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.
Komut dosyalarını "profit" için ara
Swing Trader-Pro V2The strategy- what is it?
This indicator is designed from a theory created by myself in order to distinguish a correction from an impulse. This comes down to the ability to compare "x" range of candles to "y" range of candles and highlight key differences to then correctly portray that the most recent move in price will be (or is) a correction.
Following this theory, we all understand that corrections don't go with the trend right? So this means at some point, there is a high probability of a rejection somewhere in this most recent move, that will ultimately push price higher or lower as it continues back with the trend. Therefore, through extensive quantitative research and back-testing, we are able to highlight areas of high-probability rejections within these supposed corrections.
How does it work?
Firstly, we need to establish a high and low point (using pivots ) that help us decide what the state is of the recent move between the high and low (we call this "point A" and "point B"). So we can only consider whether the recent move in price was an impulse or a correction until the move from "point B" to "point C" is made. But before that, once we have identified "Point A" and "point B", we use 2 (supposedly) strong levels which help integrate a box onscreen and thus, indicate this area of high liquidity. This box will continue to adjust according to the change of pivots (if price keeps creating HH's & HL's or LH's & LL's depending on market trend). But if we establish a strong high and low and price stays within this range, then the box will remain in place.
The default color of the box is red; the only time the color of the box will change is when:
- Price retraces from the high/low back to the box (price has to touch the box)
AND
-If any of our confirmations indicate a successful correction based on our theory.
So the box color varies:
- Red = very weak (or) no entry = no confirmations were made
- Yellow = weak entry = some but not all confirmations were made
- Green = strong entry = all confirmations have indicated that the move from "point B" to "point C" (remember that "point C" is where the box is) is a correction when compared with the move from "point A" to "point B"
These confirmations are all validated on the same candle during live candle activity (not when the candle has closed on the box). As this happens, the confirmations will determine the state of entry quality as soon as price touches the box.
In this time, we will see a new orange label highlighting what indicators have confirmed a successful correction and what haven't.
The label shows the different confirmation indicators in which we have provided different names (as this is the secret we intend to keep). So we have:
- "CC"
- "B1/B2"
- "B3"
Usually, we will see either an "OK" or "NOT OK" next to each confirmation indicator. This just tells us whether they have confirmed or not. Please note that this "point C" label does not stay permanently, regardless of the state of entry quality. The label will in fact stay on the screen until the next box has been generated, which is usually a few candles after the entry has been triggered.
Entries, SL's and TP's
This indicator shows the user an area of high-probability rejection. So in terms of specifying a precise entry, you're completely free to enter on the following:
- the moment price touches the box (depending on what color it is of course)
- the other end of the box (if you would like to catch a "sniper entry")
- or if price pierces the entire box and is still green, you can wait to see if price comes back through the box (which indicates a false breakout).
As for Stop-losses, i would recommend:
- Long entries: set your SL at the recent low (this should be "point A")
- Short entries: set your SL to the recent high (this should be "point A" as well, because if you're switching from the "long entry" setting to the "short entry" setting, the indicator labels flip around and are the opposite of what they are for long entries).
For Take profits, this is entirely up to the user. Because some entries will allow you to have great RR ratios depending on how you manage the active trades. Some recommendations below:
- Set TP to "point B" pivot
- Use trailing stop function or something similar if available
- Add other indicators such as the RSI and close when price reaches key levels
- When price shows signs of exhaustion or early stages of reversal then just close
Additional information and recommendations
- This works on any time frame and on any financial market, whether you prefer Forex, stocks, crypto, commodities , etc.
- In regards to trade direction, you can change in the settings to look for either long or short positions in the market. I would recommend using it in favor of the overall trend of the markets because you will find a lot better entries. Although, this does work against the trend at times as well. Additionally, this tool also works in consolidating markets which is beneficial.
- After becoming used to the script, i would say to apply it twice to your screen and have one looking for Long entries and the other looking for Short entries.
- As the user, you have the ability to remove the labels in the parameter settings (because it does look quite messy onscreen, especially if you have both long and short entries on at the same time). I would only personally show the labels when price hits the current box to see what confirmations have been identified.
- I will also provide the best parameters to use. You will only need one set of parameters for each long and short setting, as these parameters are universal for any time frame and any financial market.
FIRST UPDATE
After extensive back testing using our first version, we found that in fact, there are some great opportunities being wasted as the entry box stays red. This is due to some series of market structure that don't always fit our theory of continuations within the market. We found that although our theory is accurate, the amount of times the market fits this is more rare than times when price follows sequences. When we look for sequences in the market instead of specifying differences between impulses and corrections, we actually see areas of serious repetitiveness, thanks to how our indicator initially generates. Not how it confirms. So, understanding this new theory through one component of our previous indicator, we are still able to keep boxes at the same area yet accurately confirm more profitable entries external to our full previous strategy.
Moving towards the practical side of things:
-Make sure "add extra confirmation" parameter is selected, as this will allow the indicator to search for more valid entries rather than just our normal confirmations. (this is a tick box).
- Default parameters are already set for both C1 and C2
In a simple sense, this update is added to find more confirmations to turn more red boxes into green boxes based on other theories outside of our original one. How we do this exactly is part of the mystery.
SECOND UPDATE
- Fibonacci based moving average: using elements of the Fibonacci sequence and its relevance to being a hot-spot in price activity, we have integrated this into a moving average which is stronger than your usual MA. Here, you will notice it showing stronger signs of rejecting price, especially when trending. Hence, this is extremely useful to implement into your strategy as part of the trend identification. When price is consolidating, depending on how volatile or close-in the waves are during these periods, the FMA is similar to your typical MA, so therefore not so good. But the overall intention of this is to enhance your conclusion to whether price is trending and whether price is bullish or bearish.
- This is now a strategy, not just an indicator: So now we can choose from a huge variety of parameters in accordance to what ones work best with what pair, or time frame. The typical parameters to change would be the entry points, stop losses and take profits. We have also added in a "SL to entry" option. ALL PARAMETERS ARE FIBONACCI LEVELS AS THIS MAKES IT UNIVERSAL TO ANY PAIR/ TIME FRAME.
