraday.
This strategy applies the Statistical Method (IQR) to break down the components of the ADX, calculating the threshold of "normal" momentum fluctuations in price to identify potential breakouts for entry and exit signals. The script automatically closes all positions by 14:30 to avoid overnight holdings.
www.tradingview.com
Settings & Backtest Results:
- Chart: 30-minute timeframe
- Initial capital: VND 100 million
- Position size: 4 contracts per trade (includes trading fees, excludes tax)
- Backtest period: Sep-2021 to Sep-2025
- Return: over 270% (with 5 ticks slippage)
- Trades executed: 1,000+
- Win rate: ~40%
- Profit factor: 1.2
Default Script Settings:
Calculates the acceleration of changes in the +DI and -DI components of the ADX, using IQR to define "normal" momentum fluctuations (adjustable via Lookback period).
Calculates the difference between each bar’s Open and Close prices, using IQR to define "normal" gaps (adjustable via Lookback period).
Entry & Exit Conditions:
Entry Long: Change in +DI or -DI > Avg IQR Value AND Close Price > Previous Close
Exit Long: (all 4 conditions must be met)
- Change in +DI or -DI > Avg IQR Value
- RSI < Previous RSI
- Close–Open Gap > Avg IQR Gap
- Close Price < Previous Close
Entry Short: Change in +DI or -DI > Avg IQR Value AND Close Price < Previous Close
Exit Short: (all 4 conditions must be met)
- Change in +DI or -DI > Avg IQR Value
- RSI > Previous RSI
- Close–Open Gap > Avg IQR Gap
- Close Price > Previous Close
Disclaimers:
Trading futures contracts carries a high degree of risk, and price movements can be highly volatile. This script is intended as a reference tool only. It should be used by individuals who fully understand futures trading, have assessed their own risk tolerance, and are knowledgeable about the strategy’s logic.
All investment decisions are the sole responsibility of the user. DNSE bears no liability for any potential losses incurred from applying this strategy in real trading. Past performance does not guarantee future results. Please contact us directly if you have specific questions about this script.
W Bottom Reversal Strategy W Bottom Reversal Strategy (15m-close entries; intrabar TP; daily MACD exit; JSON alerts v49.3-expire2)
Overview
A precision reversal strategy designed for 15-minute charts on liquid symbols. It detects a capitulation-and-stabilization “W” base using 1-hour (1H) context, confirms momentum improvement, then enters only on bar close to avoid early/“ghost” signals. Exits combine a fast intrabar take-profit (~2.7%) with a daily MACD risk-off exit that closes positions when higher-timeframe momentum turns against the setup.
How it works (high-level, matching code)
1H volatility + oversold gate (arming)
Compute 1H Bollinger-style bands (basis = SMA(close, bbLength=20), stdev multiplier bbMult=2.0).
Arm the setup when a 1H bar closes with price < 1H lower band and 1H RSI( rsiLength=14 ) < rsiThreshold (default 20.0).
1H momentum flip → pending entry
When a new 1H bar closes and 1H MACD line (EMA12−EMA26) crosses above 0 while armed and flat, set an entryPending flag.
This does not enter yet—it prepares a confirmed, bar-close entry on the lower timeframe.
Bar-close execution on the chart timeframe (15m)
On the next 15m bar close (or within N bars, see below) and still flat, fire the entry using a limit order at close × (1 − 0.00001) (≈ 0.001% below close) to reduce slippage and maintain chart/alert alignment.
Anti-late filter (no stale triggers)
If the pending entry doesn’t trigger within N chart bars (input: “Pending entry valid for N chart bars”, default 1, range 1–8), it expires and the arm state resets. This prevents late fills long after the 1H confirmation.
Exit logic
Primary: Standing intrabar take-profit at +2.7% from the average entry price (managed via strategy.exit limit).
Risk-off: On daily bar close, if Daily MACD line (EMA12−EMA26) crosses under 0, close the position (flat on daily momentum flip).
Default Properties (used for this publication)
Timeframe: 15m (with 1H and Daily higher-timeframe confirmations via request.security)
Initial capital: $10,000
Position sizing: Percent of equity = 10% per trade (enters only when flat; no stacking while in a position)
Commission: 0.05% per side
Slippage: Recommend 1 tick in Strategy Properties for realistic fills
Inputs exposed:
BB Length: 20 • BB Multiplier: 2.0
RSI Length: 14 • RSI Threshold: 20.0
MACD: Short 12, Long 26, Signal 9 (signal kept for compatibility; logic uses MACD line vs 0)
Pending entry valid for N chart bars: default 1 (1–8)
Execution behavior (per code):
calc_on_every_tick = false (evaluates on bar close)
process_orders_on_close = true (orders placed at bar close)
Limit entry at close −0.001%
Intrabar TP (2.7%)
Daily risk-off exit on MACD<0 at daily bar close
Alerts (exact behavior in code)
Uses alert() function calls with standardized JSON.
Set your alert to “Only alert() function calls” and “Once per bar close.”
Two events are emitted:
LONG_CONFIRMED on entry fire (15m bar close)
EXIT_CONFIRMED_DAILY_MACD on daily MACD<0 (daily bar close)
JSON fields include: event, version ("v49.3-expire2"), symbol, interval, price, and time.
How to use
Apply on liquid tickers (tight spreads, healthy volume).
Keep defaults initially; run across a broad, liquid watchlist to gather a proper sample.
For automation, route bar-close alerts to your executor; confirm broker lot/route settings and that limit orders at close −0.001% are acceptable.
Expect fewer signals in powerful trends; the daily risk-off helps cut failed bases.
