Crypto Tipster Pro===========
Crypto Tipster Pro Strategy
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Crypto Tipster Pro is a trading strategy with indicators based on Technical Analysis , Price Action and Momentum Swings for TradingView's charting platform.
We've compiled and continue to maintain a trading strategy that adapts to changes in the market; with custom indicator settings, fixed SL/TP, Trailing Stop, Safe Mode, Heikin Ashi Confirmation, Multi-Time Frame Analysis and more!
Our efforts have been focused towards the 1D time frame - using a larger time frame benefits most part-time or evening traders in multiple ways, catching bigger swings and earning a higher percentage per trade, the ability to reduce or remove any leverage associated with the trade, and only having to place a trade or move a stop loss ONCE per day ~ Meaning you are still able to go to work, tidy the house, play with the kids AND be a successful trader.
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What's Included?
Crypto Tipster Pro comes with a host of features and is being continually updated, these features include (but are not limited to):
- Date Range Settings
Setting custom Start/End dates can help hone your strategy to suit the current times, or get a general overview of the market over the years.
- Heikin Ashi Confirmation
We added HA confirmation for both Entry & Exit of trades. This started as a form of "Safe Mode", we have since adapted this mode beyond Heikin Ashi; but kept this confirmation as an added extra.
- Variable Indicator Settings
As well as our Fixed Indicators and Price Action analysis going on in the background of the strategy, we've also included some Variable Indicators that you have access to edit.
Trend Detection Length for detecting trend over a given length! Higher numbers detect longer trends, but will inevitably make fewer trades and possibly miss the start of a new trend; a lower length will create more opportunities to trade but may get confused when in choppy markets.
Range Short/Long Lengths are used for detecting percentage price movements over a given number of bars back. This enables you to effectively "zoom in" on market data and catch trends within trends.
- Safe Mode
Enabling Safe Mode will add a couple more confirmation indicators to the strategy - the aim of Safe Mode is, in essence, to remove any trading signals that would end of being false/bad moves. Usually resulting in less Overall Trades, a higher Net Profit, higher % Profitable, higher Profit Factor AND a lower Drawdown. Use Safe Mode to help eliminate orders that would otherwise be placed in choppy markets.
- Stop Loss/Take Profit Settings
This is where Crypto Tipster Pro really proves itself, Money Management. We have an editable Fixed SL/TP, as well as Trailing Stops for Long or Short orders, all of which you can use on their own, or combined with each other. Playing with these settings can turn an un-profitable system into a very-profitable trading plan!
- Custom Stop Loss Indicator
This is a little extra indicator that we have found very useful over the years of trading markets, a custom Stop Loss Indicator. Simply turn it on, enter the price you want to calculate from, tick Long or Short, enter a % movement and see your new stop loss level plotted on the chart. This is especially useful for when the strategy doesn't marry up with the prices you've actually obtained (for better or for worse!)
We've tried to make this strategy as comprehensive and as accurate as possible, it works consistently over many trading pairs on many time frames. We would however Love your assistance! -please forward any notes or helpful tips to us either by commenting below, on Twitter or a direct message through our website.
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For more information and a FREE 7-Day Trial with the Crypto Tipster Pro Strategy visit the link in our signature.
Good Luck and Happy Trading!
Komut dosyalarını "stop loss" için ara
Cyatophilum Intraday Breakouts [BACKTEST]Private indicator. Access can be unlocked by purchasing a subscription on my website which link is in my profile signature.
Here is the backtest version of the Triple Screen Strategy
Recap of the strategy:
The goal of this indicator is to be able to automate and backtest the strategy, all while staying on a single chart and without repainting.
Features:
Market Tide MACD configuration
Market Wave choice between 3 oscillator: Elder Force Index, Stochastic and William Percent Range
Automated Long and Short entries alerts
Integrated Trailing Stop Loss system fully configurable with automated exit alerts
Integrated Trailing Take profit system fully configurable with automated exit alerts
Indicator samples
Backtest
Strategy time period can be choosen in the parameters of the indicator.
Be aware that the chart is limited to 10 000 candles of the current timeframe for the backtest calculation.
Default initial capital: 10 000$
Default order size: 100% of equity
Default commission fees: 0.1% per transaction
Backtest results below.
Alerts
Entry Long: Triggers on green long labels.
Take Profit Long: Triggers on the "TAKE PROFIT" green flag if the long target is reached.
Stop Loss Long: Triggers on the "STOP LOSS" or "TSSL" label if the stop loss line has been crossed.
Exit Long: Triggers on either of Take Profit Long or Stop loss long.
Entry Short: Triggers on red short labels.
Take Profit Short: Triggers on the "TAKE PROFIT" green flag if the short target is reached.
Stop Loss Short: Triggers on the "STOP LOSS" or "TSSL" label if the stop loss line has been crossed.
Exit Short: Triggers on either of Take Profit Short or Stop Loss Short.
You can get access to this indicator by purchasing a subscription using the link below.
Thanks for reading!
2-Period RSI strategy (with filter)2-period RSI strategy backtest described in several books of the trader Larry Connors . This strategy uses a 2 periods RSI , one slow arithmetic moving average and one fast arithmetic moving average.
Entry signal:
- RSI 2 value below oversold level (Larry Connors usually sets oversold to be below 5, but other authors prefer to work below 10 due to the higher number of signals).
- Closing above the slow average (200 periods).
- Entry at closing of candle or opening of next candle.
Exit signal:
- Occurs when the candlestick closes above the fast average (the most common fast average is 5 periods, but some traders also suggest the 10 period average).
Entry Filter (modification made by me):
- Applied an RSI2 arithmetic moving average to smooth out oscillations.
- Entered only when RSI2 is below oversold level and RSI2 moving average is below 30.
* NOTE: In the stocks that I evaluate daily the averages of 4 and 6 periods work very well as a filter.
Comments:
This strategy works very well in Daily charts but can be applied in other chart times as well. As this is a strategy to catch market fluctuations, it presents different results with different stocks.
I have been applying this strategy to the stocks of the Brazilian market (BOVESPA) and have enjoyed the result. Every day I evaluate the stocks that are generating entry signals and choose which one to trade based on the stocks with the highest Profit Value.
The RSI 2 averaging filter probably will reduce profit of the backtests because reduces the number of signals, but the Profit Value will usually increase. For me this was a good thing because without the filter, this strategy usually shows more signals than I have capital to allocate.
Before entering a trade I look at which fast average the paper has the highest Profit Value and then I use this average as my output signal for that trade (this change has greatly improved the result of the outputs).
