VIDYA Trend StrategyOne of the most common messages I get is people reaching out asking for quantitative strategies that trade cryptocurrency. This has compelled me to write this script and article, to help provide a quantitative/technical perspective on why I believe most strategies people write for crypto fail catastrophically, and how one might build measures within their strategies that help reduce the risk of that happening. For those that don't trade crypto, know that these approaches are applicable to any market.
I will start off by qualifying up that I mainly trade stocks and ETFs, and I believe that if you trade crypto, you should only be playing with money you are okay with losing. Most published crypto strategies I have seen "work" when the market is going up, and fail catastrophically when it is not. There are far more people trying to sell you a strategy than there are people providing 5-10+ year backtest results on their strategies, with slippage and commissions included, showing how they generated alpha and beat buy/hold. I understand that this community has some really talented people that can create some really awesome things, but I am saying that the vast majority of what you find on the internet will not be strategies that create alpha over the long term.
So, why do so many of these strategies fail?
There is an assumption many people make that cryptocurrency will act just like stocks and ETFs, and it does not. ETF returns have more of a Gaussian probability distribution. Because of this, ETFs have a short term mean reverting behavior that can be capitalized on consistently. Many technical indicators are built to take advantage of this on the equities market. Many people apply them to crypto. Many of those people are drawn down 60-70% right now while there are mean reversion strategies up YTD on equities, even though the equities market is down. Crypto has many more "tail events" that occur 3-4+ standard deviations from the mean.
There is a correlation in many equities and ETF markets for how long an asset continues to do well when it is currently doing well. This is known as momentum, and that correlation and time-horizon is different for different assets. Many technical indicators are built based on this behavior, and then people apply them to cryptocurrency with little risk management assuming they behave the same and and on the same time horizon, without pulling in the statistics to verify if that is actually the case. They do not.
People do not take into account the brokerage commissions and slippage. Brokerage commissions are particularly high with cryptocurrency. The irony here isn't lost to me. When you factor in trading costs, it blows up most short-term trading strategies that might otherwise look profitable.
There is an assumption that it will "always come back" and that you "HODL" through the crash and "buy more." This is why Three Arrows Capital, a $10 billion dollar crypto hedge fund is now in bankruptcy, and no one can find the owners. This is also why many that trade crypto are drawn down 60-70% right now. There are bad risk practices in place, like thinking the martingale gambling strategy is the same as dollar cost averaging while also using those terms interchangeably. They are not the same. The 1st will blow up your trade account, and the 2nd will reduce timing risk. Many people are systematically blowing up their trade accounts/strategies by using martingale and calling it dollar cost averaging. The more risk you are exposing yourself too, the more important your risk management strategy is.
There is an odd assumption some have that you can buy anything and win with technical/quantitative analysis. Technical analysis does not tell you what you should buy, it just tells you when. If you are running a strategy that is going long on an asset that lost 80% of its value in the last year, then your strategy is probably down. That same strategy might be up on a different asset. One might consider a different methodology on choosing assets to trade.
Lastly, most strategies are over-fit, or curve-fit. The more complicated and more parameters/settings you have in your model, the more likely it is just fit to historical data and will not perform similar in live trading. This is one of the reasons why I like simple models with few parameters. They are less likely to be over-fit to historical data. If the strategy only works with 1 set of parameters, and there isn't a range of parameters around it that create alpha, then your strategy is over-fit and is probably not suitable for live trading.
So, what can I do about all of this!?
I created the VIDYA Trend Strategy to provide an example of how one might create a basic model with a basic risk management strategy that might generate long term alpha on a volatile asset, like cryptocurrency. This is one (of many) risk management strategies that can reduce the volatility of your returns when trading any asset. I chose the Variable Index Dynamic Average (VIDYA) for this example because it's calculation filters out some market noise by taking into account the volatility of the underlying asset. I chose a trend following strategy because regressions are capturing behaviors that are not just specific to the equities market.
The more volatile an asset, the more you have to back-off the short term price movement to effectively trend-follow it. Otherwise, you are constantly buying into short term trends that don't represent the trend of the asset, then they reverse and loose money. This is why I am applying a trend following strategy to a 4 hour chart and not a 4 minute chart. It is also important to note that following these long term trends on a volatile asset exposes you to additional risk. So, how might one mitigate some of that risk?