Methodology & expectations (results transparency)
Evaluate on a dataset yielding 100+ trades before drawing conclusions.
Keep commission & slippage enabled (see defaults).
Risk sizing: With 10% of equity per trade and flat-to-flat entries, exposure aligns with typical 5–10% guidance.
No performance guarantees—outcomes depend on symbol selection, volatility regime, news, and execution quality.
Originality & value (vendor justification)
While it uses familiar building blocks (BB/RSI/MACD), the edge comes from the 1H volatility + oversold arming, 1H momentum flip, strict 15m bar-close limit execution, and the N-bar pending expiry that prevents stale triggers—paired with a dual-exit design (intrabar TP + daily risk-off). The focus is on reducing premature fills, keeping alerts 1:1 with chart marks, and capturing the first impulse out of a W-base.
Disclaimers
For educational purposes only; not financial advice. Paper-test first. Verify alerts, fills, and symbol liquidity with your broker before live use.
Changelog: v49.3-expire2 — Bar-close limit entries; anti-late pending window; standardized JSON alerts; intrabar 2.7% TP; daily MACD risk-off exit.
The Barking Rat PROThe Barking Rat PRO is designed around high/low pivot structure to capture meaningful market reversals. It intelligently identifies turning points by combining higher high/lower low (HH/LL) pivot detection, Fair Value Gap (FVG) confirmation, volatility-aware filters, and momentum checks. Unique features, such as a one-bar flip handler and a contextual ribbon overlay, provide traders with both clarity and precision. These tools help isolate high-probability setups while filtering out low-conviction signals, making trade opportunities easier to spot and act upon.
🧠 Core Logic: Structure-First, Filtered Reversals
The strategy takes a methodical, disciplined approach, prioritizing structural pivots over random signals. By layering multiple validation checks—structural pivots, gap confirmation, volatility filters, and momentum alignment—it highlights trades with high conviction while reducing exposure to noisy market conditions. The result is a clear, repeatable framework for reversal trading that can be applied across timeframes.
HH/LL Pivot Framework
Trades are triggered based on simple structural pivots: higher highs (HH) and lower lows (LL). When a structure flip occurs, the strategy either opens a new position or executes a one-bar delayed flip if an opposing position already exists. This ensures smooth transitions and avoids premature entries on minor market swings, keeping trading decisions focused on meaningful trend shifts.
Volatility & Distance Filters
To avoid low-quality trades, entries are validated against relative volatility, ensuring that pivots represent significant market movement. Trades must also be sufficiently spaced from previous entries and separated by a minimum number of bars, which prevents overtrading and clustered signals that can dilute performance.
Momentum Filter (RSI)
The strategy optionally aligns entries with momentum conditions using RSI. Long trades are favored when RSI is relatively low, suggesting potential exhaustion on the downside, while short trades are favored when RSI is relatively high, indicating potential overextension on the upside. This additional layer improves timing, helping traders avoid entering against strong, ongoing momentum.
Background Ribbon (Contextual Visuals)
A translucent ribbon overlays the chart to provide visual context of active trades. The ribbon displays volatility envelopes and position direction: green for long trades, red for short trades. It enhances clarity by giving traders a quick visual reference of the market environment without cluttering the chart.
Why These Parameters Were Chosen
The strategy focuses only on structurally meaningful pivots to ensure high-conviction trades.
Volatility filters confirm that trade signals are significant relative to recent price action, while FVG confirmation captures institutional-style imbalances.
Momentum and spacing rules prevent low-quality entries and overtrading, while the one-bar flip handler ensures seamless transitions when the structure reverses.
Ribbon overlays provide intuitive, real-time visualization of active trades and market context.
📈 Chart Visuals: Clear & Intuitive
- Green “▲” below a candle: Long entry triggered on LL → HH structure flip
- Red “▼” above a candle: Short entry triggered on HH → LL structure flip
- Translucent Ribbon: Green when long, Red when short
🔔 Alerts: Stay Notified Without Watching
The strategy supports real-time alerts on candle close, ensuring that only fully confirmed signals trigger notifications.
You must manually configure alerts within your TradingView account. Once set up, a single alert per instrument covers all relevant entries and exits, making hands-free monitoring simple and efficient.
⚙️ Strategy Report Properties
Position size: 25% of equity per trade
Initial capital: 10,000.00 USDT
Pyramiding: 25 entries per direction
Slippage: 2 ticks
Commission: 0.055% per side
Backtest timeframe: 1-minute
Backtest instrument: HYPEUSDT
Backtesting range: Aug 11, 2025 — Aug 28, 2025
💡Why 25% Equity Per Trade?
While it's always best to size positions based on personal risk tolerance, we defaulted to 25% equity per trade in the backtesting data — and here’s why:
Backtests using this sizing show manageable drawdowns even under volatile periods
The strategy generates a sizeable number of trades, reducing reliance on a single outcome
Combined with conservative filters, the 25% setting offers a balance between aggression and control
Users are strongly encouraged to customize this to suit their risk profile.
🔍 What Makes This Strategy Unique?
HH/LL Pivot Focus: Trades pivot structure flips instead of relying on generic indicators.
Fair Value Gap Confirmation: Only pivots supported by FVGs are acted upon, reducing noise.
One-Bar Flip Handler: Ensures clean transitions when the structure reverses, avoiding same-bar conflicts.
Volatility & Spacing Filters: Trades require sufficient movement from prior entries and minimum bar spacing to maintain quality.
Momentum-Aware Entries: RSI alignment favors entries near potential exhaustion points, improving signal reliability.
Contextual Ribbon Overlay: Visualizes volatility and active positions clearly, without cluttering the chart.
