Dollar cost averaging trading system (DCA)As investors, we often face the dilemma of willing high stock prices when we sell, but not when we buy. There are times when this dilemma causes investors to wait for a dip in prices, thereby potentially missing out on a continual rise. This is how investors get lured away from the markets and become tangled in the slippery slope of market timing, which is not advisable to a long-term investment strategy.
Skyrex developed a complex trading system based on dollar-cost averaging in Quick Fingers Luc's interpretation. It is a combinations of strategies which allows to systematically accumulate assets by investing scaled amounts of money at defined market cycle global support levels. Dollar-cost averaging can reduce the overall impact of price volatility and lower the average cost per asset thus even during market slumps only a small bounce is required to reach take profit.
The strategy script monitors a chart price action and identifies bases as they form. When bases are reached the script provides entry actions. During price action development an asset value can go lower and in this way the script will perform safety entries at each subsequent accumulation levels. When weighted average entry price reaches target profit the script will perform a take profit action.
Bases are identified as pivot lows in a fractal pattern and validated by an adjustable decrease/rise percentage to ensure significancy of identified bases. To qualify a pivot low, the indicator will perform the following validation:
Validate the price rate of change on drops and bounces is above a given threshold amount.
Validate the volume at the low pivot point is above the volume moving average (using a given length).
Validate the volume amount is a given factor of magnitude above is above the volume moving average.
Validate the potential new base is not too close to the previous range by using a given price percent difference threshold amount.
A fractal pattern is a recurring pattern on a price chart that can predict reversals among larger, more chaotic price movements.
These basic fractals are composed of five or more bars. The rules for identifying fractals are as follows:
A bearish turning point occurs when there is a pattern with the highest high in the middle and two lower highs on each side.
A bullish turning point occurs when there is a pattern with the lowest low in the middle and two higher lows on each side.
Basic dollar-cost averaging approach is enhances by implementation of adjustable accumulation levels in order to provide opportunity of setting them at defined global support levels and Martingale volume coefficient to increase averaging effect. According to Quick Fingers Luc's principles trading principles we added volume validation of a base because it allows to confirm that the market is resistant to further price decrease.
The strategy supports traditional and cryptocurrency spot, futures, options and marginal trading exchanges. It works accurately with BTC, USD, USDT, ETH and BNB quote currencies. Best to use with 1H timeframe charts and limit orders. The strategy can be and should be configured for each particular asset according to its global support and resistance levels and price action cycles. You can modify levels and risk management settings to receive better performance
Advantages of this script:
Strategy has high net profit of 255% at backtests
Backtests show high accuracy around 75%
Low Drawdowns of around 14% at backtests
Strategy is sustainable to market slumps and can be used for long-term trading
The strategy provides a large number of entries which is good for diversification
Can be applied to any market and quote currency
Easy to configure user interface settings
How to use?
1. Apply strategy to a trading pair your are interested in using 1H timeframe chart
2. Configure the strategy: change layer values, order size multiple and take profit/stop loss values according to current market cycle stage
3. Set up a TradingView alert to trigger when strategy conditions are met
4. Strategy will send alerts when to enter and when to exit positions which can be applied to your portfolio using external trading platforms
5. Update settings once market conditions are changed using backtests on a monthly period
在腳本中搜尋"bot"
VXD Cloud Edition for Python-Binance-bots.VXD Cloud Edition for Python-Binance-bots.
to overcome sideways market conditions this cloud configured for low timeframe.
every TA is same as VXD Cloud Edition but custom alert message for bots.
Risk:Reward Calculation
Risk of Ruin Setting can now selected between Fixed $ or %
if Buy your Stoploss will be Swing low
if Sell your Stoploss will be Swing high and can be setting at Pivot Setting
then Auto Position Sizing and TP line will be calculated form there and will show in Orange color line (Draw Position Box is available)
Tailing SL when price greater than RR=1
Alert Setting
{{strategy.order.alert_message}}
Python-Bot
github.com
There are 2 mode : one-way mode and hedge mode is different script in my Github profile.
read README.MD and there's video tutorial in thai language.
Pls study app.py and it's script before deploy for your own safty and your own risk, I'm NOT responsible for your loss.
Mean Reverse Grid Algorithm - The Quant ScienceMean Reverse Grid Algorithm - The Quant Science™ is a dynamic grid algorithm that follows the trend and run a mean reverting strategy on average percentage yield variation.
DESCRIPTION
Trades on different price levels of the grid, following the trend. The grid consists of 10 levels, 5 higher and 5 lower. The grids together create a channel, this channel represents the total percentage change where the algorithm works. The channel also represents the average change yields of the asset, identified during analysis with the "Yield Trend Indicator".