- Move the entry boxes : So this is very useful for certain pairs and mainly to help the user understand key sequences on a quantitative level. Sometimes we can notice that pairs spike higher than the typical entry (0.618) so we have allowed flexibility to the point where you can alter the box appearance to either the 0.618 level (default), 0.786 and the 0.9 level.
- Back-testing: Now the user can back-test the strategy and see the performance within any financial market you add this to! Please note that according to the strategy, once a trade is placed, it wont enter any more trades when the current one is still active. I have requested to change this, but it is out of our development team's reach. However, this doesn't discredit what the system can help you achieve, as you will still be able to find profitable parameters within the financial markets.
Strategy default properties
Backtest start: this date is when you would like to start the backtest, however, the indicator will go as far as the data can be read
Backtest end: choose your date to end the back test.
Trade session: choose the trading session you want this strategy to work on.
Filter by session: you can filter the backtested results depending on whether you want the strategy to take trades within the chosen trading session.
Filter by Fibonacci moving average: select this if you would like for the back tested results to consider whether the valid trade setups are in accordance to what the FMA displays (Bullish or Bearish). This is deselected.
Fibonacci Moving Average Timeframe: here you can select what timeframe you would like the FMA to work on, default is the “same as chart” button/ option.
TraderDirection: choose whether you would like LONG or SHORT entries for the indicator to find.
Max risk per trade: choose the risk setting per trade, i would suggest lowering this to 1% ((MODERATOR) This is the default setting!)
EntryFib: choose between the options as to where you would like the strategy to enter positions, the default is the 0.618 zone which is the closest side of the box to price. You will also see that when you choose to change this, the boxes on your screen will move accordingly. A very helpful function!
StopFib: choose your Stop Loss based on the same Fibonacci level as what you choose for your entry, remember that the higher the fib level, the higher (or safer) your Stop Loss is from price spiking. It all comes down to preference.
TakeProfitFib: choose your Take Profit based on the same Fibonacci level as what you choose for your entry, remember that the lower the fib level, the higher your Take Profit is again, It all comes down to preference.
BreakevenFib: the default setting is on “disabled” however when you select a certain Fibonacci level, once price reaches there during the active trade, your Stop Loss will be set to entry, this function is designed to stop volatile price fluctuations rendering your in-profit trade result to hitting your Stop Loss and losing when it closes out.
MACD/RVSI/Stoch/RSI/EXP(Drawdown)I have been trying for several months to get a script to work on the 1min and this one gives some good backtest results. This script will also work on higher timeframes however, I've not extensively tested on higher timeframes. My aim was to get results on about 20 crypto coins then run the 1min bots in parallel looking for small frequent profits across all the coins. If you would like me to try and fit backtest results to any coin or pair on any timeframe please do get in touch anytime.
It's based on several indicators which are combined and then a newish way for the stop loss to implement based on an exponential rising which limits the time in each trade unless the price moves in the direction of the trade. The other useful feature is drawdown minimization which previously made all of my 1minute bot attempts non-practical due to differences between backtesting and actually running the bot(s) live.
Its possible at the top to paste in strategy comments which can be used through web-hooks for auto trading bots. Leaving these blank just defaults to the pre-programmed comments that provide some indication of why a trade was exited.
It is possible to select for Short and/or Long trades. Note however, that there are exponential markers on the charts for both long and short trades in any setting. I found that this way the bot worked well with regards to timing.
The next part of the user interface settings gets a bit tricky so try and use the sample parameters provided below. For example, select a crypto coin then try some of the options below until a reasonable backtest result in obtained (or select the best from the parameter groups tested) then move down the settings interface to optimise with the remaining settings.
So 'Use MACD/RVSI', 'RSI clause' and 'Use Stochastic' are set to true for the below sample settings (1min timeframe).
MACD/RVSI Confluence Resolution (1min, 2min, 5min, 10min, 1hour)
Timeframe RSI (1min, 2min, 3min, 15min, 1hour)
FastStoch, SlowStoch (1min, 45min: 5min, 30min: 1min, 1hour: 5min, 1hour)
Eg. for FTX:ETHPERP (MACD/RVSI Confluence Resolution=1hour, Timeframe RSI= 1min, FastStoch = 1min, SlowStoch = 45min)
Setting the timings is tricky - there is a lot going on. Have a look at the chart and select/deselect the options. The MACD/RVSI Confluence Resolution shows red and green vertical regions on the chart background. The Timeframe RSI colors the candle bodies red and green. These go green if the RSI crossed over 31% or red in the RSI crossed under 69%. The MACD/RVSI Confluence Resolution is explained in more detail in one of my other scripts. Then the Slow Stoch colors above and below the price action with red or green lines depending if on an uptrend or downtrend (approximately). Where there is also an up/down trend on the faster timeframe stoch there are vertical shaded fill regions between the slow stop above/below lines.
With all the above conditions selected to represent the data (looking at strategy backtest results whilst adjusting) there is a reasonable approximation to a credible trade.
So once an ok backtest result is obtained by selecting timing settings. Its ontot the Stop Ramp Settings. This is an exponential line which rises rapidly after a period of time thus exiting the trade or going upwards with the trade. It kind of limits the maximum time a trade will stay in position which forms part of the timing aspect of this bot. Look at the chart exponential red lines and adjust the settings, along with the backtest results to select a good timing.
Then its the Drawdown Catcher and the Take Profit Setting. Start with the drawdown catcher disabled i.e. set to zero. Put in a conservative Take Profit, for example if a Take Profit at 6% gives the best backtest results, go for 4% to account for differences between backtest results and actual live bot performance.
