Initial Balance Panel Strategy for BitcoinInitial Balance Strategy
Initial Balance Strategy uses a source code of "Initial Balance Monitoring Panel" that build from "Initial Balance Markets Time Zones - Overall Highest and Lowest".
Initial Balance is based on the highest and lowest price action within the first 60 minutes of trading. Reading online this can depict which way the market can trend for the session. More information about Initial Balance Panel you can read at the end of the article.
Strategy idea
The main idea is to catch the trend move when most of the 16 Crypto pairs break the Low or High levels together. I found good results when 15 of 16 pairs is break that levels and after we manage the trade within some trail stop indicator, I choose Volatility Stop for this strategy.
Additional Strategy idea
The second one idea that was not made is to catch the pullback after fully green/red zones in Initial Balance Panel become white. That mean the main trend can be finished and we can try to catch good pullback in opposite direction.
Binance Crypto pairs
The strategy use the 16 default Crypto currencies pairs from the Binance. As additional variations of the strategy can be changing the currencies pairs and their number.
List of default pairs:
BINANCE:BTCUSDT, BINANCE:ETHUSDT, BINANCE:EOSUSDT, BINANCE:LTCUSDT, BINANCE:XRPUSDT, BINANCE:DASHUSDT, BINANCE:IOTAUSDT, BINANCE:NEOUSDT, BINANCE:QTUMUSDT, BINANCE:XMRUSDT, BINANCE:ZECUSDT, BINANCE:ETCUSDT, BINANCE:ADAUSDT, BINANCE:XTZUSDT, BINANCE:LINKUSDT, BINANCE:DOTUSDT
Summary
The strategy works very well for a buy trades with settings 15 crypto pairs of 16 that follow the trend with breaking the long initial balance level.
Initial Balance Monitoring Panel
Allows you to have an instant view of 16 Crypto pairs within a monitoring panel, monitoring Initial Balance (Asia, London, New York Stock Exchanges).
The code can easily be changed to suit the crypto pairs you are trading.
The setup of my chart would also include this indicator and the "Initial Balance Markets Time Zones - Overall Highest and Lowest" (with all IBs enabled) as shown above.
Initial Balance is based on the highest and lowest price action within the first 60 minutes of trading. Reading online this can depict which way the market can trend for the session.
The indicator has been coded for Crypto (so other symbols may not work as expected).
Though Initial Balance is based off the first 60 minutes of the trading markets opening, but Crypto is 24/7, this indicator looks at how Asia, London and New York Stock Exchanges opening trading can affect Crypto price action.
Source: Initial Balance Monitoring Panel
Cerca negli script per "bitcoin"
Boftei's StrategyI wrote this strategy about a year ago, but decided to publish it just now. I have not been able to implement this strategy in the market. If you can, then I will be happy for you.
This strategy is based on my "Botvenko Script". (It finds the difference between the logarithms of closing prices from different days.) (Check this script in my profile)
Then the strategy makes trades when the "Botvenko Script" indicator crosses the levels set earlier and manually selected for each currency pair/shares: long/short opening/closing levels, long/short re-entry levels. (They are drawn with horizontal dotted lines.) The names of these lines are: buy/sell level, long/short retry - too low/high, long close up/down, dead - close the short. Manual selection of each of the parameters provides a qualitative entry of the strategy into the deal. However, without restraining mechanisms, the strategy enters into rather controversial deals. In order to avoid going long/short during bear/bull markets, which is unacceptable, I added a fan of EMA lines.
The fan consists of several EMA lines, which are set according to Fibonacci numbers (21, 55, 89, 144). If the lines in the fan are arranged in ascending order (ema_21>ema_55 and ema_55>ema_89 and ema_89>ema_144), then this indicates a bull market, during which I banned shorting. And vice versa: during the bear market (ema_21<ema_55 and ema_55<ema_89 and ema_89<ema_144) I banned long trading. If these two inequalities are not met, then this indicates that the market is flat, and during it it is allowed to enter any transactions, because a flat is a good moment to catch massive movements in the future by entering a transaction. (This is all visualized using semi-transparent thick lines of green, yellow and red colors.)
By default, all parameters are adjusted for the btc/usd (bitstamp) pair. Best of all, the strategy shows itself if 1 candle = 1 day.
At the time of writing, on the pair btcusd (bitstamp) (1d) with pyramiding = 1, the strategy shows a profit of 64728896%. If pyramiding is increased by 1, then the profit will be greater, but I still prefer pyramiding = 1.
There is a possibility that my strategy is doing complete nonsense. I don't vouch for her.
If you select parameters for other pairs of currencies/stocks, then you should not change anything in the fan of lines.
That's all, probably.
Grospector DCA V.3This is system for DCA with strategy.
This has 5 zone Extreme high , high , normal , low , Extreme low. You can dynamic set min - max percent every zone.
Extreme zone is derivative short and long which It change Extreme zone to Normal zone all position will be closed.
Every Zone is splitted 10 channel. and this strategy calculate contribution.
and now can predict price in future.
Idea : Everything has average in its life. For bitcoin use 4 years for halving. I think it will be interesting price.