The algorithm can be set long or short.
1. Long algorithm: opens long positions with 20% of the capital every time the price crossunder a lower grid, for a maximum total of 5 simultaneous trades. Trades are closed each time the price crossover a higher grid.
2. Short algorithm: opens short positions with 20% of the capital every time the price crossover a higher grid, for a maximum total of 5 simultaneous trades. Trades are closed each time the price crossunder a lower grid.
USER INTERFACE SETTING
The user configures the percentage value of each grid from the user interface.
AUTO TRADING COMPLIANT
With the user interface, the trader can easily set up this algorithm for automatic trading. Automating it is very simple, activate the alert functions and enter the links generated by your broker.
BACKTESTING INCLUDED
With the user interface, the trader can adjust the backtesting period of the strategy before putting it live. You can analyze large periods such as years or months or focus on short-term periods.
NO LIMIT TIMEFRAME
This algorithm can be used on all timeframes and is ideal for lower timeframes.
GENERAL FEATURES
Multi-strategy: the algorithm can apply either the long strategy or the short strategy.
Built-in alerts: the algorithm contains alerts that can be customized from the user interface.
Integrated grid: the grid indicator is included.
Backtesting included: automatic backtesting of the strategy is generated based on the values set.
Auto-trading compliant: functions for auto trading are included.
ABOUT BACKTESTING
Backtesting refers to the period 1 August 2022 - today, ticker: ETH/USDT, timeframe 1H.
Initial capital: $1000.00
Commission per trade: 0.03%
DCA Average Arbitrage - The Quant ScienceDCA Average Arbitrage - The Quant Science™ is a quantitative algorithm based on a DCA model that uses averaging to create a statistical arbitrage system.
DESCRIPTION
The algorithm can be set long or short.
1. Long algorithm: opens long positions with 100% of the capital every time the price deviates negatively for a certain percentage distance from the average.
2. Short algorithm: opens short positions with 100% of capital every time the price deviates positively for a certain percentage distance from the average.
The closing of positions depends on the parameters activated by the user. The user can set the closing on the reverse condition and/or add functions such as stop loss, take profit and closing after a certain bar period.
USER INTERFACE SETTING
The user chooses the long or short direction and sets the parameters for average as length, source and percent distance.
AUTO TRADING COMPLIANT
With the user interface, the trader can easily set up this algorithm for automatic trading. Automating it is very simple, activate the alert functions and enter the links generated by your broker.
BACKTESTING INCLUDED
With the user interface, the trader can adjust the backtesting period of the strategy before putting it live. You can analyze large periods such as years or months or focus on short-term periods.
NO LIMIT TIMEFRAME
This algorithm can be used on all timeframes and is ideal for lower timeframes.
GENERAL FEATURES
Multi-strategy: the algorithm can apply either the long strategy or the short strategy.
Built-in alerts: the algorithm contains alerts that can be customized from the user interface.
Integrated indicator: the quantity indicator is included.
Backtesting included: automatic backtesting of the strategy is generated based on the values set.
Auto-trading compliant: functions for auto trading are included.
ABOUT THE BACKTEST
Backtesting refers to the period 1 January 2022 - today, ticker: ICP/USDT, timeframe 5 minutes.
Initial capital: $1000.00
Commission per trade: 0.03%
Statistical Correlation Algorithm - The Quant ScienceStatistical Correlation Algorithm - The Quant Science™ is a quantitative trading algorithm.
ALGORITHM DESCRIPTION
This algorithm analyses the correlation ratios between two assets. The main asset (on the chart), and the secondary asset (set by the user). Then apply the long or short trading strategy.
The algorithm divides trading work into three parts:
1. Correlation analysis
2. Long or short entry
3. Closing trades
Inside the strategy: the algorithm analyses the percentage change yields from a previous session, of the secondary asset. If the variation meets the set condition then it will open a long or short position, on the primary asset. The open position is closed after 'x' number of sessions. Stop loss and take profit can be added to the trade exit parameters.
Logic: analyses the correlation between two assets and looks for a statistical advantage within the correlation.
INDICATOR DESCRIPTION
The algorithm includes a quantitative indicator. This indicator is used for correlation analysis and offers a quick reading of the quantitative data. The blue area shows the correlation ratio values. The yellow histograms show the percentage change in the yields of the main asset. Purple histograms show the percentage change in secondary asset yields.
GENERAL FEATURES
Multi time-frame: the user can set any time-frame for the secondary asset.
Multi asset: the user analyses the conditions on a second asset.
Multi-strategy: the algorithm can apply either the long strategy or the short strategy.
Built-in alerts: the algorithm contains alerts that can be customized from the user interface.