One of the ways of reducing timing risk is scaling into a trade. This is different from "doubling down" or "trippling down." It is really a basic application of dollar cost averaging to reduce timing risk, although DCA would typically happen over a longer time period. If it is really a trend you are following, it will probably still be a trend tomorrow. Trend following strategies have lower win rates because the beginning of a trend often reverses. The more volatile the asset, the more likely that is to happen. However, we can reduce risk of buying into a reversal by slowly scaling into the trend with a small % of equity per trade.
Our example "VIDYA Trend Strategy" executes this by looking at a medium-term, volatility adjusted trend on a 4 hour chart. The script scales into it with 4% of the account equity every 4-hours that the trend is still up. This means you become fully invested after 25 trades/bars. It also means that early in the trade, when you might be more likely to experience a reversal, most of your account equity is not invested and those losses are much smaller. The script sells 100% of the position when it detects a trend reversal. The slower you scale into a trade, the less volatile your equity curve will be. This model also includes slippage and commissions that you can adjust under the "settings" menu.
This fundamental concept of reducing timing risk by scaling into a trade can be applied to any market.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Strategy
Strategy BackTest Display Statistics - TraderHalaiThis script was born out of my quest to be able to display strategy back test statistics on charts to allow for easier backtesting on devices that do not natively support backtest engine (such as mobile phones, when I am backtesting from away from my computer). There are already a few good ones on TradingView, but most / many are too complicated for my needs.
Found an excellent display backtest engine by 'The Art of Trading'. This script is a snippet of his hard work, with some very minor tweaks and changes. Much respect to the original author.
Full credit to the original author of this script. It can be found here: www.tradingview.com
I decided to modify the script by simplifying it down and make it easier to integrate into existing strategies, using simple copy and paste, by relying on existing tradingview strategy backtester inputs. I have also added 3 additional performance metrics:
- Max Run Up
- Average Win per trade
- Average Loss per trade
As this is a work in progress, I will look to add in more performance metrics in future, as I further develop this script.
Feel free to use this display panel in your scripts and strategies.
Thanks and enjoy :)
Smoothed Heikin Ashi Trend on Chart - TraderHalai BACKTESTSmoothed Heikin Ashi Trend on chart - Backtest
This is a backtest of the Smoothed Heikin Ashi Trend indicator, which computes the reverse candle close price required to flip a Heikin Ashi trend from red to green and vice versa. The original indicator can be found in the scripts section of my profile.
This particular back test uses this indicator with a Trend following paradigm with a percentage-based stop loss.
Note, that backtesting performance is not always indicative of future performance, but it does provide some basis for further development and walk-forward / live testing.
Testing was performed on Bitcoin , as this is a primary target market for me to use this kind of strategy.
Sample Backtesting results as of 10th June 2022:
Backtesting parameters:
Position size: 10% of equity
Long stop: 1% below entry
Short stop: 1% above entry
Repainting: Off
Smoothing: SMA
Period: 10
8 Hour:
Number of Trades: 1046
Gross Return: 249.27 %
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 28.01 %
Profit Factor (Expectancy): 2.019
Average Loss: 0.33 %
Average Win: 1.69 %
Average Time for Loss: 1 day
Average Time for Win: 5.33 days
1 Day:
Number of Trades: 429
Gross Return: 458.4 %
CAGR Return: 15.76 %
Max Drawdown: 6.37 %
Profit Factor (Expectancy): 2.804
Average Loss: 0.8 %
Average Win: 7.2 %
Average Time for Loss: 3 days
Average Time for Win: 16 days
5 Day:
Number of Trades: 69
Gross Return: 1614.9 %
CAGR Return: 26.7 %
Max Drawdown: 5.7 %
Profit Factor (Expectancy): 10.451
Average Loss: 3.64 %
Average Win: 81.17 %
Average Time for Loss: 15 days
Average Time for Win: 85 days
Analysis:
The strategy is typical amongst trend following strategies with a less regular win rate, but where profits are more significant than losses. Most of the losses are in sideways, low volatility markets. This strategy performs better on higher timeframes, where it shows a positive expectancy of the strategy.
The average win was positively impacted by Bitcoin’s earlier smaller market cap, as the percentage wins earlier were higher.