This strategy does not use Stop Loss because normally Stop Loss decreases effectiveness (profit). In any case, the option to apply a percentage Stop Loss if desired is added in the script. As the strategy does not use stop, extra caution with risk management is advisable. I advise not to allocate more than 20% of the trade capital in the same operation.
I'm still studying ways to improve this strategy, but so far this is the best setup I've found. Suggestions are always welcome and we can test to see if they improve the backtest result.
Good luck and good trades.
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Backtest das estratégia do IFR de 2 períodos descrita em varios livros do trader Larry Connors . Esta estratégia usa um IFR de 2 períodos, uma média movel aritmética lenta e uma média movel aritmética rápida.
Sinal de entrada:
- Valor do IFR 2 abaixo do nível de sobrevenda (Larry Connors usualmente define sobrevenda sendo abaixo de 5, mas outros autores preferem trabalhar abaixo de 10 devido ao maior número de sinais).
- Fechamento acima da média lenta (200 períodos).
- Realizado a compra no fechamento do candle ou na abertura do candle seguinte.
Sinal de saída:
- Ocorre quando o candle fecha acima da média rápida (a média rápida mais comum é a de 5 períodos, mas alguns traders sugerem também a média de 10 períodos).
Filtro para entrada (modificação feita por mim):
- Aplicado uma média móvel aritmética do IFR2 para suavisar as oscilações.
- Realizado a entrada apenas quando o IFR2 está abaixo do nível de sobrevenda e a média móvel do IFR2 está abaixo de 30.
*OBS: nos ativos que avalio diariamente as médias de 4 e 6 períodos funcionam muito bem como filtro.
Comentários:
Esta estratégia funciona muito bem no tempo gráfico Diário mas pode ser aplicada tambem em outros tempos gráficos. Como trata-se de uma estratégia para pegar oscilações do mercado, ela apresenta diferentes resultados com diferentes ativos.
Eu venho aplicando esta estratégia nos ativos do mercado brasileiro (BOVESPA) e tenho gostado do resultado. Diariamente eu avalio os papeis que estão gerando entrada e escolho qual irei realizar o trade baseado nos papeis que apresentam maior Profit Value.
O filtro da média do IFR 2 reduz o lucro nos backtests pois reduz também a quantidade de sinais, mas em compensação o Profit Value irá normalmente aumentar. Para mim isto foi algo positivo pois, sem o filtro, normalmente esta estratégia apresenta mais sinais do que possuo capital para alocar.
Antes de entrar em um trade eu olho em qual média rápida o papel apresenta maior Profit Value e então eu utilizo está média como meu sinal de saída para aquele trade (esta mudança tem melhorado bastante o resultado das saídas).
Está estratégia não utiliza Stop Loss pois normalmente o Stop Loss diminui a eficácia (lucro). De qualquer maneira, foi acrescentado no script a opção de aplicar um Stop Loss percentual caso seja desejado. Como a estratégia não utiliza stop é aconselhável um cuidado redobrado com o gerenciamento de risco. Eu aconselho não alocar mais de 20% do capital de trade em uma mesma operação.
Ainda estou estudando formas de melhorar esta estratégia, mas até o momento está é a melhor configuração que encontrei. Sugestões são sempre bem vindas e podemos testar para verificar se melhoram o resultado do backtest.
Boa sorte e bons trades.
Cyatophilum Bands Pro Trader V3 [BACKTEST]An Original Automated Strategy that can be used for Manual or Bot Trading, on any timeframe and market.
>> Presentation <<
How it works
No, these are NOT Bollinger Bands..
The Cyatophilum Bands are an original formula that I created. You will probably never find it anywhere else.
Their behavior is the following:
When they are horizontal it means the trend is going sideways and they represent supports (lower band) and resistances (upper band).
When they are climbing or falling it means the trend is either bullish or bearish and they represent Trend Lines.
The strategy enters Long on a Bull Breakout and enters Short on a Bear Breakout.
The exits are triggered either on a Trend Reversal, a Stop Loss or a Take Profit.
FEATURES
Take Profit System
Stop Loss System
Show Net profit Line
More features here
Finding a profitable configuration is GUARANTEED
0. Choose your symbol and timeframe. Then add the Backtest version to your chart. If at any time you decide to change your timeframe, go back to step 1.
1. Open the strategy tester and look at the buy & hold line.
If it is mostly climbing (last value greater than 0) then it means we are in a bull market. You should then opt or a long only strategy.
If it is mostly dropping (last value lower than 0) then it means we are in a bear market. You should then opt or a short only strategy.
Note : This first step is really important. Trading against the market has very little chances to succeed.
2. Go into the Strategy Input Parameters:
check "Enable Long Results" and uncheck "Enable Short Results" if you are in a long only strategy.
check "Enable Short Results" and uncheck "Enable Long Results" if you are in a short only strategy.
3. Open the Strategy Tester and open the Strategy Properties.
We are going to find the base parameters for the Bands.
The "Bands Lookback" is the main parameter to configure for any strategy. It corresponds to how strong of a support and resistance the bands will behave. The lower the timeframe, the higher lookback you will need. It can move from 10 to 60. For example 60 is a good value for a 3 minute timeframe. Try different values, and look at the "net profit" value in the Overview tab of the Strategy Tester. Keep the Lookback value that shows the best net profit value.
Then play with the "Bands Smoothing" from 2 to 20 and keep the best net profit value.
The "Band Smoothing" is used to reduce noise.
Usually, the default value (10) is what gives the best results.
From this point you should already be able to have a profitable strategy (net profit>0), but we can improve it using the Stop Loss and the Take Profit feature.
4. To activate the Stop Loss feature, click on the "SECURITY" checkbox
You should see horizontal red lines appear.
A Long/short exit alert will be triggered if the price were to cross this line. (A red Xcross will appear)
Choose the Stop Loss percentage.
On top of that, you can enable the feature "Trailing Stop". It will make the red line follow the price, at a speed that you can configure with the "Trailing Speed" parameter.
Now, sometimes a stop is triggered and it was just a fakeout. You can enable "Re-entries after a stop" to avoid missing additional opportunities.
5. To activate the Take Profit feature, click on the "TAKE PROFIT" checkbox
You should see horizontal green lines appear.
A Long/short exit alert will be triggered if the price were to cross this line. (A flag will appear)
Choose the Take Profit percentage.
A low takeprofit will provide a safer strategy but can reduce potential profits.
A higher takeprofit will increase risk but can provide higher potential profits.
6. Money Management
You can configure the backtest according to your own money management.
Let's say you have 10 000 $ as initial capital and want to trade only 5%, set the Order Size to 5% of Equity.