Default : I set MA is 365*4 days and average it again with 365 days.
Input :
len: This input represents the length of the moving average.
strongLen: This input represents the length of the moving average used to calculate the strong buy and strong sell zone.
shortMulti: This input represents the multiplier * moveing average used to calculate the short zone.
strongSellMulti: This input represents the multiplier used to calculate the strong sell signal.
sellMulti: This input represents the multiplier * moveing average used to calculate the sell zone.
strongBuyMulti: This input represents the multiplier used to calculate the strong sell signal.
longMulti: This input represents the multiplier * moveing average used to calculate the long zone.
*Diff sellMulti and strongBuyMulti which is normal zone.
useDerivative: This input is a boolean flag that determines whether to use the derivative display zone. If set to true, the derivative display zone will be used, otherwise it will be hidden.
zoneSwitch: This input determines where to display the channel signals. A value of 1 will display the signals in all zones, a value of 2 will display the signals in the chart pane, a value of 3 will display the signals in the data window, and a value of 4 will hide the signals.
price: Defines the price source used for the indicator calculations. The user can select from various options, with the default being the closing price.
labelSwitch: Defines whether to display assistive text on the chart. The user can select a boolean value (true/false), with the default being true.
zoneSwitch: Defines which areas of the chart to display assistive zones. The user can select from four options: 1 = all, 2 = chart only, 3 = data only, 4 = none. The default value is 2.
predictFuturePrice: Defines whether to display predicted future prices on the chart. The user can select a boolean value (true/false), with the default being true.
DCA: Defines the dollar amount to use for dollar-cost averaging (DCA) trades. The user can input an integer value, with a default value of 5.
WaitingDCA: Defines the amount of time to wait before executing a DCA trade. The user can input a float value, with a default value of 0.
Invested: Defines the amount of money invested in the asset. The user can input an integer value, with a default value of 0.
strategySwitch: Defines whether to turn on the trading strategy. The user can select a boolean value (true/false), with the default being true.
seperateDayOfMonth: Defines a specific day of the month on which to execute trades. The user can input an integer value from 1-31, with the default being 28.
useReserve: Defines whether to use a reserve amount for trading. The user can select a boolean value (true/false), with the default being true.
useDerivative: Defines whether to use derivative data for the indicator calculations. The user can select a boolean value (true/false), with the default being true.
useHalving: Defines whether to use halving data for the indicator calculations. The user can select a boolean value (true/false), with the default being true.
extendHalfOfHalving: Defines the amount of time to extend the halving date. The user can input an integer value, with the default being 200.
Every Zone : It calculate percent from top to bottom which every zone will be splited 10 step.
To effectively make the DCA plan, I recommend adopting a comprehensive strategy that takes into consideration your mindset as the best indicator of the optimal approach. By leveraging your mindset, the task can be made more manageable and adaptable to any market
Dollar-cost averaging (DCA) is a suitable investment strategy for sound money and growth assets which It is Bitcoin, as it allows for consistent and disciplined investment over time, minimizing the impact of market volatility and potential risks associated with market timing
MTF Diagonally Layered RSI - 1 minute Bitcoin Bot [wbburgin]This is a NON-REPAINTING multi-timeframe RSI strategy (long-only) that enters a trade only when two higher timeframes are oversold. I wrote it on BTC/USD for 1min, but the logic should work on other assets as well. It is diagonally layered to be profitable for when the asset is in a downtrend.
Diagonal layering refers to entry and exit conditions spread across different timeframes. Normally, indicators can become unprofitable because in downtrends, the overbought zones of the current timeframe are not reached. Rather, the overbought zones of the faster timeframes are reached first, and then a selloff occurs. Diagonally-layered strategies mitigate this by selling diagonally, that is, selling once the faster timeframe reaches overbought and buying once the slower timeframe reaches oversold.
Thus this strategy is diagonally layered down . I may create a separate script that alternates between diagonal-up and diagonal-down based off of overall trend, as in extended trend periods up this indicator may not flash as frequently. This can be visualized in a time series x timeframe chart as an "X" shape. Something to consider...
Let me know if you like this strategy. Feel free to alter the pyramiding entries, initial capital, and entry size, as well as commission regime. My strategies are designed to maximize average profit instead of flashing super frequently, as the fees will eat you up. Additionally, at the time of publication, all of my strategy scripts are intended to have profitable Sharpe and Sortino ratios.
Timeframes, RSI period, and oversold/overbought bounds are configurable.
Rsi strategy for BTC with (Rsi SPX)
I hope this strategy is just an idea and a starting point, I use the correlation of the Sp500 with the Btc, this does not mean that this correlation will exist forever!. I love Trading view and I'm learning to program, I find correlations very interesting and here is a simple strategy.
This is a trading strategy script written in Pine Script language for use in TradingView. Here is a brief overview of the strategy:
The script uses the RSI (Relative Strength Index) technical indicator with a period of 14 on two securities: the S&P 500 (SPX) and the symbol corresponding to the current chart (presumably Bitcoin, based on the variable name "Btc_1h_fixed"). The RSI is plotted on the chart for both securities.