Integrated indicator: the quantity indicator is included.
Backtesting included: automatic backtesting of the strategy is generated based on the values set.
Auto-trading compliant: functions for auto trading are included.
USER INTERFACE SETTINGS
Through the intuitive user interface, you can manage all the parameters of this algorithm without any programming experience. The user interface is extremely descriptive and contains all the information needed to understand the logic of the algorithm and to configure it correctly.
1. Date range: through this function you can adjust the analysis and working period of the algorithm.
2. Asset: through this function you can adjust the secondary asset and its time-frame. You can enter any type of asset, even indices and economic indicators.
3. Asset details: this function is used to adjust the percentage change to be analyzed on the secondary asset. The analysis and input conditions are also chosen.
4. Active long or short strategy: this function is used to set the type of strategy to be used, long or short.
5. Setting algo trading alert: with this function, users can manage alerts for their web-hook.
6. Exit&Money management: with this function the user can adjust the exit periods of each trade and activate or deactivate any stop losses and take profits.
7. Data Value Analysis: this function is used to adjust the parameters for the quantity indicator.
STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT [Loxx]STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT is the backtest strategy for "STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones " seen below:
Included:
This backtest uses a special implementation of ATR and ATR smoothing called "True Range Double" which is a range calculation that accounts for volatility skew.
You can set the backtest to 1-2 take profits with stop-loss
Signals can't exit on the same candle as the entry, this is coded in a way for 1-candle delay post entry
This should be coupled with the INDICATOR version linked above for the alerts and signals. Strategies won't paint the signal "L" or "S" until the entry actually happens, but indicators allow this, which is repainting on current candle, but this is an FYI if you want to get serious with Pinescript algorithmic botting
You can restrict the backtest by dates
It is advised that you understand what Heikin-Ashi candles do to strategies, the default settings for this backtest is NON Heikin-Ashi candles but you have the ability to change that in the source selection
This is a mathematically heavy, heavy-lifting strategy with multi-layered adaptivity. Make sure you do your own research so you understand what is happening here. This can be used as its own trading system without any other oscillators, moving average baselines, or volatility/momentum confirmation indicators.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Inverse MACD + DMI Scalping with Volatility Stop (By Coinrule)This script is focused on shorting during downtrends and utilises two strength based indicators to provide confluence that the start of a short-term downtrend has occurred - catching the opportunity as soon as possible.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to hodl this cash, or use it to reinvest when the market reaches attractive buying levels.
Alternatively, you can use this when trading contracts on futures markets where there is no need to already own the underlying asset prior to shorting it.
ENTRY
The trading system uses the Momentum Average Convergence Divergence (MACD) indicator and the Directional Movement Index (DMI) indicator to confirm when the best time is for selling. Combining these two indicators prevents trading during uptrends and reduces the likelihood of getting stuck in a market with low volatility.
The MACD is a trend following momentum indicator and provides identification of short-term trend direction. In this variation it utilises the 12-period as the fast and 26-period as the slow length EMAs, with signal smoothing set at 9.
The DMI indicates what way price is trending and compares prior lows and highs with two lines drawn between each - the positive directional movement line (+DI) and the negative directional movement line (-DI). The trend can be interpreted by comparing the two lines and what line is greater. When the negative DMI is greater than the positive DMI, there are more chances that the asset is trading in a sustained downtrend, and vice versa.
The system will enter trades when two conditions are met:
1) The MACD histogram turns bearish.
2) When the negative DMI is greater than the positive DMI.
EXIT
The strategy comes with a fixed take profit combined with a volatility stop, which acts as a trailing stop to adapt to the trend's strength. 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:
Take-Profit Exit: +8% price decrease from entry price.
OR
Stop-Loss Exit: Price crosses above the volatility stop.
In general, this approach suits medium to long term strategies. The backtesting for this strategy begins on 1 April 2022 to 18 July 2022 in order to demonstrate its results in a bear market. Back testing it further from the beginning of 2022 onwards further also produces good returns.
Pairs that produce very strong results include SOLUSDT on the 45m timeframe, MATICUSDT on the 2h timeframe, and AVAUSDT on the 1h timeframe. Generally, the back testing suggests that it works best on the 45m/1h timeframe across most pairs.
A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
[XBotUniverse] TREND 1.0 XBotUniverse is an automated crypto trading platform that allows you to start trading and investing in cryptocurrencies seamlessly, by implementing a fully automated trading bot using TradingView
The TREND Strategy version is a signal indicator following the direction of the trend with a fully technical method, without fundamentals, without the need for manual trading. Uses eleven Technical Indicators to measure trend strength. There are six breakouts and crosses to confirm open positions.