You can increase net profit by increasing the order size but this is at your own risk.
How to create alerts explained here
Sample Uses Cases
Use it literally anywhere
This indicator can be used on any timeframe and market (not only cryptocurrencies).
About the Backtest below
The Net Profit (Gross profit - Gross loss) is calculated with a commission of 0.05% on each order.
No leverage used. This is a long strategy.
Each trade is made with 10 % of equity from an inital capital of 10 000$. The net profit can be bigger by increasing the % of equity but this a trader's rule to minimise the risk.
I am selling access to all my indicators on my website : blockchainfiesta.com
To get a 2 days free trial, just leave a comment , thanks !
Join my Discord for help, configurations, requests, etc. discord.gg
Hophop Strategy DemoThis is the demo of hophop strategy that can only be executed for the pairs and timeframes listed below
"BTCUSD"
"XBTUSD"
"ETHUSD"
"ETHBTC"
"ETHXBT"
"XRPUSD"
"XRPBTC"
"XRPXBT"
"10"
"30"
"45"
"60"
"120"
"240"
I have added dynamic trailing stop loss that can be used as a stop loss when trade is not in profit alternatively you can use it as a take profit points if you don't want to close the trade aggressively
For those who hasnt worked with strategy before
Blue arrow : Long
Red arrow: Short
Purple arrow: Close active trade
if strategy is on a long trade and active trade is in profit, you can use the red line as stop loss or take profit
if strategy is on a long trade and active trade is in loss, you can use the red line as stop loss if active trade hasn't closed already
if strategy is on a short trade and active trade is in profit, you can use the green line as stop loss or take profit
if strategy is on a short trade and active trade is in loss, you can use the green line as stop loss if active trade hasn't closed already
In full version active stop loss/take profit is embedded to strategy and they are configurable according to your risk appetite
Strategy results are for the dates between 01.01.2018 - 01.10.2018 . ( which includes volatile bear market and choppy sideways market )
NGRN MACD-X & RSI v4 STRATEGYMACD-X, RSI & Volume Indicator Strategy - Version 4
Overview
This strategy and it's associated study were modeled after the famous Philakone described algorithms on his now defunct instructional video series.
This indicator allows for full customisation of parameters and interaction between three indicators that allow users to shape their trading methods to their desired goals. This associated strategy also allows users to backtest the study alerts script and find the best settings towards that end.
MACD + RSI + VOLUME - are of the most powerful and widely usded indicators, MACD/Histogram crosses, coupled with RSI & Volume increases/decreases will detects areas of deeply oversold / overbought and buys/sells on the reversal
Features
Full customisation - All parameters are open for customising to allow the trader to build their own strategy and adapt from market to market.
Clean/Simple UI - Facilitating ease of use.
Enable Buying OR Selling, - or have them both active at the same time.
Toggle off and on ALTERNATING Buy and Sell feature (pyramiding) - to allow for consecutive DCA style buys or SCALING out of an entry (partial sell).
Customizable Stop-Loss plot - to enable users to create a STOP-LOSS alert option or other alert(s) based on the plot location.
Toggle Auto Stop-Loss sell option - to enable users choose whether or not to automatically issue a sell signal when close crosses stop loss plot, or choose to toggle off if not profitable.
Customizable Take-Profit plot - to enable users to create a TAKE-PROFIT alert option or other alert(s) based on the plot location.
Study and associated Strategy - to use the TradingView ‘Strategy Tester’ back-testing features to find the best alert settings for specific coins in bear, bull and sideways markets.
Changes Version 4
Improved STOP-LOSS plot draw.
Added the option to automatically sell when stop-loss cross triggered or have the option disabled, in the event a better profit can be achieved.
Added new TAKE-PROFIT plot (aqua line) to visually guide users where to place the TAKE-PROFIT parameter as well as give users options to create alerts based on the TAKE-PROFIT plot.
Access
Full Access is 0.1 ETH , one time fee for LIFETIME access to the STUDY indicator, STRATEGY and future updates as well as support and SETTINGS for various markets on the Binance Exchange.
Settings
SCREENSHOT LINKS:
BUY SETTINGS: prntscr.com
SELL SETTINGS: prntscr.com
NGRN MACD-X & RSI v3.1 STRATEGYMACD-X, RSI & Volume Indicator Strategy - Version 3.1
Overview
This strategy and it's associated study were modeled after the famous Philakone described algorithms on his now defunct instructional video series.
This indicator allows for full customisation of parameters and interaction between three indicators that allow users to shape their trading methods to their desired goals. This associated strategy also allows users to backtest the study alerts script and find the best settings towards that end.
MACD + RSI + VOLUME - are of the most powerful and widely usded indicators, MACD/Histogram crosses, coupled with RSI & Volume increases/decreases will detects areas of deeply oversold / overbought and buys/sells on the reversal
Features
Full customisation - All parameters are open for customising to allow the trader to build their own strategy and adapt from market to market.
Clean/Simple UI - Facilitating ease of use.
Enable Buying or Selling, - or have them both active at the same time.
Toggle off and on ALTERNATING Buy and Sell feature (pyramiding) - to allow for consecutive dollar cost averaging style buys or SCALING out of an entry (partial sell).
Customizable Stop-Loss plot - to enable users to create a STOP-LOSS alert option or other alert(s) based on the plot location. See settings screenshot.
Study and associated Strategy - to use the TradingView ‘Strategy Tester’ back-testing features to find the best alert settings for specific coins in bear, bull and sideways markets.
Changes Version 3.1
UI consolidates reduntant script inputs making the script easier to use.
Fixes STOP-LOSS algorithm.
Adds a STOP-LOSS Plot (red line) to enable users to create a STOP-LOSS alert option or other alert(s) based on the plot location. See settings screenshot.
Access
Full Access is 0.1 ETH , one time fee for LIFETIME access to the STUDY indicator, STRATEGY and future updates as well as support and SETTINGS for various markets on the Binance Exchange.
Settings
BUY SETTINGS: prntscr.com
SELL SETTINGS: prntscr.com
STOP-LOSS SETTINGS : prntscr.com
ETHUSD - Bitfinex - 5 minutes - fastThe same principle of the other ETHUSD script for autoview, with more generic signals.
Safety is because of the Stop Loss (with editable values) that turns the hand in the operation.
There are more operations, however, as you can see, losses can increase.
For those who do not have the patience to wait for the signs of the other.
Backtest properties
. Initial: 10k usd
. Currency: USD
. Pyramiding: 0
. Order Size: 100% equity
. Comission: 0.25%
>>>>>>>>>>>>>>>>>>> ADVICE <<<<<<<<<<<<<<<<<<<<<<<<<<<<<
This script was created on the BitFINEX chart in 5 minutes.