Then start to increase the Drawdown Catcher. This shades a lime region where the bot will not enter a trade. I found that with most trades using this bot, if the price action moved in the direction of the trade (long or short) at the onset - this gave most of the good results (high probability of positive trade). Also if a trade entered at the start price and when south, the accumilated drawdown from these failing trades made all previous 1min bot attempt non=profitable in practice (even with good backtest results). The exp timing and also this drawdown reduction strategies seem to be the thing which makes this approach credible.
Try to go for settings that give a very high change of positive trade. For example, an 85% profitable trades will probably provide say 55% positive trades in practice as its always highly possible to just fit the parameters to the exact position/trade timings - and in reality going forwards these don't play out the same. Also a Profit Factor of 2 is about the minimum I would accept - again this provides for example a Profit Factor of 1.2 in practive.
However all being said - I think its possible with this bot on the 1min across lots of coins - with regularly updating settings - to make profits. (Not financial advice)
Please do get in touch if you would like me to fit this bot to anytimeframe to any trade.
MoonFlag PhD
Scalping Dips On Trend (by Coinrule)Coinrule's Community is an excellent source of inspiration for our trading strategies.
In these months of Bull Market, our traders opted mostly on buy-the-dips strategies, which resulted in great returns recently. But there has been an element that turned out to be the cause for deep division among the Community.
Is it advisable or not to use a stop-loss during a Bull Market?
This strategy comes with a large stop-loss to offer a safer alternative for those that are not used to trade with a downside protection.
Entry
The strategy buys only when the price is above the Moving Average 50 , making it less risky to buy the dip, which is set to 2%.
The preferred time frame is 1-hour.
The stop-loss is set to be quite loose to increase the chances of closing the trade in profit, yet protecting from unexpected larger drawdowns that could undermine the allocation's liquidity.
Exit
Stop loss: 10%
Take Profit: 3%
In times of Bull Market, such a trading system has a very high percentage of trades closed in profit (ranging between 70% to 80%), which makes it still overall profitable to have a stop-loss three times larger than the take profit.
Pro tip: use a larger stop-loss only when you expect to close in profit most of the trades!
The strategy assumes each order to trade 30% of the available capital and opens a trade at a time. A trading fee of 0.1% is taken into account.
Pinescript v4 - The Holy Grail (Trailing Stop)After studying several other scripts, I believe I have found the Holy Grail! (Or perhaps I've just found a bug with Tradingview's Pinescript v4 language) Anyhow, I'm publishing this script in the hope that someone smarter than myself could shed some light on the fact that adding a trailing stop to any strategy seems to make it miraculously...no that's an understatement...incredulously, stupendously, mind-bendingly profitable. I'm talking about INSANE profit factors, higher than 200x, with drawdowns of <10%. Sounds too good to be true? Maybe it is...or you could hook it up to your LIVE broker, and pray it doesn't explode. This is an upgraded version of my original Pin Bar Strategy.
Recommended Chart Settings:
Asset Class: Forex
Time Frame: H1
Long Entry Conditions:
a) Exponential Moving Average Fan up trend
b) Presence of a Bullish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Short Entry Conditions:
a) Exponential Moving Average down trend
b) Presence of a Bearish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Exit Conditions:
a) Trailing stop is hit
b) Moving Averages cross-back (optional)
c) It's the weekend
Default Robot Settings:
Equity Risk (%): 3 //how much account balance to risk per trade
Stop Loss (x*ATR, Float): 0.5 //stoploss = x * ATR, you can change x
Stop Loss Trail Points (Pips): 1 //the magic sauce, not sure how this works
Stop Loss Trail Offset (Pips): 1 //the magic sauce, not sure how this works
Slow SMA (Period): 50 //slow moving average period
Medium EMA (Period): 18 //medium exponential moving average period
Fast EMA (Period): 6 //fast exponential moving average period
ATR (Period): 14 // average true range period
Cancel Entry After X Bars (Period): 3 //cancel the order after x bars not triggered, you can change x
Backtest Results (2019 to 2020, H1, Default Settings):
AUDUSD - 1604% profit, 239.6 profit factor, 4.9% drawdown (INSANE)
NZDUSD - 1688.7% profit, 100.3 profit factor, 2.5% drawdown
GBPUSD - 1168.8% profit, 98.7 profit factor, 0% drawdown
USDJPY - 900.7% profit, 93.7 profit factor, 4.9% drawdown
USDCAD - 819% profit, 31.7 profit factor, 8.1% drawdown
EURUSD - 685.6% profit, 26.8 profit factor, 5.9% drawdown
USDCHF - 1008% profit, 18.7 profit factor, 8.6% drawdown
GBPJPY - 1173.4% profit, 16.1 profit factor, 7.9% drawdown
EURAUD - 613.3% profit, 14.4 profit factor, 9.8% drawdown
AUDJPY - 1619% profit, 11.26 profit factor, 9.1% drawdown
EURJPY - 897.2% profit, 6 profit factor, 13.8% drawdown
EURGBP - 608.9% profit, 5.3 profit factor, 9.8% drawdown (NOT TOO SHABBY)
As you can clearly see above, this forex robot is projected by the Tradingview backtester to be INSANELY profitable for all common forex pairs. So what was the difference between this strategy and my previous strategies? Check my code and look for "trail_points" and "trail_offset"; you can even look them up in the PineScript v4 documentation. They specify a trailing stop as the exit condition, which automatically closes the trade if price reverses against you.
I however suspect that the backtester is not properly calculating intra-bar price movement, and is using a simplified model. With this simplfied approach, the trailing stop code becomes some sort of "holy grail" generator, making every trade entered profitable.
Risk Warning:
This is a forex trading strategy that involves high risk of equity loss, and backtest performance will not equal future results. You agree to use this script at your own risk.
Hint:
To get more realistic results, and *maybe* overcome the intrabar simulation error, change the settings to: "Stop Loss Trail Points (pips)": 100
I am not sure if this eradicates the bug, but the entries and exits look more proper, and the profit factors are more believable.
Low volatility Buy w/ TP & SL (Coinrule)The compression of volatility usually leads to expansion. When the breakout comes, it can ignite strong trends. One way to catch a coin trading in an accumulation area is to spot three moving averages with values close to each other. The strategy uses a combination of Moving Averages to spot the best time to buy a coin before its breakout.