Overall the strategy shows potential for further development and may be suitable for walk-forward testing and out of sample analysis to be considered for a demo trading account.
Note in an actual trading setup, you may wish to use this with volatility filters, combined with support resistance zones for a better setup.
As always, this post/indicator/strategy is not financial advice, and please do your due diligence before trading this live.
Original indicator links:
On chart version -
Oscillator version -
Update - 27/06/2022
Unfortunately, It appears that the original script had been taken down due to auto-moderation because of concerns with no slippage / commission. I have since adjusted the backtest, and re-uploaded to include the following to address these concerns, and show that I am genuinely trying to give back to the community and not mislead anyone:
1) Include commission of 0.1% - to match Binance's maker fees prior to moving to a fee-less model.
2) Include slippage of 10 ticks (This is a realistic slippage figure from searching online for most crypto exchanges)
3) Adjust account balance to 10,000 - since most of us are not millionaires.
The rest of the backtesting parameters are comparable to previous results:
Backtesting parameters:
Initial capital: 10000 dollars
Position size: 10% of equity
Long stop: 2% below entry
Short stop: 2% above entry
Repainting: Off
Smoothing: SMA
Period: 10
Slippage: 10 ticks
Commission: 0.1%
This script still remains to shows viability / profitablity on higher term timeframes (with slightly higher drawdown), and I have included the backtest report below to document my findings:
8 Hour:
Number of Trades: 1082
Gross Return: 233.02%
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 25.6%
Profit Factor (Expectancy): 1.627
Average Loss: 0.46 %
Average Win: 2.18 %
Average Time for Loss: 1.33 day
Average Time for Win: 7.33 days
Once again, please do your own research and due dillegence before trading this live. This post is for education and information purposes only, and should not be taken as financial advice.
3ngine Global BoilerplateABOUT THE BOILERPLATE
This strategy is designed to bring consistency to your strategies. It includes a macro EMA filter for filtering out countertrend trades,
an ADX filter to help filter out chop, a session filter to filter out trades outside of desired timeframe, alert messages setup for automation,
laddering in/out of trades (up to 6 rungs), trailing take profit , and beautiful visuals for each entry. There are comments throughout the
strategy that provide further instructions on how to use the boilerplate strategy. This strategy uses `threengine_global_automation_library`
throughout and must be included at the top of the strategy using `import as bot`. This allows you to use dot notation
to access functions in the library - EX: `bot.orderCurrentlyExists(orderID)`.
HOW TO USE THIS STRATEGY
1. Add your inputs
There is a section dedicated for adding your own inputs near the top of the strategy, just above the boilerplate inputs
2. Add your calculations
If your strategy requires calculations, place them in the `Strategy Specific Calculations` section
3. Add your entry criteria
Add your criteria to strategySpecificLongConditions (this gets combined with boilerplate conditions in longConditionsMet)
Add your criteria to strategySpecificShortConditions (this gets combined with boilerplate conditions in shortConditionsMet)
Set your desired entry price (calculated on every bar unless stored as a static variable) to longEntryPrice and shortEntryPrice. ( This will be the FIRST ladder if using laddering capabilities. If you pick 1 for "Ladder In Rungs" this will be the only entry. )
4. Plot anything you want to overlay on the chart in addition to the boilerplate plots and labels. Included in boilerplate:
Average entry price
Stop loss
Trailing stop
Profit target
Ladder rungs
Swing Failure Reversal StrategyThis strategy is using Swing Failure Patterns as a reversion indicator.
The strategy automatically adapts itself to the timeframe of the current chart.
Swing Failure Pattern occurs when the price trend fails to set new highs in uptrend or meet new lows in a downtrend. This pattern helps traders decide when to enter and exit the market. Usually, traders enter in the downtrend i.e. lower price highs and lower price lows, and exit in the uptrend situation i.e. higher price highs and higher price lows. Thus, traders go against the current trend. This helps the traders take advantage of early trend reversal indicators.
Types of Failure Swing :
Failure Swing Top: This occurs when the stock price goes higher whereas the RSI fails to make a higher high and falls below the recent fail point. The Fail Point is where the RSI line is below the recent swing low. This Failure Swing indicates a short position.