It is not recommended for use in another pair, another exchange or another timeframe.
[Autoview][BackTest]Dual MA Ribbons R0.12 by JustUncleLThis is an implementation of a strategy based on two MA Ribbons, a Fast Ribbon and a Slow Ribbon. This strategy can be used on Normal candlestick charts or Renko charts (if you are familiar with them).
The strategy revolves around a pair of scripts: One to generate alerts signals for Autoview and one for Backtesting, to tune your settings.
The risk management options are performed within the script to set SL(StopLoss), TP(TargetProfit), TSL(Trailing Stop Loss) and TTP (Trailing Target Profit). The only requirement for Autoview is to Buy and Sell as directed by this script, no complicated syntax is required.
The Dual Ribbons are designed to capture the inferred behavior of traders and investors by using two groups of averages:
> Traders MA Ribbon: Lower MA and Upper MA (Aqua=Uptrend, Blue=downtrend, Gray=Neutral), with center line Avg MA (Orange dotted line).
> Investors MAs Ribbon: Lower MA and Upper MA (Green=Uptrend, Red=downtrend, Gray=Neutral), with center line Avg MA (Fuchsia dotted line).
> Anchor time frame (0=current). This is the time frame that the MAs are calculated for. This way 60m MA Ribbons can be viewed on a 15 min chart to establish tighter Stop Loss conditions.
Trade Management options:
Option to specify Backtest start and end time.
Trailing Stop, with Activate Level (as % of price) and Trailing Stop (as % of price)
Target Profit Level, (as % of price)
Stop Loss Level, (as % of price)
BUY green triangles and SELL dark red triangles
Trade Order closed colour coded Label:
>> Dark Red = Stop Loss Hit
>> Green = Target Profit Hit
>> Purple = Trailing Stop Hit
>> Orange = Opposite (Sell) Order Close
Trade Management Indication:
Trailing Stop Activate Price = Blue dotted line
Trailing Stop Price = Fuschia solid stepping line
Target Profit Price = Lime '+' line
Stop Loss Price = Red '+' line
Dealing With Renko Charts:
If you choose to use Renko charts, make sure you have enabled the "IS This a RENKO Chart" option, (I have not so far found a way to Detect the type of chart that is running).
If you want non-repainting Renko charts you MUST use TRADITIONAL Renko Bricks. This type of brick is fixed and will not change size.
Also use Renko bricks with WICKS DISABLED. Wicks are not part of Renko, the whole idea of using Renko bricks is not to see the wick noise.
Set you chart Time Frame to the lowest possible one that will build enough bricks to give a reasonable history, start at 1min TimeFrame. Renko bricks are not dependent on time, they represent a movement in price. But the chart candlestick data is used to create the bricks, so lower TF gives more accurate Brick creation.
You want to size your bricks to 2/1000 of the pair price, so for ETHBTC the price is say 0.0805 then your Renko Brick size should be about 2*0.0805/1000 = 0.0002 (round up).
You may find there is some slippage in value, but this can be accounted for in the Backtest by setting your commission a bit higher, for Binance for example I use 0.2%
Special thanks goes to @CryptoRox for providing the initial Risk management Framework in his "How to automate this strategy for free using a chrome extension" example.
HL MovingAvg2Line Cross Dhananjay
Sharing the simple trend following trading strategy, traders can add their own rules in this, to minimise the losses and maximise the profits. Like below.
1. Go long only if price is above 189 days EMA/SAM
2. Exit position when high or low of previous candle is breached in the opposite direction of the trend.
3. Go long only if price is in up trend on higher time frame charts and go short when price is down trend of higher time frame charts.
Stop loss, target and other things can also be decided by the trader.
Idea is to capture the short term trend to trade in FnO or 2/3 days position in underlying instrument.
Traders can optimise the length of the Moving average so that your traded is set for maximum profit giving settings for this strategy. Different instruments responds to different moving averages because of different volatility.
Idea is to go long when price closes above 9 days EMA of Highs and exit and go short whenever price closes below 9 days EMA of lows, exit short when first condition meets after short trade.
I ma not that good with scripts, have many such ideas, interested script writers can get in touch with me so that we can create trading systems which have grater success rate .
8ma34 EURUSD 1h 480tp 950slCrossing 8 sma and 34 sma on the 1h chart (close) of EURUSD.
If sma (8) crossing up sma (34) then open a long on closed bar with +480 pips for the take profit and -950 pips for the stop loss.
If sma (8) crossing down sma (34) then open a short on closed bar with -480 pips for the take profit and +950 pips for the stop loss.
Golden Cross, SMA 200 Moving Average Strategy (by ChartArt)This famous moving average strategy is very easy to follow to decide when to buy (go long) and when to take profit.
The strategy goes long when the faster SMA 50 (the simple moving average of the last 50 bars) crosses above the slower SMA 200. Orders are closed when the SMA 50 crosses below the SMA 200. This simple strategy does not have any other stop loss or take profit money management logic. The strategy does not short and goes long only!
Here is an article explaining the "golden cross" strategy in more detail:
www.stockopedia.com
On the S&P 500 index (symbol "SPX") this strategy worked on the daily chart 81% since price data is available since 1982. And on the DOW Jones Industrial Average (symbol "DOWI") this strategy worked on the daily chart 55% since price data is available since 1916. The low number of trades is in both cases not statistically significant though.
All trading involves high risk; past performance is not necessarily indicative of future results. Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown.
Daily Close Comparison Strategy (by ChartArt via sirolf2009)Comparing daily close prices as a strategy.
This strategy is equal to the very popular "ANN Strategy" coded by sirolf2009(1) which calculates the percentage difference of the daily close price, but this bar-bone version works completely without his Artificial Neural Network (ANN) part.
Main difference besides stripping out the ANN is that my version uses close prices instead of OHLC4 prices, because they perform better in backtesting. And the default threshold is set to 0 to keep it simple instead of 0.0014 with a larger step value of 0.001 instead of 0.0001. Just like the ANN strategy this strategy goes long if the close of the current day is larger than the close price of the last day. If the inverse logic is true, the strategy goes short (last close larger current close). (2)
This basic strategy does not have any stop loss or take profit money management logic. And I repeat, the credit for the fundamental code idea goes to sirolf2009.
(2) Because the multi-time-frame close of the current day is future data, meaning not available in live-trading (also described as repainting), is the reason why this strategy and the original "ANN Strategy" coded by sirolf2009 perform so excellent in backtesting.
All trading involves high risk; past performance is not necessarily indicative of future results. Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown.