The script then sets up two trading conditions using the RSI values:
A long entry condition: when the RSI for the current symbol crosses above the RSI for the S&P 500, a long trade is opened using the "strategy.entry" function.
A short entry condition: when the RSI for the current symbol crosses below the RSI for the S&P 500, a short trade is opened using the "strategy.entry" function.
The script also includes a take profit input parameter that allows the user to set a percentage profit target for closing the trade. The take profit is set using the "strategy.exit" function.
Overall, the strategy aims to take advantage of divergences in RSI values between the current symbol and the S&P 500 by opening long or short trades accordingly. The take profit parameter allows the user to set a specific profit target for each trade. However, the script does not include any stop loss or risk management features, which should be considered when implementing the strategy in a real trading scenario.
RSI and MA with Trailing Stop Loss and Take Profit (by Coinrule)The relative strength index is a momentum indicator used in technical analysis. It measures the speed and magnitude of a coin's recent price changes to evaluate overvalued or undervalued conditions in the price of that coin. The RSI is displayed as an oscillator (a line graph essentially) on a scale of zero to 100. When the RSI reaches oversold levels, it can provide a signal to go long. When the RSI reaches overbought levels, it can mark a good exit point or alternatively, an entry for a short position. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
A moving average (MA) calculates the average of a selected range of prices, usually closing prices, by the number of periods in that range. Essentially it is used to help smooth out price data by creating a constantly updated average price.
The Strategy enters and closes trades when the following conditions are met:
Entry Conditions:
RSI is greater than 50
MA9 is greater than MA50
RSI increases by 5
Exit Conditions:
Price increases by 1% trailing
Price decreases by 2% trailing
This strategy is back-tested from 1 January 2022 to simulate how the strategy would work in a bear market. The strategy provides good returns.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
VWMA/SMA 3Commas BotThis strategy utilizes two pairs of different Moving Averages, two Volume-Weighted Moving Averages (VWMA) and two Simple Moving Averages (SMA).
There is a FAST and SLOW version of each VWMA and SMA.
The concept behind this strategy is that volume is not taken into account when calculating a Simple Moving Average.
Simple Moving Averages are often used to determine the dominant direction of price movement and to help a trader look past any short-term volatility or 'noise' from price movement, and instead determine the OVERALL direction of price movement so that one can trade in that direction (trend-following) or look for opportunities to trade AGAINST that direction (fading).
By comparing the different movements of a Volume-Weighted Moving Average against a Simple Moving Average of the same length, a trader can get a better picture of what price movements are actually significant, helping to reduce false signals that might occur from only using Simple Moving Averages.
The practical applications of this strategy are identifying dominant directional trends. These can be found when the Volume Weighted Moving Average is moving in the same direction as the Simple Moving Average, and ideally, tracking above it.
This would indicate that there is sufficient volume supporting an uptrend or downtrend, and thus gives traders additional confirmation to potentially look for a trade in that direction.
One can initially look for the Fast VWMA to track above the Fast SMA as your initial sign of bullish confirmation (reversed for downtrending markets). Then, when the Fast VWMA crosses over the Slow SMA, one can determine additional trend strength. Finally, when the Slow VWMA crosses over the Slow SMA, one can determine that the trend is truly strong.
Traders can choose to look for trade entries at either of those triggers, depending on risk tolerance and risk appetite.
Furthermore, this strategy can be used to identify divergence or weakness in trending movements. This is very helpful for identifying potential areas to exit one's trade or even look for counter-trend trades (reversals).
These moments occur when the Volume-Weighted Moving Average, either fast or slow, begins to trade in the opposite direction as their Simple Moving Average counterpart.
For instance, if price has been trending upwards for awhile, and the Fast VWMA begins to trade underneath the Fast SMA, this is an indication that volume is beginning to falter. Uptrends need appropriate volume to continue moving with momentum, so when we see volume begin to falter, it can be a potential sign of an upcoming reversal in trend.
Depending on how quickly one wants to enter into a movement, one could look for crosses of the Fast VWMA under/over the Fast SMA, crosses of the Fast VWMA over/under the Slow SMA, or crosses over/under of the Slow VWMA and the Slow SMA.
This concept was originally published here on TradingView by ProfitProgrammers.
Here is a link to his original indicator script:
I have added onto this concept by:
converting the original indicator into a strategy tester for backtesting
adding the ability to conveniently test long or short strategies, or both
adding the ability to calculate dynamic position sizes
adding the ability to calculate dynamic stop losses and take profit levels using the Average True Range
adding the ability to exit trades based on overbought/oversold crosses of the Stochastic RSI
conveniently switch between different thresholds or speeds of the Moving Average crosses to test different strategies on different asset classes
easily hook this strategy up to 3Commas for automation via their DCA bot feature
Full credit to ProfitProgrammers for the original concept and idea.
Any feedback or suggestions are greatly appreciated.
Trend Following based on Trend ConfidenceThis is a Trend Following strategy based on the Trend Confidence indicator.