Buy Condition
The MA200 is greater than the MA100
The MA50 is greater than the MA100
According to backtesting results, the 1-hour time frame is the best to run this strategy.
Sell Condition
Take Profit: the price increases 8% from the entry price
Stop Loss: the price drops 4% from the entry price
The strategy has a profitability of 40-60% (depending on the market conditions). Having a ratio of two between Take profit and Stop Loss helps keeping the strategy profitable in the long term.
Kairos [Backtester]Kairos bot looks for the opportune time to buy low and sell high at targets
It provides signals to open and close trades, and indicates favorable positions for a stop loss and profit taking
The Kairos bot can be used on any chart and on any time frame
---BACKTESTER---
Using the backtester script the user can look at a chart's history between selected dates to find optimal bot settings and optimal time frames
The backtester is based on general percentages for profit taking as indicated below:
-------------T1 T2 T3 T4 T5 T6 CLOSE
1 Target: 50% 50%
2 Targets: 50% 25% 25%
3 Targets: 40% 30% 20% 10%
4 Targets: 40% 25% 20% 10% 5%
5 Targets: 35% 25% 20% 10% 5% 5%
6 Targets: 30% 25% 20% 10% 5% 5% 5%
ie: If 2 targets are selected:
- 50% of investment will be taken at target 1
- 25% of investment will be taken at target 2
- and the remainder 25% will be taken when the trade is closed on a close signal
However, it is up to the user's own risk appetite to determine where and how much profit to take
Note that the backtester does not have any on screen indicators other than OPEN and CLOSE, however profit taking can be indicated by ticking the Style -> Trades on Chart tick box on the settings userform
---SIGNALS---
The signals script can be used for automation and can indicate up to 6 potential Profit Targets, as well as a Stop Loss based on how many bars back needs to be taken into consideration
The signals (Open/Close) can be automated using TradingView alerts, however the Stop Loss and Profit Taking are only indicators and are for the users own interpretation
The user does not have to place a Stop Loss or take profit at the Targets if so wished, the bot can be used to simply buy on an OPEN signal and sell on a CLOSE signal, however, the backtester will indicate that it is far more profitable to take profits.
It is advised to take profits just below indicated Targets as these are potentially high selling zones and price action can sometimes turn down just short of these targets.
---INVITE-ONLY SCRIPT---
This is an invite-only script, so if you would like to try out Kairos Bot, send me a message
kiska clouds backtest editionkiska clouds: crypto twitter's next cloud meme
Crypto is a fast-paced, highly-volatile asset, therefore, many traditional strategies are thrown out of the window when applied to cryptocurrency markets. In trading, there are only two things known for sure: price and volume . Price and volume data is then manipulated using various math equations in an effort to discover patterns and/or make predictions. kiska clouds are no different.
The kiska clouds are a simple crossover strategy. The clouds are different because of the unique averages being used and the embedded momentum indicator .
To use the clouds is simple:
When the green line crosses above the pink line, you buy/long.
When the green line crosses below the pink line, you sell/short.
The clouds are indicative of the trend's momentum. Using the power of math, the larger the cloud indicates a higher amount of buying/selling pressure. As the cloud thins, momentum is slowing, and the trend may be reversing.
At the time of testing, the strategy had a profitability of 54.55% accuracy with 1133.41% net profit. While I think this could be automated into a bot, adding a human element with stop losses and further analysis will significantly improve the accuracy/profitability.
This indicator is the backtest version of the kiska clouds (). For a trial or to purchase this indicator, send me a message on Twitter @moonkiska or here on TradingView. You will be granted a 2-3 day trial period to the backtesting strategy.
Renkomonster, v. 5.0RenkoMonster Elite
A radical redesign of our other approaches, RenkoMonster uses deep algorithmic and pattern tracking against standard systems to produce trade logic with the quietest, most actionable, signal-to-noise certainty. Designing around the right parameters brings the best results, and for investment instruments, that means focusing on market-mirroring mathematics that produce profits. Note, however, that focus on profits also means building an engine that maximizes for return, not some abstract percentage of right guesses. RenkoMonster is therefore built to enter every high-probability trade, then evaluate ongoing metrics to stay with a winner as long as possible, but also recognizing the need to exit quickly as soon as high-probability failure signals arise. The result: consistent winning trades, but, more importantly, immense profit-to-loss ratios that bring a superior payoff. RenkoMonster keeps you in the game to catch the huge runs that bring the big returns. (see Results Chart below) A premium trading system for the discerning investor seeking robust, effortless results across a range of asset classes, contact us for a free trial to test RenkoMonster on your favorite charts.
Results Chart
The backtests below show 1-year returns against a Buy and Hold (B+H) approach for 40 of the world’s top traded instruments (as measured by price volume or similar indicator). The RenkoMonster system was run on the top 10 instruments in four major markets: Equities, ETFs, Cryptocurrencies, and Forex. All tests were on a 30min chart, set to Renko “Traditional” blocks (because TradingView does not support realtime alerts from ATR-based charts). Block size was set proportionately to price to give the instrument positive results, but there was no “cherry-picking”. (In fact, changing time frames would have produced even better returns in some cases, but it seemed best to use a consistent measure.) Subscribers to the system receive a full, step-by-step breakdown on how to customize the parameters to get the best from their favorite markets.
(For each instrument, you see listed its Symbol (name), Profit % (annual return using RenkoMonster), Winning Trade % (being “right”), ProfitFactor (Ratio of money won to money lost), Max Draw Down % (point of worst % loss during the year), and B+H (how much the asset would have returned with no trades, via "buy-and-hold" )...)