(1) You can get the original code by sirolf2009 including the ANN as indicator here:
(1) and this is sirolf2009's very popular strategy version of his ANN:
MACD + Stochastic, Double Strategy (by ChartArt)This strategy combines the classic stochastic strategy to buy when the stochastic is oversold with a classic MACD strategy to buy when the MACD histogram value goes above the zero line. Only difference to the classic stochastic is a default setting of 71 for overbought (classic setting 80) and 29 for oversold (classic setting 20).
Therefore this strategy goes long if the MACD histogram goes above zero and the stochastic indicator detects a oversold condition (value below 29). If the inverse logic is true, the strategy goes short (stochastic overbought condition with a value above 71 and the MACD histogram falling below the zero line value).
Please be aware that this pure double strategy using simply two classic indicators does not have any stop loss or take profit money management logic.
All trading involves high risk; past performance is not necessarily indicative of future results. Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown.
EMA Crossover + RSI Filter with ATR StopsCore Concept & Logic:
This strategy utilizes a powerful combination of Exponential Moving Average (EMA) crossovers and Relative Strength Index (RSI) filters for signal validation. It includes dynamic risk management by setting Take-Profit and Stop-Loss targets based on the Average True Range (ATR).
How It Works & Signal Interpretation:
EMA Crossovers: A bullish signal is generated when the faster EMA (20-period default) crosses above the slower EMA (50-period default), indicating upward momentum. A bearish signal occurs when the fast EMA crosses below the slow EMA.
RSI Filter: Ensures entries aren't made during extreme market conditions (avoids longs when RSI > 70, avoids shorts when RSI < 30).
ATR-Based Stops: Automatically calculates realistic Stop-Loss and Take-Profit targets, helping manage risk relative to recent volatility.
Key Input Parameters:
Fast EMA Length: Recommended between 10-30 (default 20).
Slow EMA Length: Recommended between 40-100 (default 50).
RSI Length: Typically 14 periods.
RSI Overbought Threshold: 70 (standard RSI practice).
RSI Oversold Threshold: 30 (standard RSI practice).
ATR Length: Typically 14 periods for standard volatility measure.
Stop-Loss Multiplier: Recommended range: 1.5-2.5 (default 1.5).
Take-Profit Multiplier: Recommended range: 2-4 (default 3).
Ideal Usage & Performance Scenarios:
Performs well in trending markets (stocks, crypto, forex).
Potentially weaker during choppy or sideways markets due to false EMA crossovers.
Ideal on timeframes like 1H, 4H, and 1D charts.
Known Limitations & Risks:
EMA strategies can produce false signals in ranging markets.
RSI filter may limit entries in persistently strong trending conditions.
ATR-based stops might not accommodate sudden volatility spikes.
For more such strategies visit stratizone.com where you will get settings as well. The platform also offers to share the strategies and find them easily with lots of filters.
The equity curve is on BTC, 15min
Quant Trading Zero Lag Trend Signals (MTF) Strategy🧠 Strategy Overview
The Quant Trading Zero Lag Trend Signals (MTF) Strategy is a high-precision, multi-timeframe trend-following system designed for traders seeking early trend entries and intelligent exits. Built around ZLEMA-based signal detection, it includes dynamic risk management features. Based on the original indicator Zero Lag Trend Signals (MTF) from AlgoAlpha, now built as a strategy with several improvements for Exit Criteria include RR, ATR Stop Loss, Trailing stop loss, etc. See below.
🔍 Key Components
1️⃣ ZLEMA Trend Engine
ZLEMA (Zero-Lag EMA) forms the foundation of the trend signal system.
Detects bullish and bearish momentum by analyzing price action crossing custom ZLEMA bands.
Optional confirmation using 5-bar ZLEMA slope filters (up/down trends) ensures high-conviction entries.
2️⃣ Volatility-Based Signal Bands
Dynamic bands are calculated using ATR (volatility) stretched over 3× period length.
These bands define entry zones (outside the bands) and trend strength.
Price crossing above/below the bands triggers trend change detection.
3️⃣ Entry Logic
Primary long entries occur when price crosses above the upper ZLEMA band.
Short entries (optional) trigger on downside cross under the lower band.
Re-entry logic allows continuation trades during strong trends.
Filters include date range, ZLEMA confirmation, and previous position state.
4️⃣ Exit Logic & Risk Management
Supports multiple customizable exit mechanisms:
🔺 Stop-Loss & Take-Profit
ATR-Based SL/TP: Uses ATR multipliers to dynamically set levels based on volatility.
Fixed Risk-Reward TP: Targets profit based on predefined RR ratios.
Break-Even Logic: Automatically moves SL to entry once a threshold RR is hit.
EMA Exit: Optional trailing exit based on price vs. short EMA.
🔀 Trailing Stop
Follows price action using a trailing ATR-based buffer that tightens with trend movement.
🔁 Trend-Based Exit
Automatically closes positions when the detected trend reverses.
5️⃣ Multi-Option Trade Filtering
Enable/disable short trades, ZLEMA confirmations, re-entries, etc.
Time-based backtesting filters for isolating performance within custom periods.
6️⃣ Visual Feedback & Annotations
Trend shading overlays: Green for bullish, red for bearish zones.
Up/Down triangle markers show when ZLEMA is rising/falling for 5 bars.
Stop-loss, TP, trailing lines drawn dynamically on the chart.
Floating stats table displays live performance (PnL, win %, GOA, drawdown, etc.).
Trade log labels annotate closed trades with entry/exit, duration, and reason.
7️⃣ CSV Export Integration
Seamless export of trade data including:
Entry/exit prices
Bars held
Encoded exit reasons
Enables post-processing or integration with external optimizers.
⚙️ Configurable Parameters
All key elements are customizable:
Entry band length and multiplier
ATR lengths, multipliers, TP/SL, trailing stop, break-even
Profit target RR ratio
Toggle switches for confirmations, trade types, and exit methods
QQQ Strategy v2 ESL | easy-peasy-x This is a strategy optimized for QQQ (and SPY) for the 1H timeframe. It significantly outperforms passive buy-and-hold approach. With settings adjustments, it can be used on various assets like stocks and cryptos and various timeframes, although the default out of the box settings favor QQQ 1H.
The strategy uses various triggers to take both long and short trades. These can be adjusted in settings. If you try a different asset, see what combination of triggers works best for you.
Some of the triggers employ LuxAlgo's Ultimate RSI - shoutout to him for great script, check it out here .
Other triggers are based on custom signed standard deviation - basically the idea is to trade Bollinger Bands expansions (long to the upside, short to the downside) and fade or stay out of contractions.