Runs well when the market is moving uptrend or downtrend. This strategy uses good risk management, where every open position takes a take profit of around 1.6% and a stop loss of around 1.3%, this is possible, because this strategy applies a "Smart Quantity", where the amount or quantity will adjust so that the profit and loss according to the calculation risk management, this will prevent your funds from being eroded
When the market is running very sideways, the strategy reduces the risk of loss by reducing open positions, by measuring the strength of the trend and measuring the shallowness of the average candle bar mixed with the "Smart Quantity" technique.
This strategy is applied to the Binance Futures Exchange, uses API Management, and can only be applied to the ETHUSDTPERP pair, so if it is used on another pair, the strategy will not work.
The main point of this platform is to reduce risk in trading digital instruments, by maintaining transactions with good risk management, namely preventing trading in a brutal way that will erode funds in an immeasurable way. Another important thing is that there is no risk of fundraising which results in funds or deposits being unable to be taken, such as in the case of forex trading robots where the biggest risk is with the broker, where our funds may not necessarily return due to non-transparent and poorly regulated mechanisms.
No business in this world can measure with certainty when the return on investment or can generate consistent profits, so also take care of the risk from our side as users, please analyze this strategy by paying attention to the strategy tester to measure NetProfit, Percent Profitable and Maximum Drawdown, from trading history that has occurred the previous month, because the results are relatively the same as what happened in real trading. Also remember that past profits do not guarantee future profits
Every business has risks, so use cold money so that we are comfortable when trading, don't use debt / borrow money, pawn money, kitchen money or hot money.
If you are interested in using this strategy for automated and real-time trading, please contact the owner of this strategy or the founders who can provide education on how to use it.
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
Joker Trailing TP BotTrailing Take Profit is used by the traders to increase their gains when the prices moves in a favorable direction. Let’s have a look at what is Trailing Take Profit and how it works.
What Is a Trailing Take Profit?
Trailing Take Profit is a term largely used in crypto, whereas you may encounter the term Trailing Stop in traditional trading describing almost the same thing, So what’s the difference between Trailing Take Profit and Trailing Stop? Trailing Stop is a type of Stop Loss automatically moving in the same direction as the asset’s price. Trailing Take Profit is nothing else than Trailing Stop activated after initial Take Profit is reached.
The main difference between these two is that Trailing Take Profit takes the profit in any case (altough it might be later annihilated by Trailing Stop). Thus, Trailing Take Profit reduces the risks that might’ve occurred using Trailing Stop alone. Trailing Take Profit is bound to the maximum of Take Profit price instead of just a price increase/decrease.
As you might notice, the terms Trailing Take Profit and Stop Loss are quite similar. To avoid confusion, in this article we will be talking about Trailing Take Profit as defined above.
Trailing Take Profit only moves in one direction. It is designed to lock in profit and limit losses. The trailing profit only moves up (in case of a long strategy) once the price has surpassed previous high and a new high has been established. If the trailing take profit moves up, it cannot move back down, thus securing the profit and preventing losses.
Trailing Take Profit allows the trade to remain open and continue to profit as long as the price is moving in the investor’s favor. If the price changes direction and the change surpasses the previously set percentage the order will be closed.
How Does it Work?
For example if you buy BTC at the price of 10000, if you set a Take Profit at 11000 and a Trailing Take Profit at 5% :
If the price goes up to 10500, nothing happens because the Take Profit at 11000 has not been reached.
Then if the BTC price goes up top 11000, a Stop Order at 10450 will be set.
Then if the BTC price goes down to 10500, the Stop Order stays at 104500.
Then if the BTC price goes up to 12000, the Stop Order moves to 11400.
Then if the BTC price goes down to 11000, the Stop Order at 11400 is executed.
You see that without Trailing Take Profit, the buy order would have been sold at 11000. Thus, a trader would miss an earning opportunity at 11400.
Short Swing Bearish MACD Cross (By Coinrule)This strategy is oriented towards shorting during downside moves, whilst ensuring the asset is trading in a higher timeframe downtrend, and exiting after further downside.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to hodl this cash, or use it to reinvest when the market reaches attractive buying levels. Alternatively, you can use this when trading contracts on futures markets where there is no need to already own the underlying asset prior to shorting it.
ENTRY
This script utilises the MACD indicator accompanied by the Exponential Moving Average (EMA) 450 to enter trades. The MACD is a trend following momentum indicator and provides identification of short-term trend direction. In this variation it utilises the 11-period as the fast and 26-period as the slow length EMAs, with signal smoothing set at 9.