Failure Swing Bottom: This occurs when the stock price gets lower whereas RSI fails to make a lower low and rises over the recent fail point. Fail point is the point where the RSI line is above the recent swing high. This Failure Swing indicates a long position.
Buy and hold strategyA simple buy and hold strategy. A short or a long position can be chosen. The start date will determine the date where your position will start and end date is the date it will end. This works well as a baseline to your other existing strategies since buy and hold is just the simplest strategy available.
Gap Reversion StrategyToday I am releasing to the community an original short-term, high-probability gap trading strategy, backed by a 20 year backtest. This strategy capitalizes on the mean reverting behavior of equity ETFs, which is largely driven by fear in the market. The strategy buys into that fear at a level that has historically mean reverted within ~5 days. Larry Connors has published useful research and variations of strategies based on this behavior that I would recommend any quantitative trader read.
What it does:
This strategy, for 1 day charts on equity ETFs, looks for an overnight gap down when the RSI is also in/near an oversold position. Then, it places a limit order further below the opening of the gapped-down day. It then exits the position based on a higher RSI level. The limit buy order is cancelled if the price doesn't reach your limit price that day. So, the larger you make the gap and limit %, the less signals you will have.
Features:
Inputs to allow the adjustment of the limit order %, the gap %, and the RSI entry/exit levels.
An option to have the limit order be based on a % of ATR instead of a % of asset price.
An optional filter that can turn-off trades when the VIX is unusually high.
A built in stop.
Built in alerts.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
SuperIchi StrategyTRADE CONDITIONS
Long entry:
Tenkan-Sen is above Kijun-Sen (blue line above red line)
Price closes above both Tenkan-Sen and Kijun-Sen (price closes above both blue and red lines)
Tenkan-Sen and Kijun-Sen is above Senkou Span (both blue and red lines are above cloud)
Senkou Span is green (cloud is green)
Price pulled back and closed below both Tenkan-Sen and Kijun-Sen within last X (configurable in settings) candles (price pulled back below blue and red lines)
Short entry:
Tenkan-Sen is below Kijun-Sen (blue line below red line)
Price closes below both Tenkan-Sen and Kijun-Sen (price closes below both blue and red lines)
Tenkan-Sen and Kijun-Sen is below Senkou Span (both blue and red lines are below cloud)
Senkou Span is red (cloud is red)
Price pulled back and closed above both Tenkan-Sen and Kijun-Sen within last X (configurable in settings) candles (price pulled back above blue and red lines)
Risk management:
Each trade risks 2% of account (configurable in settings)
SL size determined by swing low/high of previous X candles (configurable in settings) or using the ATR override (configurable in settings) where the max of swing high/low or ATR value will be used to calculate SL
TP is calculated by Risk:Reward ratio (configurable in settings)
TIPS
Timeframe: I have found best results running on anything 5M and above
CREDITS
SuperIchi by LuxAlgo
Pinbar trailing stop strategyThe strategy finds the nearest pinbar pattern and opens a position (long or short). You choose your take profit and stop loss multiplier.
Take Profit - X times the pinbar size from it's highest point.
Stop loss - X times the pinbar size from it's lowest point.
You can find more detailed screenshots and the source-code on my github page: samgozman/pinbar-strategy-tradingview
Bollinger Bands + EMA 9A 1 minute scalping strategy.
Uses Bollinger Bands (no basis line) and a 9 period EMA.
Waits for price to close below the lower Bollinger Band and the next candle to close bullish above the lower Bollinger Band but below the 9 Period EMA.
If all conditions are met, the script enters a long position with TP at the 9 Period EMA.