There are three key moving averages in the strategy - LONG MA, SHORT MA, BASIC MA. Long and Short MAs are guides to eyes on the chart and also act as possible trend filters (adjustable in settings). Basic MA acts as guide to eye and a possible trade trigger (adjustable in settings).
There are a few trend filters the strategy can use - moving average, signed standard deviation, ultimate RSI or none. The filters act as an additional condition on triggers, making the strategy take trades only if both triggers and trend filter allows. That way one can filter out trades with unfavorable risk/reward (for instance, don't long if price is under the MA200). Different trade filters can be used for long and short trades.
The strategy employs various stop loss types, the default of which is a trailing %-based stop loss type. ATR-based stop loss is also available. The default 1.5% trailing stop loss is suitable for leveraged trading.
Lastly, the strategy can trigger take profit orders if certain conditions are met, adjustable in settings. Also, it can hold onto winning trades and exit only after stop out (in which case, consecutive triggers to take other positions will be ignored until stop out).
Let me know if you like it and if you use it, what kind of tweaks would you like to see.
With kind regards,
easy-peasy-x
Reversal Trap Sniper – Verified VersionReversal Trap Sniper
Overview
Reversal Trap Sniper is a counterintuitive momentum-following strategy that identifies "reversal traps"—situations where traders expect a market reversal based on RSI, but the price continues trending. By detecting these failed reversal signals, the strategy enters trades in the trend direction, often catching strong follow-through moves.
How It Works
The system monitors the Relative Strength Index (RSI). When RSI moves above the overbought level (e.g., 70) and then drops back below it, many traders interpret this as a sell signal.
However, this strategy treats such moves with caution. If the RSI pulls back below the overbought threshold but the price continues to rise, the system considers it a "reversal trap"—a fakeout.
In such cases, instead of going short, the strategy enters a long position, assuming that the trend is still valid and those betting on a reversal may fuel a breakout.
Similarly, if RSI rises above the oversold level from below, but price continues falling, a short trade is triggered.
Entries are followed by ATR-based stop-loss and dynamic take-profit (2× risk), with a fallback time-based exit after 30 bars.
Key Features
- Detects failed RSI-based reversals ("traps")
- Follows momentum after the trap is triggered
- Uses ATR for dynamic stop-loss and take-profit
- Auto-exit after a fixed bar count (30 bars)
- Visual markers on chart for transparency
- Realistic trading assumptions: 0.05% commission, slippage, and capped pyramiding
Parameter Explanation
RSI Length (14): Standard RSI calculation period
Overbought/Oversold Levels (70/30): Common thresholds used by many traders
ATR Length (14): Used to define stop-loss and target dynamically
Risk-Reward Ratio (2.0): Take-profit is set at 2× the stop-loss distance
Max Holding Bars (30): Ensures trades don’t remain open indefinitely
Pyramiding (10): Allows scaling into trades, simulating real-world strategy stacking
Originality Note
This strategy inverts traditional RSI logic. Instead of treating overbought/oversold conditions as signals for reversal, it waits for those signals to fail. Only after such failures, confirmed by continued price action in the same direction, does the system enter trades. This logic is based on the behavioral observation that failed reversal signals often trigger stronger trend continuation—making this strategy uniquely positioned to exploit trap scenarios.
Disclaimer
This script is for educational and research purposes only. Trading involves risk, and past performance does not guarantee future results. Always test thoroughly before applying with live capital.
TLCproTLCpro Trading Strategy
Description
TLCpro is a multi-timeframe trend-following strategy that combines EMA crossovers, MACD filtering, RSI confirmation, and VWAP/Trend EMA as dynamic support/resistance levels. The strategy is optimized for 1-hour (1H) and 4-hour (4H) timeframes, ensuring adaptability to different market conditions.
Key Features
Dual EMA Crossover (Fast & Slow EMA) – Generates entry signals when the fast EMA crosses above/below the slow EMA.
MACD Filter – Confirms trend direction by requiring MACD histogram alignment with the trade direction.
RSI Filter – Avoids overbought/oversold conditions by enforcing RSI thresholds (default: RSI > 50 for long, RSI < 50 for short).
Trend Filter (4H Only) – Uses a 200-period EMA to ensure trades align with the broader trend.
VWAP Filter (1H Only) – Requires price to be above/below the daily VWAP for additional confirmation.
Smart Risk Management – Implements 3-tier take-profit (TP) levels and a trailing stop-loss (SL) that converts to breakeven (BE) after TP1 is hit.
How It Works
Entry Conditions
Long Entry:
Fast EMA (15) crosses above Slow EMA (30).
MACD histogram is positive.
RSI > 50 (configurable).
On 1H: Price above daily VWAP.
On 4H: Price above 200-period Trend EMA.
Short Entry:
Fast EMA (15) crosses below Slow EMA (30).
MACD histogram is negative.
RSI < 50 (configurable).
On 1H: Price below daily VWAP.
On 4H: Price below 200-period Trend EMA.
Exit & Risk Management
3 Take-Profit Levels (TP1, TP2, TP3) – Closes portions of the trade at predefined profit levels (default: 3%, 6%, 10%).
Dynamic Stop-Loss (SL) & Breakeven (BE) Logic:
Initial SL: Fixed at 3% from entry.
After TP1 is hit: SL moves to breakeven (entry price).
After TP2 is hit: SL moves to TP1 level, locking in partial profits.
Visual SL/TP Lines – Drawn on the chart for easy tracking.
Why TLCpro is Unique & Worth Using
Multi-Timeframe Adaptability: Uses different filters (VWAP for 1H, Trend EMA for 4H) to improve signal quality.
Smart Risk Management: Unlike static SL/TP strategies, TLCpro trails stops to lock in profits while minimizing risk.
High-Confirmation Filters: Combines EMA, MACD, RSI, and Trend/VWAP to reduce false signals.
Visual Clarity: Clearly marks SL, TP, and BE levels on the chart for intuitive trade management.
Backtesting & Risk Considerations
Realistic Risk per Trade: Default stop-loss is 3%, ensuring sustainable risk management.
Partial Profit-Taking: Exits 25% at TP1, 25% at TP2, and 50% at TP3, balancing risk and reward.
Commission & Slippage: Should be accounted for in live trading (adjust in strategy settings).
Recommended Capital: Works well with $1,000+ accounts due to percentage-based position sizing.
How to Use
Apply to 1H or 4H charts (optimized for these timeframes).
Default settings work well, but adjust EMA lengths, RSI thresholds, and TP/SL levels based on volatility.
Monitor SL/TP lines – The strategy auto-updates them as price moves.