The EMA 450 is used as additional confirmation to prevent the script from shorting when price is above this long-term moving average. Once price is above the EMA 450 the script will not open any shorts - preventing the rule from attempting to short uptrends. Due to this, this strategy is ideal for setting and forgetting.
The script will enter trades based on two conditions:
1) When the MACD signals a bearish cross. This occurs when the EMA 11 crosses below the EMA 26 within the MACD signalling the start of a potential downtrend.
2) Price has closed below the EMA 450. Price closing below this long-term EMA signals that the asset is in a sustained downtrend. Price breaking above this could indicate a bullish strength in which shorting would not be profitable.
EXIT
This script utilises a set take-profit and stop-loss from the entry of the trade. The take profit is set at 8% and the stop loss of 4%, providing a risk reward ratio of 2. This indicates the script will be profitable if it has a win ratio greater than 33%.
Take-Profit Exit: -8% price decrease from entry price.
OR
Stop-Loss Exit: +4% price increase from entry price.
Based on backtesting results across a selection of assets, the 45-minute and 1-hour timeframes are the best for this strategy.
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.
The backtesting data was recorded from December 1st 2021, just as the market was beginning its downtrend. We therefore recommend analysing the market conditions prior to utilising this strategy as it operates best on weak coins during downtrends and bearish conditions, however the EMA 450 condition should mitigate entries during bullish market conditions.
Customizable OCC Non Repainting Scalper Bot v7.0bThis strategy is intended to be used on an automated trading platform and should be run on a one minute chart for fastest confirmations and signal relay to crypto automation platform. The strategy has been modded to only go long at this time to focus on profitability for one direction. The open long and close long text fields allow you to use your own webhook message for this purpose.
I have spent quite a bit of time and I figured I would put it out to the community to share the work and also get some feedback.
Ok, so let me say that I have done absolutely everything I can to make the strategy not repaint while still maintaining it's profitability. It has been a challenge so I am publishing this to the community to help test this.
What I have observed: the strategy will not repaint in real time. That is, if you have the chart open and keep it open, the signals are the same as the ones that are sent out by the strategy. In certain cases, when I reload the chart- the signals might be off from what was sent. In some ways, that is repainting, but it is repainting based on losing the real time data and recalculating from a different set of bars- since I am running it on a one minute chart then the start becomes different when you refresh.
To address repainting while keeping the strategy calculating as quickly as possibly I have altered the logic in the following ways:
I have made an assumption which might not work for everyone- at the first tick of the next bar, you can almost safefly assume in crypto that if you are looking at the previous bar for information, the open of the current bar was the close of the previous bar. This for the most part holds true in crypto with good liquidity. If you are trading a pair that jumps around due to low volume- this might not be the strategy to use. I might publish a different version with a different logic.
I have altered the security repaint to use isbarconfirmed, so at the very end of the bar (as soon as the bar is confirmed), we recalculate to the higher time frames. So as soon as the data is available, it is at that point that we can then safely calculate higher time frames. This is unique and experimental, but seems to do well at creating good signals for entry.
I have employed my own intervals by utilizing the resolution as an integer (used by the previous authors)- but in this case, I use the interval to take a snapshot of the higher time frame. With open close cross, the different moving averages can cause the repainting as they change to show the exact point of the cross. The interval feature I created minimizes this by utilizing the previous bar info until the interval is closed and then we recalculate the variants. You can use the interval offset feature to denote which minute is the one that starts and ends the interval. So for instance, Trading View uses minue 1 and minute 31 for 30 minute intervals. If you offset your 30 minute interval would start on minute 16 and do its calculations based on the last 30 minutes,
As with most of my scripts, I have started using filters and a "show data" feature that will give you the ability to see the values of indicators that you cannot plot in the overlay. This allows you to figure out how to filter losing trades or market conditions.
I have also added a trailing stop and created a fixed stop loss as seems to perform better than the original occ strategy. The original one seemed to repaint enough that it would close too quickly and not give the posiition enough time to become profitable. In certain cases where there was a large move, it would perform well, but for the most part the trades would not close profitably even though the backtest said that it did - probably due to the delay in execution and pinescript not having a confirmation on what the actual position price was.
This is still in beta mode, so please forward test first and use at your own risk.
If you spot repaint issues, please send me a message and try to explain the situation.
Miyagi (VWAP) + DCA BacktestVWAP has been the main condition for entry for our trading community when using the Wick Hunter bot for quite some time now; however using VWAP alone can lead to poor entries and bags.
Miyagi adds filtering conditions which can then be used to improve entries as opposed to simply using VWAP alone.
Miyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
Miyagi (VWAP) + DCA Backtest allows you to backtest your VWAP settings across each pair.
VWAP + Backtest starts with the user selectable take profit and stoploss, both in percent.