The goal of this strategy is to be a simple Trend Following strategy, but also to be as precise as possible when it comes to the question 'how confident are we that a linear trend is ongoing?'. For this we calculate the 'confidence' of a linear trend in the past number of closing prices. The idea of this strategy is that past a certain confidence, the ongoing linear trend is more likely to continue than not.
Trend Confidence:
The Trend Confidence shows us how strong of a linear trend the price has made in the past number (given by Length parameter) of closing prices. The steepness of the price change makes the Trend Confidence more extreme (more positive for an uptrend or more negative for a downtrend), and the deviation from a straight line makes the Trend Confidence less extreme (brings the confidence closer to 0). This way we can filter out signals by wild/sudden price moves that don't follow a clear linear trend.
Math behind the Trend Confidence:
A linear fit is made on the past number of closing prices, using Ordinary Linear Regression. We have the steepness of the linear fit: b in y=a+bx . And we have the standard deviation of the distances from the closing prices to the linear fit: sd . The Trend Confidence is the ratio b/sd .
Entries and Exits:
For entry and exit points we look at how extreme the Trend Confidence is. The strategy is based on the assumption that past a certain confidence level, the ongoing linear trend is more likely to continue than not.
So when the Trend Confidence passes above the 'Long entry" threshold, we go Long. After that when the Trend Confidence passes under the 'Long exit' threshold, we exit. The Long entry should be a positive value so that we go Long once a linear uptrend with enough confidence has been detected.
When the Trend Confidence passes below the 'Short entry' threshold, we go Short. After that when the Trend Confidence passes above the 'Short exit' threshold, we exit. The Short entry should be a negative value so that we go Short once a linear downtrend with enough confidence has been detected.
Default Parameters:
The strategy is intended for BTC-USD market, 4 hour timeframe. The strategy also works on ETH-USD with similar parameters.
The Length is arbitrarily set at 30, this means we look at the past 30 closing prices to determine a linear trend. Note that changing the length will change the range of Trend Confidence values encountered.
The default entry and exit thresholds for Longs and Shorts do not mirror each other. This is because the BTC-USD market goes up more heavily and more often than it goes down. So the ideal parameters for Longs and Shorts are not the same.
The positive results of the strategy remain when the parameters are slightly changed (robustness check).
The strategy uses 100% equity per trade, but has a 10% stop loss so that a maximum of 10% is risked per trade.
Commission is set at 0.1% as is the highest commission for most crypto exchanges.
Slippage is set at 5 ticks, source for this is theblock.co.
Ichimoku Cloud and ADX with Trailing Stop Loss (by Coinrule)The Ichimoku Cloud is a collection of technical indicators that show support and resistance levels, as well as momentum and trend direction. It does this by taking multiple averages and plotting them on a chart. It also uses these figures to compute a “cloud” that attempts to forecast where the price may find support or resistance in the future.
The Ichimoku Cloud was developed by Goichi Hosoda, a Japanese journalist, and published in the late 1960s. It provides more data points than the standard candlestick chart. While it seems complicated at first glance, those familiar with how to read the charts often find it easy to understand with well-defined trading signals.
The Ichimoku Cloud is composed of five lines or calculations, two of which comprise a cloud where the difference between the two lines is shaded in.
The lines include a nine-period average, a 26-period average, an average of those two averages, a 52-period average, and a lagging closing price line.
The cloud is a key part of the indicator. When the price is below the cloud, the trend is down. When the price is above the cloud, the trend is up.
The above trend signals are strengthened if the cloud is moving in the same direction as the price. For example, during an uptrend, the top of the cloud is moving up, or during a downtrend, the bottom of the cloud is moving down.
DMI is simple to interpret. When +DI > - DI, it means the price is trending up. On the other hand, when -DI > +DI , the trend is weak or moving on the downside. The ADX does not give an indication about the direction but about the strength of the trend.
Typically values of ADX above 25 mean that the trend is steeply moving up or down, based on the -DI and +D positioning. This script aims to capture swings in the DMI, and thus, in the trend of the asset, using a contrarian approach.
Trading on high values of ADX, the strategy tries to spot extremely oversold and overbought conditions. Values of ADX above 45 may suggest that the trend has overextended and is may be about to reverse.
This strategy combines the Ichimoku Cloud with the ADX indicator to better enter trades.
Long orders are placed when these basic signals are triggered.
Long Position:
Tenkan-Sen is above the Kijun-Sen
Chikou-Span is above the close of 26 bars ago
Close is above the Kumo Cloud
MACD line crosses over the signal line
-DI is greater than +DI
ADX is greater than 45
Close Position:
3% increase trailing
3% decrease trailing
The script is backtested from 1 January 2018 and provides good returns.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
This script also works well on MATIC (1d timeframe), ETH (1d timeframe), and SOL (1d timeframe).
Catching the Bottom (by Coinrule)This script utilises the RSI and EMA indicators to enter and close the trade.
The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. The RSI is displayed as an oscillator (a line graph) on a scale of zero to 100. The RSI can do more than point to overbought and oversold securities. It can also indicate securities that may be primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average (SMA), which applies an equal weight to all observations in the period.