Boom Hunter + Hull Suite + Volatility Oscillator StrategyTRADE CONDITIONS
Long entry:
Boom Hunter (leading indicator): Trigger line crosses over Quotient 2 line (white cross over red)
Hull Suite (trend confirmation): Price closed above hull suite line and hull suite is green (represented by horizontal line at -10 in strategy pane)
Volatility Oscillator (volatility confirmation): Volatility spike trigger line is above upper band (represented by horizontal line at -30 in strategy pane)
Short entry:
Boom Hunter (leading indicator): Trigger line crosses under Quotient 2 line (white cross under red)
Hull Suite (trend confirmation): Price closed below hull suite line and hull suite is red (represented by horizontal line at -10 in strategy pane)
Volatility Oscillator (volatility confirmation): Volatility spike trigger line is below lower band (represented by horizontal line at -30 in strategy pane)
Risk management:
Each trade risks 3% of account (configurable in settings)
SL size determined by swing low/high of previous X candles (configurable in settings) or 1 ATR if swing is less than 1 ATR
TP is calculated by Risk:Reward ratio (configurable in settings)
TIPS
Timeframe: I have found good results running on BTC/USDT 5M chart
Note: To help visual identification of trade entries and exits you may wish to add the Hull Suite and Volatility Oscillator to the chart separately. It was not possible to display them in a clear way within a single panel for the strategy. Make sure you set the settings of the auxiliary indicators to match what is in the settings of this indicator if you do decide to add them.
CREDITS
Boom Hunter Pro by veryfid
Hull Suite by InSilico
Volatility Oscillator by veryfid
Nabz-BBMACD-2022-V1.1I have tried to make script which triggers indicators on combination of different feedback including Bollinger bands and MACD. Also used some of my logic by trial and error, It gave 744%+ profit on back-testing on coin RUNE/USDT from Jan 2021. It is my first script, I am happy to help the community. Please share your feedback.
Parabolic SAR Heikin Ashi MTF Candle ScalperThis is scalper strategy designed around parabolic sar indicator, where as an input candle value it uses the heikinashi from a higher timeframe.
This example has been adapted to SPY/SPX chart
In this case ,we are using a 5 min chart, but the calculations are made on a 15 min heikin ashi chart for the PSAR and then on 5 min chart we plot the results.
At the same time we are conditioning the entry to be base on a time/session for daytrading/scalper mentality
In this case we only enter within the first 30 min of SPY opening session , and then we exit after 3-4 hours of staying in the position ( unless we hit a reverse condition).
For long condition we enter when the mtf ha candle close is above the mtf psar and for short condition we enter when the mtf ha candle close is below the mtf psar
This script is made with an educational purpose to show the power of multiple time frame approach compared to a single chart.
If you have any questions, let me know !
Hull Suite + Stoch RSI Strategy v1.1 This strategy uses Hull Suite with Stoch RSI
Uses Hull Suite as trend and only trades with the direction of the trend.
Entry conditions:
Hull Suite as a trend
Stoch RSI overbought for short entries & oversold for long entries
Current parameters works best on BINANCE:BNBBUSDPERP pair.
QQE MOD + SSL Hybrid + Waddah Attar ExplosionTRADE CONDITIONS
Long entry:
QQE Mod changes to Blue (leading indicator)
SSL Hybrid is Blue and price is above MA Channel line
Waddah Attar Explosion is Green and above Explosion line
Short entry:
QQE Mod changes to Red (leading indicator)
SSL Hybrid is Red and price is below MA Channel line
Waddah Attar Explosion is Red and above Explosion line
Risk management:
Each trade risks 2% of account (configurable in settings)
SL size determined by swing low/high of previous X candles (configurable in settings)
TP is triggered on SSL Hybrid EXIT arrow signals
TIPS
Timeframe: Personally I've found best results running this on 1H timeframe.
Note: To help visual identification of trade entries and exits you may wish to add the SSL Hybrid and Waddah Attar Explosion to the chart separately. They are being used to determine trade entry/exit within the code of this strategy but it was not possible to display them in a clear way within a single panel. Make sure you set the settings of the auxiliary indicators to match what is in the settings of this indicator if you do decide to add them.
CREDITS
QQE MOD byMihkel00
SSL Hybrid by Mihkel00
Waddah Attar Explosion by shayankm
BTC WaveTrend R:R=1:1.5In this strategy, I used Wavetrend indicator (Lazy Bear).
It is very simple and easy to understanding: Long when Wavetrend1 crossover Wavetrend2 and they are less than a limit value (not buy when price overbought). Stoploss at lowest 3 bar previous. R:R = 1:1,5.
About other shortterm strategies for crypto market, you can view my published strategies.
Scalping The Bull - Two EMA StrategyName: Scalping The Bull - Two EMA "Gianno-Nano" Strategy from the Meeting
Category: Trend Follower
Operating mode: Spot or Future, only long or swing trading
Trades duration: Multiday
Timeframe: 4H
Suggested usage: Mid-term trading, when the market is in trend and it is showing high volatility.