Backtesting can be done in any trend direction: Long, Short, or both.
First we start with the VWAP Settings for entry.
The VWAP Settings are based off the original VWAP Indicator found here:
VWAP Timeframe can be adjusted to chart timeframe, or to another timeframe of choice.
VWAP Period is adjustable and should be set to your current period setting.
VWAP Lower Line (Long) is the Percentage Below VWAP = Long Entry.
VWAP Upper Line (Short) is the Percentage Above VWAP = Short Entry.
Once your VWAP Settings are now configured for your current trading settings, you can move onto the DCA and Backtest Settings.
Select DCA to allow and backtest DCA.
Equity %: allows you to select what percent of Equity you will use.
Leverage: Set the leverage you will trade at, from 1-100X.
DCA Start %: This is the percent that your DCA Starts at, in % away from initial entry.
Entry Order Size %: This is the entry order size, in percent of your account, that is used for entry.
DCA Order Size %: This is the DCA order size, in percent of your account, that is used for the first DCA.
DCA Order Size Scale %: This is the DCA size scaling, in %, that is applied to all DCA's after the first.
DCA % from Avg: This is the % away from Average DCA setting.
Step Size Scale %: This is the step size scale setting in percent, that is placed away from your first DCA.
DCA Buy Count: How many DCAs in total that can be used.
Properties Tab includes generic backtest settings including capital amount, base currency, order size, commission, slippage and margin.
Happy Trading!
Short Selling EMA Cross (By Coinrule)BINANCE:AVAXUSDT
This short selling script works best in periods of downtrends and general bearish market conditions, with the ultimate goal to sell as the the price decreases further and buy back before a rebound.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to to hodl this cash, or use it to reinvest when the market reaches attractive buying levels.
Entry
The exponential moving average ( EMA ) 20 and EMA 50 have been used for the variables determining the entry to the short. EMAs can operate better than simple moving averages due to the additional weighting placed on the most recent data points, whereas simple moving averages weight all the data the same. This means that price is tracked more closely and the most recent volatile moves can be captured and exploited more efficiently using EMAs.
Our backtesting data revealed that the most profitable timeframe was the 30-minute timeframe, this also enabled a good frequency of trades and high profitability.
A fast (shorter term) exponential moving average , in this strategy the EMA 20, crossing under a slow (longer term) moving average, in this example the EMA 50, signals the price of an asset has started to trend to the downside, as the most recent data signals price is declining compared to earlier data. The entry acts on this principle and executes when the EMA 20 crosses under the EMA 50.
Enter Short: EMA 20 crosses under EMA 50.
Exit
This script utilises a take profit and stop loss for the exit. The take profit is set at -8% and the stop loss is set at +16% from the entry price. This would normally be a poor trade due to the risk:reward equalling 0.5. However, when looking at the backtesting data, the high profitability of the strategy (93.33%) leads to increased confidence and showcases the high probability of success according to historical data.
The take profit (-8%) and the stop loss (+16%) of the strategy are widely placed to ensure the move is captured without being stopped out due to relief rallies. The stop loss also plays a role of mitigating losses and minimising risk of being stuck in a short position once there has been a fundamental trend reversal and the market has become bullish .
Exit Short: -8% price decrease from entry price.
OR
Exit Short: +16% price increase from entry price.
Tip: Research what coins have consistent and large token unlocks / highly inflationary tokenomics, and target these during bear markets to short as they will most likely have substantial selling pressure that outweighs demand - leading to declining prices.
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.
The backtesting data was recorded from December 1st 2021, just as the market was beginning its downtrend. We therefore recommend analysing the market conditions prior to utilising this strategy as it operates best on weak coins during downtrends and bearish conditions.
The Impossible TraderTHE IMPOSSIBLE TRADER
A simple, but effective High Freq Strategy script based on MACD or RSI trend, with extra customizable Alert Messages for Bots.
WHAT IT DOES
This script (works best at lower TimeFrames) just follow the trend of MACD or RSI on your asset.
Why it should work? Because in an upper trend, there are more chance of green candles than reds. And in dump trend there are more chance of red candles than greens.
While trend is positive, it will try to open Long orders as fast as possible at market price.
While trend is negative, it will try to open Short orders as fast as possible at market price.
HOW TO SETUP YOUR PREFERENCES
Capital : Insert a % of Margin you want to use for your positions (usually 30% is quite good)
Leverage : Choose leverage based on your plans
Trail Tick @ : This value (in Tick) tell the script "when" the "Trail Stop" order must be activated (from the Entry price)
Offset Tick @ : This is the price (in Tick) from the Trail Stop Price activated. Basically it is a Stop Loss that follow the price at a fixed distance.