The strategy enters and exits the trade based on the following conditions.
ENTRY
RSI has a decrease of 3.
RSI <40.
EMA100 has crossed above the EMA50.
EXIT
RSI is greater than 65.
EMA9 has crossed above EMA50.
This strategy is back tested from 1 April 2022 to simulate how the strategy would work in a bear market and provides good returns.
Pairs that produce very strong results include ETH on the 5m timeframe, BNB on 5m timeframe, XRP on the 45m timeframe, MATIC on the 30m timeframe and MATIC on the 2H timeframe.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Ichimoku Cloud and Bollinger Bands (by Coinrule)The Ichimoku Cloud is a collection of technical indicators that show support and resistance levels, as well as momentum and trend direction. It does this by taking multiple averages and plotting them on a chart. It also uses these figures to compute a “cloud” that attempts to forecast where the price may find support or resistance in the future.
The Ichimoku Cloud was developed by Goichi Hosoda, a Japanese journalist, and published in the late 1960s. It provides more data points than the standard candlestick chart. While it seems complicated at first glance, those familiar with how to read the charts often find it easy to understand with well-defined trading signals.
The Ichimoku Cloud is composed of five lines or calculations, two of which comprise a cloud where the difference between the two lines is shaded in.
The lines include a nine-period average, a 26-period average, an average of those two averages, a 52-period average, and a lagging closing price line.
The cloud is a key part of the indicator. When the price is below the cloud, the trend is down. When the price is above the cloud, the trend is up.
The above trend signals are strengthened if the cloud is moving in the same direction as the price. For example, during an uptrend, the top of the cloud is moving up, or during a downtrend, the bottom of the cloud is moving down.
The Bollinger Bands are among the most famous and widely used indicators. A Bollinger Band is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average ( SMA ) of a security's price, but which can be adjusted to user preferences. They can suggest when an asset is oversold or overbought in the short term, thus providing the best time for buying and selling it.
This strategy combines the Ichimoku Cloud with Bollinger Bands to better enter trades.
Long orders are placed when these basic signals are triggered.
Long Position:
Tenkan-Sen is above the Kijun-Sen
Chikou-Span is above the close of 26 bars ago
Close is above the Kumo Cloud
The closing price is greater than the upper standard deviation of the Bollinger Bands
Short Position:
Tenkan-Sen is below the Kijun-Sen
Chikou-Span is below the close of 26 bars ago
Close is below the Kumo Cloud
The upper standard deviation of the Bollinger Band is greater than the closing price
The script is backtested from 1 January 2022 and provides good returns.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
This script also works well on BTC 30m/1h, ETH 2h, MATIC 2h/30m, AVAX 1h/2h, SOL 45m timeframes
RSI SMA Crossover StrategyOverview
RSI SMA Crossover Strategy works the same way as traditional MA crossover strategies, but using RSI instead of price. When RSI crosses over the SMA, a long position is opened (buy). When RSI crosses under the SMA, the long position is closed (sell).
This strategy can be very effective when the right inputs are used (see below). Be sure to use the backtesting tool to determine the optimal parameters for a given asset/timeframe.
Inputs/Parameters
RSI Length: length for RSI calculation (default = 50)
SMA Length: length for SMA calculation (default = 25)
Strategy Properties
Initial Capital = $1000
No default properties are defined for Slippage, Commission, etc, so be sure to set these values to get accurate backtesting results. This script is being published open-source for a reason - save yourself a copy and adjust the settings as you like!
Backtesting Results
Testing on Bitcoin (all time index) 1D chart, with all default parameters.
$1,000 initial investment on 10/07/2010 turns into almost $2.5 billion as of 08/30/2022 (compared to $334 million if the initial investment was held over the same period)
Remember, results can vary greatly based on the variables mentioned above, so always be sure to backtest.
ALMA/EMA/SRSI Strategy + IndicatorBack with another great high hit rate strategy!!
Disclaimer* This strategy was sampled using source code written by @ClassicScott , as referred to in the script, there is a clear line where the source code was scripted by myself.
This Strategy consists of three key factors, the ALMA, EMA crossover, and a Stochastic Rsi
ALMA: The Alma is the step line shown, turning green and red at select times. This average value gives general oversight of the macro movement of price action. and this particular one was coded by Mr.ClassicScott.
EMA crossover: At the input screen you are given an option of the fast and slow ema's. The default is solely for the hit rate and correlation to the Alma of this strategy. The arrows you see depicted on the chart are the crossover events happening.
Stochastic Rsi: The Stochastic Rsi is a stochastic value, using data sampled from the rsi. The use of this indicator in my strategy is to prevent entries when too overbought and oversold, as well as closures and vice versa, to prevent holding bags either way.
Fixed % TP: In the input screen you are given a take profit and stop loss percentage, for good R/R the hit rate will take a notch down, but with no R/R it will be near perfect.
How to use this:
Add it to your chart to get the strategy inputs. (The strategy is really only useful on a 15min TF. However the indicator within it can be used on anything at anytime!)
Watch the yellow and aqua moving averages, these are your ema's and crossover's will trigger signals based on your integer inputs.