Entry: When fast EMA crosses over slow EMA.
Exit: When fast EMA crosses under slow EMA then Exit Long or Entry Short (for reversal strategy).
Usage:
⁃ It can be useful to use alerts or web-hooks to automate this strategy.
⁃ This is a raw system that can be improved in different ways (e.g. Stop-loss, take-profit, position sizing) or studying more the behaviour of the coin.
Configuration:
- N/A
Backtesting
⁃ Exchange: BINANCE
⁃ Pair: NEOUSDT
⁃ Timeframe: 4H
⁃ Fee 0.075%
⁃ Slippage 0
- Start : 2017-12-03
How you or we can improve? Source code is open so share your ideas!
Take profit Multi timeframeRepublish:
Take profit Multi timeframe:
In this scipts, I build risk-reward system managemant. You can take profit in two way: percent or at resistant in higher timeframe or both.
Strategy in this scripts, I use Wave trend indicator as example strategy.
Estrategia Larry Connors [JoseMetal]============
ENGLISH
============
- Description:
This strategy is based on the original Larry Connors strategy, using 2 SMAs and RSI.
The strategy has been optimized for better total profit and works better on 4H (tested on BTCUSDT).
LONG:
Price must be ABOVE the slow SMA.
When a candle closes in RSI oversold area, the next candle closes out of the oversold area and the closing price is BELOW the fast SMA = open LONG.
LONG is closed when a candle closes ABOVE the fast SMA.
SHORT:
Price must be BELOW the slow SMA.
When a candle closes in RSI overbought area, the next candle closes out of the overbought area and the closing price is ABOVE the fast SMA = open SHORT.
SHORT is closed when a candle closes BELOW the fast SMA.
*Larry Connor's strategy does NOT use a fixed Stop Loss or Take Profit, as he said, that reduces performance significantly.
- Visual:
Both SMAs (fast and slow) are shown in the chart.
By default, the fast SMA is aqua color, the slow changes between green and red depending on the "trend" (price over slow SMA = bullish, below = bearish).
RSI can't be shown because TradingView doesn't allow to show both overlay and panel indicators, so candles get a RED color when RSI is in OVERBOUGHT area and GREEN when they're on OVERSOLD area to help with that.
Background is colored when conditions are met and a position is going to be open, green for LONGs red for SHORTs.
- Usage and recommendations:
As this is a coded strategy, you don't even have to check for indicators, just open and close trades as the strategy shows.
The original strategy uses a 5 period SMA instead of the 10, and 10/90 for oversold/overbought levels, this has been optimized after the testings and results but feel free to change settings and test by yourself.
Also, the original strategy was developed for daily, but seems to work better en 4H.
- Customization:
As usual I like to make as many aspects of my indicators/strategies customizable, indicators, colors etc., feel free to ask if you feel that something that should be configurable is missing or if you have any ideas to optimize the strategy.
============
ESPAÑOL
============
- Descripción:
Esta estrategia está basada en la estrategia original de Larry Connors, utilizando 2 SMAs y RSI.
La estrategia ha sido optimizada para un mejor beneficio total y funciona mejor en 4H (probado en BTCUSDT).
LONG:
El precio debe estar por encima de la SMA lenta.
Cuando una vela cierra en la zona de sobreventa del RSI, la siguiente vela cierra fuera de la zona de sobreventa y el precio de cierre está POR DEBAJO de la SMA rápida = abre LONG.
Se cierra cuando una vela cierra POR ENCIMA de la SMA rápida.
SHORT:
El precio debe estar POR DEBAJO de la SMA lenta.
Cuando una vela cierra en la zona de sobrecompra del RSI, la siguiente vela cierra fuera de la zona de sobrecompra y el precio de cierre está POR ENCIMA de la SMA rápida = abre SHORT.
Se cierra cuando una vela cierra POR DEBAJO de la SMA rápida.
*La estrategia de Larry Connor NO utiliza un Stop Loss o Take Profit fijo, como él dijo, eso reduce el rendimiento significativamente.
- Visual:
Ambas SMAs (rápida y lenta) se muestran en el gráfico.
Por defecto, la SMA rápida es de color aqua, la lenta cambia entre verde y rojo dependiendo de la "tendencia" (precio por encima de la SMA lenta = alcista, por debajo = bajista).