RenkoMonste
Settings: 30 min, Traditional Renko box, over 1 year (June 1, 2018 - June 1, 2019)
EQUITIES
Symbol._.Profit Profit %._.Trade %._.Pf Max DrDn._.B + H
AMZN._.._.9,935._.._.._.._.66._.._..9.4._.._.._.1._.._.._.7
AAPL._.._.._318._.._.._.._.54._.._.._7.4._.._.._.2._.._.._.5
TSLA._.._32,777._.._.._.._.60._.._.._7.8._.._.._.2._.._.(-38)
FB._.._.._.._371._.._.._.._.46._.._.._5.4._.._.._.3._.._..(-7)
BABA._.._.._671._.._.._.._.55._.._.._6.3._.._.._.2._.._.(-23)
BYND._.._.._499._.._.._.._.67._.._..24._.._.._.._1._.._.140
MSFT._.._.._.271._.._.._.._.52._.._.._6.5._.._.._.2._.._..28
AMD._.._.._..539._.._.._.._.48._.._.._4.1._.._...13._.._.._0
NFLX._.._..8,695._.._.._.._.57._.._.._.7.4._.._.._2._.._.(-5)
BA._.._.._.1,448._.._.._.._..57._.._.._7.9._.._.._1._.._..97
ETFs
Symbol._.Profit %._.Trade %._.Pf._.Max DrDn._.B + H
SPY._.._.._.775._.._..66._.._..9.7._.._.1._.._.._.4
QQQ._.._.2,918._.._..66._.._.11._.._..1._.._.._.5
EEM._.._.._.463._.._..60._.._..7.1._.._.1._.._.._0
IWM._.._..2,060._.._..62._.._..9.1._.._.1._.._.(-3)
HYG._.._.._..18._.._..45._.._..2.2._.._.1._.._.._0.5
EWZ._.._.10,426._.._..62._...11._.._.._1._.._..26
LQD._.._.._..12._.._.._45._.._.2.3._.._.1._.._.._6
EFA._.._.._.236._.._.._52._.._.6.2._.._.1._.._..(-7)
FXI._.._.._..900._.._.._62._.._.8.3._.._.1._.._.(-15)
XLF._.._.._..393._.._...60._.._.6.8._.._.1._.._..(-3)
CRYPTOCURRENCIES
Symbol._.._.Profit %._.._.Trade %._.Pf._.Max DrDn._.B + H
BTC/USD._.100 million._.._.58._.._.12._.._.2._.._.._.(-8)
ETH/USD._.232 million._.._.54._.._..6.2._..4._.._.._(-54)
XRP/USD._.840 million._.._.52._.._..7._.._.4._.._.._(-21)
LTC/USD._.14 million._.._..51._.._..6.5._..5._.._.._..13
BCH/USD._.17 million._.._.51._.._..6._.._15._.._.._.(-5)
EOS/USD._.5 million._.._..49._.._..5.3._.._7._.._.._..13
BNB/USDT._.4 billion._.._..56._...15._.._..4.._.._.._128
BSV/USD._.299,000._.._.._.67._..109._.._..2._.._.._.(-8)
XLM/USD._.94 billion._.._..64._.._11._.._..3._.._.._..34
ADA/USD._.647,000._.._.._75._.._34._.._..1._.._.._.._3
FOREX PAIRS
Symbol._.._.Profit %._.Trade %._.Pf._.Max DrDn._.B + H
EUR/USD._.._.17._.._.._.37._.._.1.5._.._.2._.._.._.(-4)
USD/JPY._.._..9._.._.._..34._.._.1.2._.._.3._.._.._.(-1)
AUD/USD._.._.35._.._.._.45._.._.2.1._.._.2._.._.._.10
USD/CAD._.._.13._.._.._.35._.._.1.4._.._.3._.._.._..3
GBP/USD._.._.46._.._.._.38._.._.1.8._.._.3._.._.._.(-5)
NZD/USD._.._.39._.._.._.50._.._.2.6._.._.1._.._.._.(-6)
GBP/JPY._.._.66._.._.._.39._.._.1.9._.._.2._.._.._.(-6)
EUR/JPY._.._.25._.._.._.45._.._.2.1._.._.3._.._.._.(-5)
AUD/JPY._.._.65._.._.._.36._.._.1.9._.._.2._.._.._(-10)
EUR/GBP._.._.25._.._.._.45._.._.2.1._.._.3._.._.._..0.1
The 15 Minutes SlingShot System StrategyUse this strategy on the 15 Minutes timeframe for maximum profit. Even if the profitability is less than 60%, the profit factor is still above 5 for minimum losses which make it very profitable. The strategy is based on the SlingShot System Study.
Cyatophilum Trend Indicator [BACKTEST][STRATEGY]HOW IT WORKS
Based on my Cyatophilum Trend Indicator, this Strategy performs simple Buy and Sell orders when an alert from the Indicator triggers. The goal is to find the best Time Frame and Trend Parameter in order to make the most profit. The indicator turns the candles green for a Long trade/Buy and red for a Short trade/Sell.
ABOUT THE STRATEGY
The Net Profit (Gross profit - Gross loss) is calculated with a commission of 0.05% on each order.
Each trade is made with 1 BTC : The backtest buys 1 BTC and sells 1 BTC.
It clearly outperforms the Buy & Hold line, meaning it is more profitable to use this strategy than to just hold Bitcoin.
If you decided to Short or Long, profits can be higher, but trade at your own risk.
To use this strategy on a BTC market (for trading altcoins), change the default Order Size from 1 Contract to 100% of Equity.
MY PERSONNAL ADVICE
This is a Trend Indicator, meaning the least profitable trades are made during flat markets. Keep an eye on News and Volume to indentify a possible breakout and avoid trades during those flat periods. Do not trade during a Triangle since the commissions will rekt you.
Get This Indicator Today!
Purchase at blockchainfiesta.com
NOTE
If you purchase the Indicator you will get both the Alert Setup and the Backtest Strategy.
Find, discuss and request more backtesting on my discord!
discord.gg
CryptoMatt MT GainTrading StratThis project is focusing on the percent of profitability. Being consistently profitable is much easier on the mind when using a service to trade for you. Gains are still solid, but will continue this project to keep tweaking to be consistently profitable.