Find Correlation between other leading indicators, as well as crossover's down/up and a red/green alma.
DO NOT use the arrows as buy/sell signals. These are simply to show ema's are crossing under or over. Momentum indicator's paired with this can be useful to determine if it could be a buy signal or sell signal.
Cheat Code's Notes:
Almost at 1000 boosts!!! I appreciate the support from everyone and I will keep trying my best to deliver quality strategies for the people.
-Cheat Code
BYBIT:BTCUSDT
Close v Open Moving Averages Strategy (Variable) [divonn1994]This is a simple moving average based strategy that works well with a few different coin pairings. It takes the moving average 'opening' price and plots it, then takes the moving average 'closing' price and plots it, and then decides to enter a 'long' position or exit it based on whether the two lines have crossed each other. The reasoning is that it 'enters' a position when the average closing price is increasing. This could indicate upwards momentum in prices in the future. It then exits the position when the average closing price is decreasing. This could indicate downwards momentum in prices in the future. This is only speculative, though, but sometimes it can be a very good indicator/strategy to predict future action.
What I've found is that there are a lot of coins that respond very well when the appropriate combination of: 1) type of moving average is chosen (EMA, SMA, RMA, WMA or VWMA) & 2) number of previous bars averaged (typically 10 - 250 bars) are chosen.
Depending on the coin.. each combination of MA and Number of Bars averaged can have completely different levels of success.
Example of Usage:
An example would be that the VWMA works well for BTCUSD (BitStamp), but it has different successfulness based on the time frame. For the 12 hour bar timeframe, with the 66 bar average with the VWMA I found the most success. The next best successful combo I've found is for the 1 Day bar timeframe with the 35 bar average with the VWMA.. They both have a moving average that records about a month, but each have a different successfulness. Below are a few pair combos I think are noticeable because of the net profit, but there are also have a lot of potential coins with different combos:
It's interesting to see the strategy tester change as you change the settings. The below pairs are just some of the most interesting examples I've found, but there might be other combos I haven't even tried on different coin pairs..
Some strategy settings:
BTCUSD (BitStamp) 12 Hr Timeframe : 66 bars, VWMA=> 10,387x net profit
BTCUSD (BitStamp) 1 Day Timeframe : 35 bars, VWMA=> 7,805x net profit
BNBUSD (Binance) 12 Hr Timeframe : 27 bars, VWMA => 15,484x net profit
ETHUSD (BitStamp) 16 Hr Timeframe : 60 bars, SMA => 5,498x net profit
XRPUSD (BitStamp) 16 Hr Timeframe : 33 bars, SMA => 10,178x net profit
I only chose these coin/combos because of their insane net profit factors. There are far more coins with lower net profits but more reliable trade histories.
Also, usually when I want to see which of these strategies might work for a coin pairing I will check between the different Moving Average types, for example the EMA or the SMA, then I also check between the moving average lengths (the number of bars calculated) to see which is most profitable over time.
Features:
-You can choose your preferred moving average: SMA, EMA, WMA, RMA & VWMA.
-You can also adjust the previous number of calculated bars for each moving average.
-I made the background color Green when you're currently in a long position and Red when not. I made it so you can see when you'd be actively in a trade or not. The Red and Green background colors can be toggled on/off in order to see other indicators more clearly overlayed in the chart, or if you prefer a cleaner look on your charts.
-I also have a plot of the Open moving average and Close moving average together. The Opening moving average is Purple, the Closing moving average is White. White on top is a sign of a potential upswing and purple on top is a sign of a potential downswing. I've made this also able to be toggled on/off.
Please, comment interesting pairs below that you've found for everyone :) thank you!
I will post more pairs with my favorite settings as well. I'll also be considering the quality of the trades.. for example: net profit, total trades, percent profitable, profit factor, trade window and max drawdown.
*if anyone can figure out how to change the date range, I woul really appreciate the help. It confuses me -_- *
PlanB Quant Investing 101 v2This script has been Inspired by PlanB Article Quant Investing 101.
With this script, I implemented Plan B strategy outlined in that article, trying to reproduce his findings independently and allowing TradeView Users to do the same.
PlabB is aware of this effort, and he's positive about it, via Twitter commenting, liking and sharing of this resource .
Trading Idea:
This script uses RSI index to determine the Buy And Sell signal.
As per the original PlanB article:
IF ( RSI was above 90% last six months AND drops below 65%) THEN sell,
IF ( RSI was below 50% last six months AND jumps +2% from the low) THEN buy, ELSE hold
My simple code is aimed at replicating his study in Pine so that every TV user can check his signal.
LONG SAZB $This strategy combines the use of:
-The MTF EMA to detect trends.
-The MACD to create Long and Short Buy signals.
-The ATR for setting Stop Losses and Take Profits.
This works well with many different crypto and fiat pairs, but it must be optimized for the certain behavior of the currency pair. Its optimal use is strong trends, not so profitable when sideways.
This strategy was developed with the 5-minute Bitcoin / TetherUS Perpetual futures for Binance (Crypto trading platform).