Avoid over-optimization – Test on multiple instruments before live trading.
Final Notes
TLCpro is designed for swing traders and trend followers who want a systematic, rules-based approach with clear risk management. By combining multiple confirmation filters and dynamic stop adjustments, it aims to improve consistency in trending markets.
RSI Divergence Strategy - AliferCryptoStrategy Overview
The RSI Divergence Strategy is designed to identify potential reversals by detecting regular bullish and bearish divergences between price action and the Relative Strength Index (RSI). It automatically enters positions when a divergence is confirmed and manages risk with configurable stop-loss and take-profit levels.
Key Features
Automatic Divergence Detection: Scans for RSI pivot lows/highs vs. price pivots using user-defined lookback windows and bar ranges.
Dual SL/TP Methods:
- Swing-based: Stops placed a configurable percentage beyond the most recent swing high/low.
- ATR-based: Stops placed at a multiple of Average True Range, with a separate risk/reward multiplier.
Long and Short Entries: Buys on bullish divergences; sells short on bearish divergences.
Fully Customizable: Input groups for RSI, divergence, swing, ATR, and general SL/TP settings.
Visual Plotting: Marks divergences on chart and plots stop-loss (red) and take-profit (green) lines for active trades.
Alerts: Built-in alert conditions for both bullish and bearish RSI divergences.
Detailed Logic
RSI Calculation: Computes RSI of chosen source over a specified period.
Pivot Detection:
- Identifies RSI pivot lows/highs by scanning a lookback window to the left and right.
- Uses ta.barssince to ensure pivots are separated by a minimum/maximum number of bars.
Divergence Confirmation:
- Bullish: Price makes a lower low while RSI makes a higher low.
- Bearish: Price makes a higher high while RSI makes a lower high.
Entry:
- Opens a Long position when bullish divergence is true.
- Opens a Short position when bearish divergence is true.
Stop-Loss & Take-Profit:
- Swing Method: Computes the recent swing high/low then adjusts by a percentage margin.
- ATR Method: Uses the current ATR × multiplier applied to the entry price.
- Take-Profit: Calculated as entry price ± (risk × R/R ratio).
Exit Orders: Uses strategy.exit to place bracket orders (stop + limit) for both long and short positions.
Inputs and Configuration
RSI Settings: Length & price source for the RSI.
Divergence Settings: Pivot lookback parameters and valid bar ranges.
SL/TP Settings: Choice between Swing or ATR method.
Swing Settings: Swing lookback length, margin (%), and risk/reward ratio.
ATR Settings: ATR length, stop multiplier, and risk/reward ratio.
Usage Notes
Adjust the Pivot Lookback and Range values to suit the volatility and timeframe of your market.
Use higher ATR multipliers for wider stops in choppy conditions, or tighten swing margins in trending markets.
Backtest different R/R ratios to find the balance between win rate and reward.
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Trading carries significant risk and you may lose more than your initial investment. Always conduct your own research and consider consulting a professional before making any trading decisions.
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation
This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds.
1. Introduction
Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels.
2. Theoretical Foundation
2.1 Momentum Component
The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems.
2.2 Keltner Channels
Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978).
2.3 Stochastic Oscillator Principles
The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value.
3. Strategy Methodology
3.1 Entry and Exit Conditions
The strategy employs a contrarian approach within the channel framework:
Long Entry Condition:
Close price > Close price periods ago (momentum filter)
KeltnerStochastic < threshold (oversold within channel)
Short Entry Condition:
Close price < Close price periods ago (momentum filter)
KeltnerStochastic > threshold (overbought within channel)
Exit Conditions:
Exit long positions when KeltnerStochastic > threshold
Exit short positions when KeltnerStochastic < threshold
This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters.
3.2 Risk Management
Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated.
3.3 Dynamic Position Sizing
The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance:
$ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$
This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity.
4. Empirical Performance Analysis
Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics:
Enhanced performance during trending markets with moderate volatility
Reduced drawdowns during choppy market conditions through the dual-filter approach
Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008)
5. Strategy Limitations and Future Research
While effective in many market conditions, this strategy faces challenges during:
Rapid volatility expansion events where stop-loss mechanisms may be inadequate
Prolonged sideways markets with insufficient momentum
Markets with structural changes in volatility profiles
Future research should explore:
Adaptive threshold parameters based on regime detection
Integration with additional confirmatory indicators
Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016)
References
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons.
Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926.
Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service.
Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons.
Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group.
Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
TASC 2025.05 Trading The Channel█ OVERVIEW
This script implements channel-based trading strategies based on the concepts explained by Perry J. Kaufman in the article "A Test Of Three Approaches: Trading The Channel" from the May 2025 edition of TASC's Traders' Tips . The script explores three distinct trading methods for equities and futures using information from a linear regression channel. Each rule set corresponds to different market behaviors, offering flexibility for trend-following, breakout, and mean-reversion trading styles.
█ CONCEPTS
Linear regression
Linear regression is a model that estimates the relationship between a dependent variable and one or more independent variables by fitting a straight line to the observed data. In the context of financial time series, traders often use linear regression to estimate trends in price movements over time.
The slope of the linear regression line indicates the strength and direction of the price trend. For example, a larger positive slope indicates a stronger upward trend, and a larger negative slope indicates the opposite. Traders can look for shifts in the direction of a linear regression slope to identify potential trend trading signals, and they can analyze the magnitude of the slope to support trading decisions.
One caveat to linear regression is that most financial time series data does not follow a straight line, meaning a regression line cannot perfectly describe the relationships between values. Prices typically fluctuate around a regression line to some degree. As such, analysts often project ranges above and below regression lines, creating channels to model the expected extent of the data's variability. This strategy constructs a channel based on the method used in Kaufman's article. It measures the maximum distances from points on the linear regression line to historical price values, then adds those distances and the current slope to the regression points.
Depending on the trading style, traders might look for prices to move outside an established channel for breakout signals, or they might look for price action to reach extremes within the channel for potential mean reversion opportunities.
█ STRATEGY CALCULATIONS
Primary trade rules
This strategy implements three distinct sets of rules for trend, breakout, and mean-reversion trades based on the methods Kaufman describes in his article:
Trade the trend (Rule 1) : Open new positions when the sign of the slope changes, indicating a potential trend reversal. Close short trades and enter a long trade when the slope changes from negative to positive, and do the opposite when the slope changes from positive to negative.
Trade channel breakouts (Rule 2) : Open new positions when prices cross outside the linear regression channel for the current sample. Close short trades and enter a long trade when the price moves above the channel, and do the opposite when the price moves below the channel.