[NG] Strategy: CryptoMine - v1 - Low Drawdown - Beats Buy&Hold!So, I created this strategy that works on BTCUSD 0.28% pair along with almost all ALT-BTC pairs, and ALT-USD pairs (i.e. the cryptosphere). Here are some of the considerations I had when creating this script:
- Should work on BTCUSD 0.28% , along with most ALT-BTC and ALT-USD pairs without modifying strategy parameters for individual pairs.
- Should work with several timeframes, esp -0.67% . 15m-4hr timeframes. Better if the parameters are adjusted for these timeframes, automatically.
- Should have a small MaxDrawdown. Arguably <50% for cryptos.
- Should beat buy and hold profits for the pair.
- Should have multiple modes for switching between: Higher Returns vs Lower Drawdowns, multiple Long/Short versions - one which allows me to do margin trading by using the short calls (so, LONG, SHORT and FLAT), and another one where I can use the short calls by exiting out of the market and entering when the short call ends (so, LONG and SHORT only).
- Should have proper risk management built-in. Moreover, TakeProfit and StopLoss will be defined at a fixed 20% each, which is reasonable for crypto markets. Most strategies I see on tradingview fail on this count.
After several weeks of building such a script, and testing it successfully on multiple pairs - here are the results. :)
ETHBTC
=======================================================================================
Mode 1 (LONG and SHORT only)
--------------------------------------
First Trade: 2015-08-10 09:30, Final Profit: 1474649.65%, Drawdown: 66.21%, PF: 2.149, Trades: 1059
--------------------------------------
Mode 2 (LONG, SHORT and FLAT positions)
First Trade: 2015-08-14 05:30, Final Profit: 715274.55%, Drawdown: 36.34%, PF: 2.806, Trades: 589
BTCUSD 0.28%
=======================================================================================
Mode 1 (LONG and SHORT only)
--------------------------------------
First Trade: 2015-01-03 21:30, Final Profit: 28944.04%, Drawdown: 44.87%, PF: 1.353, Trades: 1222
--------------------------------------
Mode 2 (LONG, SHORT and FLAT positions)
First Trade: 2015-01-11 13:30, Final Profit: 22522.28%, Drawdown: 27.77%, PF: 1.877, Trades: 669
NANOBNB
=======================================================================================
Mode 1 (LONG and SHORT only)
--------------------------------------
First Trade: 2018-02-04 23:30, Final Profit: 365.5%, Drawdown: 39.4%, PF: 1.967, Trades: 51
--------------------------------------
Mode 2 (LONG, SHORT and FLAT positions)
First Trade: 2015-01-11 13:30, Final Profit: 236.91%, Drawdown: 25.62%, PF: 3.692, Trades: 25
NOTE: I will not be sharing access to this script, since market saturation is a real thing. Send me an email at menikhguptacom - if you are really interested in this script.
CETUS x BTC Filtered BUY & Sell indicatorThe Pine Script you requested is designed for CETUS coin and generates buy and sell signals while also taking BTC’s price movements into account.
In short, its purpose is: run on the CETUS chart, but filter trade signals based not only on CETUS data but also on BTC’s overall trend.
📌 Core Logic of the Pine Script
Dual Data Source
CETUS: The coin you’re actually trading.
BTC: Used as a market trend reference (trend filter).
Indicators Used
CETUS side: Technical indicators such as RSI and/or moving averages (EMA/SMA).
BTC side: Often EMA or trend direction (e.g., is BTC price above the 50 EMA?) is used as a filter.
Buy Signal
CETUS triggers a bullish signal and BTC is in a positive trend → generate a “Buy” command.
Sell Signal
CETUS triggers a bearish signal and BTC is in a negative trend → generate a “Sell” command.
Strategy Testing (Backtest)
In TradingView’s strategy tester, you can run it on historical data to check profitability, win rate, maximum drawdown, etc.
📊 What is the win rate of this code?
Profitability depends entirely on the parameters and the historical period tested.
For example:
CETUS’s high-volatility periods may yield higher profits.
Sideways or inverse BTC movement periods may reduce trade frequency.
In general, such a BTC-trend-filtered strategy produces fewer but safer trades compared to using CETUS indicators alone.
That means lower drawdowns but also lower total profit in some cases.
📌 In short: The strategy’s purpose is to filter out bad CETUS trade signals by confirming them with BTC’s trend.
Curry/Schlink AKA: The Kitchen Sink! (Enhanced) 455 BB UPDATE# The Kitchen Sink Strategy: A Story of Brotherhood and Innovation
## The Origin Story
Deep in the Pacific Northwest, at McChord Air Force Base in Washington, two aircraft maintainers were grinding through another long shift on the flight line. The massive C-17 Globemaster towered above them as they worked side by side, turning wrenches and troubleshooting systems that kept America's airlift capability running.
These weren't just any maintainers - they were brothers in arms, bound by the unique camaraderie that only comes from working on multi-million dollar aircraft where precision and attention to detail literally means life or death. One was a seasoned Crew Chief, the other an Engine troop - different specialties, but united by the same Air Force blue and the same dream of financial freedom beyond their military paychecks.
During those long nights between sorties, conversations would drift from hydraulic systems and engine diagnostics to something that fascinated them both: the financial markets. They'd watch charts on their phones during breaks, discussing price movements with the same analytical mindset they brought to aircraft troubleshooting.
**The Air Force taught them systems thinking** - how multiple components work together to create something greater than the sum of their parts. It taught them discipline, risk management, and the critical importance of checklists and procedures. Most importantly, it taught them that when lives are on the line, you don't wing it - you follow proven processes.
They realized that if they could master the complexities of keeping 585,000-pound aircraft mission-ready, they could certainly figure out how to read market patterns.
## Birth of "The Kitchen Sink"
Working in the maintenance world, they knew the value of redundancy. Aircraft have backup systems for their backup systems. So when they started developing their trading strategy, they applied the same principle: **multiple confirmation systems working together**.