This is the first version, updates will come.
MTF EMA
The MTF EMA (Multi-TimeFrame Exponential Moving Average ) is a great indicator to see the overall trend of an asset, you can see the status of a moving average for all timeframes on one chart.
Normally when you check a moving average of the price it's on some specific timeframe. The MTF EMA allows you to see moving average status for all timeframes in a single place. You can simplify your visual representation and know if an asset or a pair is overall bullish or bearish , with this improving your entry and exit signal decisions.
This strategy uses the 1 hour and 15 min EMA with different values. Experimenting with these is important to understand the currency pairs.
Up trend:
Price (source) > 1h MTF and 1h MTF < 15m MTF
Down trend:
Price (source) < 1h MTF and 1h MTF > 15m MTF
MACD
Using MACD (Moving Average Convergence Divergence) as a reference, the strategy identifies when the MACD line crosses over (a factor in a buy signal) and under (a factor in a Sell signal) the Signal line. This shows a shift in positive (cross over) and negative (cross under) of a security.
This strategy uses values of 12 on the Fast MA, 26 on the Slow MA, and 9 in the Signal Line MA.
The optional ribbon is for a more visual representation of the MACD .
The MACD and Signal line have the option to have a crossover limit to cancel buy signals depending on the value they crossed at according to the 0 line of the MACD . This is to avoid fake signals.
ATR TP/SL
Using ATR to define the stop loss and take profit is that it should allow you to set them at a realistic distance from price. Simply put, a pair experiencing a high level of volatility has a higher ATR, and a low volatility stock has a lower ATR.
The indicator does not indicate the price direction; rather it is used primarily to measure volatility caused by gaps and limit up or down moves. All this is used to allow the Stop Loss “breathing space” so trades don't get unnecessarily stopped, and allow the Take Profit to be at a more realistic, flexible, and profitable price.
This strategy uses different values for Longs and Shorts depending on the market behavior, optionally analyzes swing lows and highs according to the value of the candle lookback and sets the ATR depending on them, they must be tested to optimum. Also the ATR has a multiplicator to find the most efficient price levels.
Trade Setup
Shorts and Longs can be turned OFF and ON.
There is an optional maximum % loss for trades, the trade is closed when the high-low average of a candle is over this %.
Longs
This strategy indicates a Long Buy signal when these conditions are met:
- Uptrend signal from MTF EMA .
- MACD Crossover of Signal ( MACD > Signal) while being under the MACD crossover limit.
A Long exit signal is indicated when:
- Price crosses over the ATR Take Profit limit.
- Price crosses under the ATR Stop Loss limit.
- Price crosses under optional max % long loss.
Shorts
This strategy indicates a Long Buy signal when these conditions are met:
- Downtrend signal from MTF EMA .
- Signal Crossover of MACD ( MACD < Signal) while being over the MACD crossover limit.
A Short exit signal is indicated when:
- Price crosses under the ATR Take Profit limit.
- Price crosses over the ATR Stop Loss limit.
- Price crosses over optional max % short loss.
Disclaimer
1. I am not a licensed financial advisor or broker dealer. I do not tell you when or what to buy or sell. I developed this software which enables you to execute manual or automated trades multiple trades using TradingView. The software allows you to set the criteria you want for entering and exiting trades.
2. Do not trade with money you cannot afford to lose.
3. I do not guarantee consistent profits or that anyone can make money with no effort. I am not selling the holy grail.
4. Every system can have winning and losing streaks.
5. Money management plays a large role in the results of your trading. For example: lot size, account size, broker leverage, and broker margin call rules all have an effect on results. Also, your Take Profit and Stop Loss settings for individual pair trades and for overall account equity have a major impact on results. If you are new to trading and do not understand these items, then I recommend you seek education materials to further your knowledge.
**YOU NEED TO FIND AND USE THE TRADING SYSTEM THAT WORKS BEST FOR YOU AND YOUR TRADING TOLERANCE.**
**I HAVE PROVIDED NOTHING MORE THAN A TOOL WITH OPTIONS FOR YOU TO TRADE WITH THIS PROGRAM ON TRADINGVIEW.**
I am 100 % open to suggestions to improve the script.
If you encounter any problems or would like to see the script, share them with me at "steven17zmuda@gmail.com".
Items in this description text may not be written directly by me, but may be taken from education sites.
Trade HourThis script is just finds the best hour to buy and sell hour in a day by checking chart movements in past
For example if the red line is on the 0.63 on BTC/USDT chart it mean the start of 12AM hour on a day is the best hour to buy (all based on
It's just for 1 hour time-frame but you can test it on other charts.
IMPORTANT: You can change time Zone in strategy settings.to get the real hours as your location timezone
IMPORTANT: Its for now just for BTC/USDT but you can optimize and test for other charts...
IMPORTANT: A green and red background color calculated for show the user the best places of buy and sell (green : positive signal, red: negative signals)
settings :
timezone : We choice a time frame for our indicator as our geo location
source : A source to calculate rate of change for it
Time Period : Time period of ROC indicator
About Calculations:
1- We first get a plot that just showing the present hour as a zigzag plot
2- So we use an indicator ( Rate of change ) to calculate chart movements as positive and negative numbers. I tested ROC is the best indicator but you can test close-open or real indicator or etc as indicator.