SL Tick @ : Set a Stop Loss at amount Tick distance from the Entry Price. (Let's call it a Safety Stop Loss for bad decisions...)
TP Tick @ : Set a Take Profit at amount Tick distance from the Entry Price. Sometimes is better to exit in full Gain than keep positions.
Strategy : You can choose a Only Long, Only Short or Long+Short sametime strategy.
with MACD or RSI : You can try the strategy applied on MACD or applied on customizable RSI EMA
EMA : If you choosed RSI EMA, you can set any value for your testing (usually 80-120 works very nice)
Exit order after bars : Some Exchanges / Brokers apply fixed cost, and a strategy too fast could not be productive. This set will let you to delay the Exit Order on already Opened positions.
Keep Stop Loss active : If you are planning a delay for Exit Orders, sometime could be useful to keep activated Stop Loss.
Strategy Preset : Some preset I've found interesting, with good results.
BackTest Days : If there are too many results and script doesn't work, you can choose a closer range to show results.
EXTRA FEATURES
On Screen Display : OSD will show you some realtime stats about your strategy, like Asset Tick, Trading Period Range, Drawdown, Gains and not closed trade.
Alert Message : You can enter custom Long Entry/Exit and Short Entry/Exit message for your Bots (like AutoView, WunderBit, etc...). When alert is triggered, you can send custom message with {{strategy.order.comment}} in the text field
AutoView Alert Message : If you are user of AutoView, you can generate your calls. Those are tested only on Oanda with index like Sp500, US100, Us30.
TIPS ON USE
Some asset on TradingView require an higher initial capital. Go to this Script Settings -> Properties and rise Initial Capital.
Be aware of commissions and spread when evalutating a strategy. Go to this Script Settings -> Properties and set Commission and Slippage
Trail Stop and Ticks could be difficult to understand, but very profitable. Please take your time and study how it works.
Consider Tick like the minimum movement your asset can do. Ticks occurs "intra-bar", so some of your positions could be closed almost instantly.
Consider Trail Stop like a Stop Loss that keep always the same distance from your positions, but never came back . If you are in gain, say of 10 Ticks, and your Trail have 5 Ticks, this means for sure a close at minimum 5 Ticks from Entry Price.
On Screen Display will show you Ticks for your asset. This will help you on strategy settings, because not all asset responds on the same way.
ONLY LONG EXAMPLE
ONLY SHORT EXAMPLE
Hyper Bot | Self Optimizing Buy & SellThis strategy primarily uses Bollinger Bands with custom improvements and alterations in entry exit signals which i have learned over the past years.
How to use?
Visit strategy settings. You will see checkboxes before all options, meaning they can be turned ON and OFF.
For "Long SL %" to work, the "LONG" should be enabled aswell. If Long is not enabled, changing numbers in "Long SL%" won't make any difference.
Likewise if you want to test take profit, then either long or short or both should be enabled with "TP Long%" or "TP Short%"
This wide customization is being provided for you to be able to test all possible variations and choose whichever best is working for you. Play around with the numbers of SL% and TP% and find best ones that work for you in terms of drawdown, risk to profit etc.
Commission 0.1% by default is included, if however your commissions are lower, please change them to get better results.
This strategy is mainly optimized for Cryptocurrencies, however if you wish to use for indices and commodities please DM me, i will provide customization services.
Enjoy!
© Copyright 2022 "M Tahreem Alam"
AlphaTrend For ProfitViewThis strategy is based on the AlphaTrend indicator by KivancOzbilgic A full description of this algorithm functionality may be found by clicking the linked image above.
Changes and/or additions:
It is now a backtestable strategy
Updated alert trigger logic
Easy integration with ProfitView to use this algorithm for automated trading
When you create an alert, and you are using ProfitView, select " alert() function calls only " as the condition option. If you would rather set your own custom alert message, select " Order fills only " instead.
There is a selectable setting in the options to trigger alert() function calls immediately, that you may use to see what text it will send.
EMA_TREND_CATCHERSimple strategy based on the crossing of moving averages of 50,100 and 200 periods. Designed to identify trends
You are ready to use trading bots (all you have to do is fill in "Variables for Alert"). However, it can also be used for discretionary operations.
BTCUSDT FUTURES BINANCE
4H
Oversold RSI with tight SL Strategy (by Coinrule)This is one of the best strategies that can be used to get familiar with technical indicators and start to include them in your trading bot rules.
ENTRY
1. This trading system uses the RSI ( Relative Strength Index ) to anticipate good points to enter positions. RSI is a technical indicator frequently used in trading. It works by measuring the speed and change of price movements to determine whether a coin is oversold (indicating a good entry point) or overbought (indicating a point of exit/entry for a short position). The RSI oscillates between 0 and 100 and is traditionally considered overbought when over 70 and oversold when below 30.