They called it "The Kitchen Sink" - not because it was messy, but because they threw everything reliable into it. Just like their aircraft pre-flight checklists, every component had to work in harmony before they'd commit to a trade.
**Their mission was clear**: Create a strategy that would help fellow maintainers - Crew Chiefs, Engine troops, Avionics techs, and all the hardworking enlisted personnel - dominate the markets and build wealth beyond their military careers.
## How to Use The Kitchen Sink Strategy
### Understanding the Components
This strategy combines multiple technical indicators, just like aircraft systems work together:
**1. Triple Moving Average System (The Trend Engine)**
- Uses three different timeframe averages that must align
- Think of it like your aircraft's three-engine redundancy
- **For uptrends**: Price must be above the short average, short above medium, medium above long
- **For downtrends**: The reverse - everything cascading downward
- This creates a strong directional filter
**2. Volume Confirmation (The Power Check)**
- Requires above-average volume to validate moves
- Like checking engine power settings before takeoff
- No volume = no conviction = no trade
**3. RSI Filter (The Momentum Gauge)**
- Choose your RSI logic based on market conditions:
- **"Standard (Momentum)"**: Trade with the trend - buy strength, sell weakness
- **"Contrarian (Mean Reversion)"**: Trade pullbacks - buy dips, sell spikes
- **"Disabled"**: Remove RSI filter entirely
- Experiment to see what works for your trading style
**4. Time Filter (Operations Schedule)**
- Only trades during active market hours (default: 7 AM - 4 PM)
- Avoids low-liquidity periods
- Just like how aircraft ops follow specific schedules
### Risk Management (Mission Critical)
**ATR-Based Stop Losses and Take Profits:**
- **ATR (Average True Range)** automatically adjusts your risk based on market volatility
- Volatile markets = wider stops, Calm markets = tighter stops
- Default settings: 2.0 ATR for profits, 1.5 ATR for stops
- This gives you approximately 1.33:1 reward-to-risk ratio
**Position Sizing:**
- Default: **2% of account per trade** (much safer for beginners)
- Can be adjusted from 1-3% based on your risk tolerance and experience
- **Never risk more than you can afford to lose**
- As a maintainer, you know that safety margins save lives - same principle applies to trading
### How to Read the Signals
**Buy Signal Requirements (ALL must be true):**
- Bullish moving average alignment ✓
- Volume above average ✓
- RSI condition met (based on your selected logic) ✓
- Within trading hours ✓
- Green arrow appears below the candle
**Sell Signal Requirements (ALL must be true):**
- Bearish moving average alignment ✓
- Volume above average ✓
- RSI condition met ✓
- Within trading hours ✓
- Red arrow appears above the candle
### Pro Tips for Success
**1. Start Small**
- Paper trade first to understand the signals
- Begin with smaller position sizes until you're comfortable
**2. Backtest Different Settings**
- Try different RSI logic modes
- Adjust ATR multipliers based on your risk tolerance
- Test various timeframes to find your sweet spot
**3. Maintain Discipline**
- Follow the signals - don't second-guess the system
- Stick to your risk management rules
- Keep detailed trade logs
**4. Market Adaptation**
- Switch RSI logic based on market conditions
- Use "Standard" in trending markets
- Use "Contrarian" in ranging markets
### The Maintainer's Advantage
As military maintainers, you already have the discipline, attention to detail, and systems thinking that most traders lack. You understand:
- Following procedures saves lives (and money)
- Redundant systems prevent failures
- Regular maintenance prevents major breakdowns
- Teamwork and continuous learning are essential
**Crew Chiefs and Engine troops unite!** This strategy was built by maintainers, for maintainers. It's your ticket to financial freedom beyond the flight line.
Remember: Just like aircraft maintenance, trading is about consistency, discipline, and following proven procedures. The markets might not have Technical Orders, but this strategy is your trading manual.
*Stay disciplined, stay profitable, and may your trades always be mission-ready.*
---
**Disclaimer**: Trading involves risk. Always use proper risk management and never trade with money you can't afford to lose. This strategy was developed for educational purposes and past performance doesn't guarantee future results.
Recovery Zone Hedging [Starbots]Recovery Zone Hedging Strategy — Advanced Adaptive Hedge Recovery System
This strategy introduces an innovative zone-based hedge recovery approach tailored to TradingView’s single-direction trading model. Designed for serious traders and professionals, it combines multiple technical indicators with dynamic position sizing and adaptive take-profit mechanisms to manage drawdowns and maximize recovery efficiency.
How Recovery Zones Are Calculated
The strategy defines recovery zones as a configurable percentage distance from the last executed trade price. This percentage can be adjusted to suit different market volatility environments — wider zones for volatile assets, tighter zones for stable ones. When price moves into a recovery zone against the open position, the strategy places a hedge trade in the opposite direction to help recoup losses.
Dynamic Take-Profit Calculation
Take-profit targets are not fixed. Instead, they increase dynamically based on any accumulated losses from previous hedge trades. For example, if your initial target is 2%, but you have a $5 loss from prior hedges, the next take-profit target adjusts upward to cover both the loss and your profit goal, ensuring the entire hedge sequence closes in net profit.
Originality & Value
Unlike traditional hedging or recovery scripts that rely on static stop losses and fixed trade sizing, this strategy offers:
- Dynamic Hedge Entry Zones: Uses configurable percentage-based recovery zones that adapt to price volatility, allowing precise placement of hedge trades at meaningful reversal levels.
- Multi-Indicator Signal Fusion: Integrates MACD and Directional Movement Index (DMI) signals to confirm trade entries, improving signal accuracy and reducing false triggers.
- Exponential Position Sizing: Each hedge trade’s size grows exponentially using a customizable multiplier, accelerating loss recovery while carefully balancing capital usage.
- Adaptive Take-Profit Logic: The take-profit target adjusts dynamically based on accumulated losses and profit margins, ensuring that the entire hedge sequence closes with a net gain.