3 - for observe effects of all previous data we should indicator_cum that just a full sum of indicator values.
4- now we need to split this effects to hours and find out which hour is the best place to buy and which is the best for sell. Ok we should just calculate multiple of hour*indicator and get complete sum of it so:
5- we will divide this number to indicator_cum : (indicator_mul_hour_cum) / indicator_cum
6- Now we have the best hour to buy! and for best sell we should just reverse the ROC indicator and recalculate the best hour for it!
7- A green and red background color calculated for show the user the best places of buy and sell that dynamically changing with observing green and red plots(green : positive signal, red: negative signals) when green plot on 15 so each day on hour 15 the background of strategy indicator will change to 15 and if its go upper after some days and reached to 16 the background green color will move to 16 dynamically.
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!
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.
Rate Of Change Trend Strategy (ROC)This is very simple trend following or momentum strategy. If the price change over the past number of bars is positive, we buy. If the price change over the past number of bars is negative, we sell. This is surprisingly robust, simple, and effective especially on trendy markets such as cryptos.
Works for many markets such as:
INDEX:BTCUSD
INDEX:ETHUSD
SP:SPX
NASDAQ:NDX
NASDAQ:TSLA
Sideways Strategy DMI + Bollinger Bands (by Coinrule)Markets don’t always trade in a clear direction. At a closer look, most of the time, they move sideways. Relying on trend-following strategies all the time can thus lead to repeated false signals in such conditions.
However, before you can safely trade sideways, you have to identify the most suitable market conditions.
The main features of such strategies are:
Short-term trades, with quick entries and quick exits
Slightly contrarian and mean-reversionary
Require some indicator that tells you it’s a sideways market
This Sideways DMI + Bollinger Bands strategy incorporates such features to bring you a profitable alternative when the regular trend-following systems stop working.
ENTRY
1. The trading system requires confirmation for a sideways market from the Directional Movement Index (DMI) before you can start opening any trades. For this purpose, the strategy uses the absolute difference between positive and negative DMI, which must be lower than 20.
2. To pick the right moment to buy, the strategy looks at the Bollinger Bands (BB). It enters the trade when the price crosses over the lower BB.
EXIT
The strategy then exits when the move has been exhausted. Generally, in sideways markets, the price should revert lower. The position is closed when the price crosses back down below the upper BB.
The best time frame for this strategy based on our backtest is the 1-hr. Shorter timeframes can also work well on certain coins that are more volatile and trade sideways more often. However, as expected, these exhibit larger volatility in their returns. In general, this approach suits medium timeframes. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange.
Three EMAs Trend-following Strategy (by Coinrule)Trend-following strategies are great because they give you the peace of mind that you're trading in line with the market.
However, by definition, you're always following. That means you're always a bit later than your want to be. The main challenges such strategies face are:
Confirming that there is a trend
Following the trend, hopefully, early enough to catch the majority of the move
Hopping off the trade when it seems to have run its course
This EMA Trend-following strategy attempts to address such challenges while allowing for a dynamic stop loss.
ENTRY
The trading system requires three crossovers on the same candle to confirm that a new trend is beginning:
Price crossing over EMA 7
Price crossing over EMA 14
Price crossing over EMA 21
The first benefit of using all three crossovers is to reduce false signals. The second benefit is that you know that a strong trend is likely to develop relatively soon, with the help of the fast setup of the three EMAs.
EXIT
The strategy comes with a fixed take profit and a volatility stop, which acts as a trailing stop to adapt to the trend's strength. That helps you get out of the way as soon as market conditions change. Depending on your long-term confidence in the asset, you can edit the fixed take profit to be more conservative or aggressive.
The position is closed when:
The price increases by 4%
The price crosses below the volatility stop.
The best time frame for this strategy based on our backtest is the 4-hr. Shorter timeframes can also work well, although they exhibit larger volatility in their returns. In general, this approach suits medium timeframes. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange.
Optimised RSI strategy for Reversals (by Coinrule)The most common way to use the RSI to spot a good buy opportunity is to check for values lower than 30. Unfortunately, the RSI can remain in oversold territory for long periods, and that could leave you trapped in a trade in loss. It would be appropriate to wait for a confirmation of the trend reversal.
In the example above I use a short-term Moving Average (in this case, the MA9) coupled with an RSI lower than 40. This combination of events is relatively rare as reversal confirmations usually come when RSI values are already higher. As unusual as this setup is, it provides buy-opportunities with much higher chances of success.
The parameters of this strategy would be:
ENTRY: RSI lower than 40 and MA9 lower than the price
TAKE PROFIT and STOP-LOSS with a ratio of at least 2. That means that if you set up a take profit of 3%, your stop-loss shouldn’t be larger than 1.5%.
The advantage of this approach is that it has a high rate of success and allows you the flexibility of setting up the percentages of the take profit and stop-loss according to your preferences and risk appetite.