Trade within the channel (Rule 3) : Open new positions based on price values within the channel's range. Close short trades and enter a long trade when the price is near the channel's low, within a specified percentage of the channel's range, and do the opposite when the price is near the channel's high. With this rule, users can also filter the trades based on the channel's slope. When the filter is active, long positions are allowed only when the slope is positive, and short positions are allowed only when it is negative.
Position sizing
Kaufman's strategy uses specific trade sizes for equities and futures markets:
For an equities symbol, the number of shares traded is $10,000 divided by the current price.
For a futures symbol, the number of contracts traded is based on a volatility-adjusted formula that divides $25,000 by the product of the 20-bar average true range and the instrument's point value.
By default, this script automatically uses these sizes for its trade simulation on equities and futures symbols and does not simulate trading on other symbols. However, users can control position sizes from the "Settings/Properties" tab and enable trade simulation on other symbol types by selecting the "Manual" option in the script's "Position sizing" input.
Stop-loss
This strategy includes the option to place an accompanying stop-loss order for each trade, which users can enable from the "SL %" input in the "Settings/Inputs" tab. When enabled, the strategy places a stop-loss order at a specified percentage distance from the closing price where the entry order occurs, allowing users to compare how the strategy performs with added loss protection.
█ USAGE
This strategy adapts its display logic for the three trading approaches based on the rule selected in the "Trade rule" input:
For all rules, the script plots the linear regression slope in a separate pane. The plot is color-coded to indicate whether the current slope is positive or negative.
When the selected rule is "Trade the trend", the script plots triangles in the separate pane to indicate when the slope's direction changes from positive to negative or vice versa. Additionally, it plots a color-coded SMA on the main chart pane, allowing visual comparison of the slope to directional changes in a moving average.
When the rule is "Trade channel breakouts" or "Trade within the channel", the script draws the current period's linear regression channel on the main chart pane, and it plots bands representing the history of the channel values from the specified start time onward.
When the rule is "Trade within the channel", the script plots overbought and oversold zones between the bands based on a user-specified percentage of the channel range to indicate the value ranges where new trades are allowed.
Users can customize the strategy's calculations with the following additional inputs in the "Settings/Inputs" tab:
Start date : Sets the date and time when the strategy begins simulating trades. The script marks the specified point on the chart with a gray vertical line. The plots for rules 2 and 3 display the bands and trading zones from this point onward.
Period : Specifies the number of bars in the linear regression channel calculation. The default is 40.
Linreg source : Specifies the source series from which to calculate the linear regression values. The default is "close".
Range source : Specifies whether the script uses the distances from the linear regression line to closing prices or high and low prices to determine the channel's upper and lower ranges for rules 2 and 3. The default is "close".
Zone % : The percentage of the channel's overall range to use for trading zones with rule 3. The default is 20, meaning the width of the upper and lower zones is 20% of the range.
SL% : If the checkbox is selected, the strategy adds a stop-loss to each trade at the specified percentage distance away from the closing price where the entry order occurs. The checkbox is deselected by default, and the default percentage value is 5.
Position sizing : Determines whether the strategy uses Kaufman's predefined trade sizes ("Auto") or allows user-defined sizes from the "Settings/Properties" tab ("Manual"). The default is "Auto".
Long trades only : If selected, the strategy does not allow short positions. It is deselected by default.
Trend filter : If selected, the strategy filters positions for rule 3 based on the linear regression slope, allowing long positions only when the slope is positive and short positions only when the slope is negative. It is deselected by default.
NOTE: Because of this strategy's trading rules, the simulated results for a specific symbol or channel configuration might have significantly fewer than 100 trades. For meaningful results, we recommend adjusting the start date and other parameters to achieve a reasonable number of closed trades for analysis.
Additionally, this strategy does not specify commission and slippage amounts by default, because these values can vary across market types. Therefore, we recommend setting realistic values for these properties in the "Cost simulation" section of the "Settings/Properties" tab.
Fibonacci Counter-Trend TradingOverview:
The Fibonacci Counter-Trend Trading strategy is designed to capitalize on price reversals by utilizing Fibonacci levels calculated from the standard deviation of price movements. This strategy opens a sell order when the closing price crosses above a specified upper Fibonacci level and a buy order when the closing price crosses below a specified lower Fibonacci level. By leveraging the principles of Fibonacci retracement and volatility, this strategy aims to identify potential reversal points in the market.
How It Works:
Fibonacci Levels Calculation:
The strategy calculates upper and lower Fibonacci levels based on the standard deviation of the price over a specified moving average length. These levels are derived from the Fibonacci sequence, which is widely used in technical analysis to identify potential support and resistance levels.
The upper levels are calculated by adding specific Fibonacci ratios (0.236, 0.382, 0.5, 0.618, 0.764, and 1.0) multiplied by the standard deviation to the basis (the volume-weighted moving average).
The lower levels are calculated by subtracting the same Fibonacci ratios multiplied by the standard deviation from the basis.
Trade Entry Rules:
Sell Order: A sell order is triggered when the closing price crosses above the selected upper Fibonacci level. This indicates a potential reversal point where the price may start to decline.
Buy Order: A buy order is initiated when the closing price crosses below the selected lower Fibonacci level. This suggests a potential reversal point where the price may begin to rise.
Trade Management:
The strategy includes stop-losses based on the Fibonacci levels to protect against adverse price movements.
How to Use:
Users can customize the moving average length and the multiplier for the standard deviation to suit their trading preferences and market conditions.
The strategy can be applied to various financial instruments, including stocks, forex, and cryptocurrencies, making it versatile for different trading environments.
Pros:
The Fibonacci Counter-Trend Trading strategy combines the mathematical principles of the Fibonacci sequence with the statistical measure of standard deviation, providing a unique approach to identifying potential market reversals.
This strategy is particularly useful in volatile markets where price swings can lead to significant trading opportunities.
The use of Fibonacci levels can help traders identify key support and resistance areas, enhancing decision-making.
Cons:
The strategy may generate false signals in choppy or sideways markets, leading to potential losses if the price does not reverse as anticipated.
Relying solely on Fibonacci levels without considering other technical indicators or market conditions may result in missed opportunities or increased risk.
The effectiveness of the strategy can vary depending on the chosen parameters (e.g., moving average length and standard deviation multiplier), requiring users to spend time optimizing these settings for different market conditions.
As with any counter-trend strategy, there is a risk of significant drawdowns during strong trending markets, where the price continues to move in one direction without reversing.
By understanding the mechanics of the Fibonacci Counter-Trend Trading strategy, along with its pros and cons, traders can effectively implement it in their trading routines and potentially enhance their trading performance.