El RSI no puede mostrarse porque TradingView no permite mostrar tanto los indicadores superpuestos como los del panel, así que las velas obtienen un color ROJO cuando el RSI está en el área de SOBRECOMPRA y VERDE cuando están en el área de VENTA para ayudar a ello.
El fondo se colorea cuando se cumplen las condiciones y se va a abrir una posición, verde para LONGs rojo para SHORTs.
- Uso y recomendaciones:
Como se trata de una estrategia ya programada, ni siquiera hay que comprobar los indicadores, sólo hay que abrir y cerrar las operaciones tal y como muestra la estrategia en el gráfico.
La estrategia original utiliza una SMA de 5 periodos en lugar de 10, y 10/90 para los niveles de sobreventa/sobrecompra, esto ha sido optimizado después de las pruebas y los resultados, pero sé libre de cambiar la configuración y probarla por sí mismo.
Además, la estrategia original fue desarrollada para diario, pero parece funcionar mejor en 4H.
- Personalización:
Como siempre me gusta hacer personalizables todos los aspectos de mis indicadores/estrategias, indicadores, colores, etc., preguntar si notas que falta algo que debería ser configurable o si tienes alguna idea para optimizar la estrategia.
MTF RSI & STOCH Strategy by kziThis script is a teaml job with Indicator-Johns.
First he used my script, then i transform his code.
The origine:
The first transformation:
www.tradingview.com
Funny moment together, thanks for that. :)
This sharing is an indicator where you can see the average of different time frames.
The RSI is the blue line
The Stock is the yellow line
You can manage the timeframe in the parameters.
The strategy is to take position when the two lines get overbought or oversold and close when the stoch and RSI goes to the middle.
GRID SPOT TRADING ALGORITHM - GRID BOT TRADING STRATEGYGRID SPOT TRADING ALGORITHM : LONG ONLY STRATEGY OPEN SOURCE
This is a long only strategy for spot assets.
HOW IT WORKS
Grid trading is a trading strategy where an investor creates a so-called "price grid". The basic idea of the strategy is to repeatedly buy at the pre-specified price and then wait for the price to rise above that level and then sell the position (and vice versa with shorting or hedging).
FEATURES
Grids: This algorithm has a total of 10 grids.
Take profit: The trader can increase or decrease the distance between the grids from the User Interface panel, the distance between one grid and another represents the take profit.
Management: The algorithm buys 10% of the capital every time the price breaks down a grid and sells during a rise to the next higher grid. The initial capital is invested in 10 sizes which represent 10% of the capital per trade.
Stop Loss: The algorithm knows no stop loss as long as it is not activated from the User Interface panel. By activating the stop loss from the User Interface panel the algorithm will insert a close condition on all trades which will be calculated from the last lower grid.
Trades: Trades are opened only if the price is within the grid. If the market leaves the grid the algorithm will not buy new positions or sell new positions.
Optimal market conditions: The favorable market for this algorithm is the sideways market.
LIMITATIONS OF THE MODEL
The trader must take into account that this is a static model. It only works perfectly well if the market is in a sideways phase and incurs heavy losses if the market takes a downward trend. The model is unusable for an uptrend. The trader must therefore carefully analyze the market where he intends to use this strategy, making sure that the price is in a sideways phase.
USES
Indispensable research and backtesting tool for those using bots for their investments. The algorithm produces a backtesting of the strategy for past history. It is used by professional traders to understand if this strategy has been profitable on a market and what parameters to use for bots using this strategy (Kucoin, Binance etc.).
If you would like to develop your own algorithm with customized conditions based on a grid strategy, please contact us.
If you need help in using this tool, please contact us without hesitation.
Saturday Strategy BTC By KziI take the hypothèse that saturday is the most stable day of the week because, no SP500, no fed announcement and no weekly closure.
My Strategy is very simple:
Take the friday color (Red = Short // Green = Long)
Then open at the friday close price
Take a small pourcentage (1 or 2 %) then close.
What ever close on midnight.
Work Well on the 1h chart.
The Yellow is the saturday.
Maybe we can add the monthly close information to avoid opening trade on saturday if we are at this event.
Enjoy and give me your comment.
Kzi