2. To pick the right moment to buy, the strategy enters a trade when the RSI falls below 30 indicating the coin is oversold and primed for a trend reversal.
EXIT
The strategy then exits the position when the price appreciates 7% from the point of entry. The position also maintains a tight stop-loss and closes the position if the price depreciates 1% from the entry price. The idea behind this is to cut your losing trades fast and let your winners ride.
The best time frame for this strategy based on our backtesting data is the daily. Shorter time frames can also work well on certain coins, however in our experience, the daily works best. Feel free to experiment with this script and test it on a variety of your coins! With our backtesting data 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 by volume. In the example shown, this strategy made a handsome net profit of 39.31% on Chainlink with 61.54% of trades being profitable.
EMA Stoch Strategy For ProfitViewThis strategy will enter positions when the set stochastic conditions are met, and uses the moving average to filter the direction of the trades (long/short). The background is used to illustrate the strength of the stochastic values.
The following is a step by step guide in order to automate the trading of the strategy with ProfitView:
In the indicator settings, set the desired stochastic and ema values, and the stochastic condition you want to use to enter a trade.
In the indicator, set which exchange, symbol, and account to execute trades on.
In the indicator, set the PV Alert names you intend to use. If you want to use the same names as provided in the pastebin below, you may set the three names to Market Long, Market Short, TP SL Hit.
In PV, create two new PV Alerts in the PV Alert tab in accordance to these specifics pastebin.com .
On the Tradingview chart you want the indicator run on, create a new TV alert with this script as its condition, and specify the alert to "alert() function calls only".
Cycle strategy DEMO V1.0READ BEFORE USING:
This indicator includes the Cycle strategy and 2 bonus indicators ( pivot strategy & volume strategy). This is a DEMO version that doesn't show the signals after end of January 2022. This indicator only allows you the backtest/study previous results and give a general idea on the workings on the indicator.
Introduction
Cycle strategy works on the following timeframes, 1HR, 4HR, 12HR and 1D. Cycle strategy is mostly used by me on the 1D timeframe, however, if you prefer shorter timeframes you can select those. Indicator settings will automatically adjust based off the timeframe on your chart. I use this indicator mainly for BTC , however, altcoins such as ETH, LTC, DOGE, ADA, ETC, SOL and more have shown reasonable results in the past.
The theory behind cycle strategy
The cycle strategy is based off the theory that Bitcoin moves in cycles, each time followed by periods of sideways action. This strategy tries to breakout trade momentum out of a sideways range by calculating things such as momentum, volatility and average price. The indicators, based off calculations, tries to spot breakout trends. When a trend break up it gives a "long" signal on the chart and when the trend breaks down it gives a "short" signal.
Sometimes the price doesn't break out, this is called a fakeout. The bot will automatically reverse its previous signal and take a small loss.
Applications of it in my trading setup
I apply the wave strategy in my own trading enviroment as a tool to determine buy/sell moments and general trend.
Whenever Bitcoin reaches extreme overbought level I'll wait for the indicator to give me a "sell" signal in order to hedge myself against possible corrections. In the past I've seen many bearmarkets before, I tended to not have any fiat on the side to buy these dips. The indicator has be allowed in the past to almost perfectly sell the top multiple times allowing me to accumulate BTC on lower levels and therefor increase my BTC position. I also use this indicator to spot the current Bitcoin trend. If the indicator shows a "long" signal I'll generally be looking to long on dips and whenever there is a "sell" signal I tend to look for shorts.
Bonus indicators
There are 2 bonus indicators included in this strategy. These are "bonus" indicators as I haven't had a long enough time to backtest them. They are based off my own strategies that I apply when trading. The bonus indicators have been highly succesful in the past though they are a bit more experimental.
Bonus indicator 1: Pivot strategy
Pivot points is a powerful indicator that Bitcoin tends to be very reactive to. The pivot strategy tries to determine if Bitcoin is in a bulltrend/beartrend. If Bitcoin is inside a bulltrend it will look to buy on pivot points . If the price is in a beartend it'll be looking to sell on pivot points .
Pivot strategy only works on 1HR timeframe, optimized on BYBIT:BTCUSD
Bonus indicator 2: Volume strategy
Volume strategy tries to look for large spikes of volume , once price breaks under this volume spike it'll try to buy/sell. The theory is that large volume spikes are traders getting stopped out on their leverage positions. By buying under these spikes it tries to counter trade these small price sqeeuzes.
Volume strategy only works on 1HR timeframe, only works on BYBIT:USD