- Capital Usage Monitoring: A built-in dashboard tracks real-time equity consumption, preventing over-leveraging by highlighting critical capital thresholds.
- Fail-Safe Exit Mechanism: An optional forced exit beyond the last hedge zone protects capital in extreme market scenarios.
This strategy’s layered design and adaptive mechanisms provide a unique and powerful tool for traders seeking robust recovery systems beyond standard hedge or martingale methods.
How Components Work Together
- Entry Signals: The script listens for MACD line crossovers and DMI directional crosses to open an initial trade.
- Recovery Zones: If the market moves against the initial position, the strategy calculates a recovery zone a set percentage away and places a hedge trade in the opposite direction.
- Position Scaling: Each subsequent hedge trade increases in size exponentially according to the hedge multiplier, designed to recover all previous losses plus a profit.
- Take-Profit Target: Rather than a fixed target, the TP level is dynamically calculated considering current drawdown and desired profit margin, ensuring the entire hedge sequence closes profitably.
- Cycle Management: Trades alternate direction following the recovery zones until profit is realized or a maximum hedge count is reached. If needed, a forced stop-out limits risk exposure.
Key Benefits for Professional Traders
- Enhanced Risk Management: Real-time capital usage visualization helps maintain safe exposure levels.
- Strategic Hedge Recovery: The adaptive recovery zones and exponential sizing accelerate loss recoupment more efficiently than traditional fixed-step systems.
- Multi-Indicator Confirmation: Combining MACD and DMI reduces false signals and improves hedge timing accuracy.
- Versatility: Suitable for multiple timeframes and asset classes with adjustable parameters.
- Comprehensive Visuals: On-chart recovery zones, hedge levels, dynamic take-profits, and equity usage tables enable informed decision-making.
Recommended Settings & Use Cases
- Initial Position Size: 0.1–1% of account equity
- Recovery Zone Distance: 2–5% price movement
- Hedge Multiplier: 1.5–1.85x growth per hedge step
- Max Hedge Steps: 5–10 for controlled risk exposure
Ideal for trending markets where price retracements create viable recovery opportunities. Use caution in sideways markets to avoid extended hedge sequences.
Important Notes
- TradingView’s single-direction model means hedging is simulated via alternating trades.
- Position sizes grow rapidly—proper parameter tuning is essential to avoid over-leveraging.
This script is designed primarily for professional traders seeking an advanced, automated hedge recovery framework, offering superior capital efficiency and loss management.
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (🐑):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (💧):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (🏦):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (⚖️):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (🛡️):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
NOMANOMA Adaptive Confidence Strategy —
What is NOMA?
NOMA is a next-generation, confidence-weighted trading strategy that fuses modern trend logic, multi-factor market structure, and adaptive risk controls—delivering a systematic edge across futures, stocks, forex, and crypto markets. Designed for precision, adaptability, and hands-off automation, NOMA provides actionable trade signals and real-time alerts so you never miss a high-conviction opportunity.
Key Benefits & Why Use NOMA?
Trade With Confidence, Not Guesswork:
NOMA combines over 11 institutional-grade confirmations (market structure, order flow, volatility, liquidity, SMC/ICT concepts, and more) into a single “confidence score” engine. Every trade entry is filtered through customizable booster weights, so only the strongest opportunities trigger.
Built-In Alerts:
Get instant notifications on all entries, take-profits, trailing stop events, and exits. Connect alerts to your mobile, email, or webhook for seamless automation or just peace of mind.
Advanced Position Management:
Supports up to 5 separate take-profit levels with adjustable quantities, plus dynamic and stepwise trailing stops. Protects your gains and adapts exit logic to market movement, not just static targets.
Anti-Chop/No Trade Zones:
Eliminate low-probability, sideways market conditions using the “No Chop Zone” filter, so you only trade in meaningful, trending environments.
Full Market Session Control:
Restrict trades to custom sessions (e.g., New York hours) for added discipline and to avoid overnight risk.
— Ideal for day traders and prop-firm requirements.
Multi-Asset & Timeframe Support:
Whether you trade micro futures, stocks, forex, or crypto, NOMA adapts its TP/SL logic to ticks, pips, or points and works on any timeframe.
How NOMA Works (Feature Breakdown)
1. Adaptive Trend Engine
Uses a custom NOMA line that blends classic moving averages with dynamic momentum and a proprietary “Confidence Momentum Oscillator” overlay.
Visual trend overlay and color fill for easy chart reading.
2. Multi-Factor Confidence Scoring
Each trade is scored on up to 11 confidence “boosters,” including:
Market Manipulation & Accumulation (detects smart money traps and true range expansions)
Accumulation/Distribution (AD line)
ATR Volatility Rank (prioritizes trades when volatility is “just right”)
COG Cross (center of gravity reversal points)
Change of Character/Break of Structure (CHoCH/BOS logic, SMC/ICT style)
Order Blocks, Breakers, FVGs, Inducements, OTE (Optimal Trade Entry) Zones
You control the minimum score required for a trade to trigger, plus the weight of each factor (customize for your asset or style).
3. Smart Trade Management
Step Take-Profits:
Up to 5 profit targets, each with individual contract/quantity splits.
Step Trailing Stop:
Trail your stop with a ratcheting logic that tightens after each TP is hit, or use a fully dynamic ATR-based trail for volatile markets.
Kill-Switch:
Instant trailing stop logic closes all open contracts if price reverses sharply.
4. Session Filter & Cooldown Logic
Restricts trading to key sessions (e.g., NY open) to avoid low-liquidity or dead zones.
Cooldown bars prevent “overtrading” or rapid re-entries after an exit.
5. Chop Zone Filter
Optionally blocks trades during flat/choppy periods using a custom “NOMA spread” calculation.
When enabled, background color highlights no-trade periods for clarity.
6. Real-Time Alerts
Receive alerts for:
Trade entries (long & short, with confidence score)
Every take-profit target hit
Trailing stop exits or full position closes
Easy setup: Create alerts for all conditions and get notified instantly.
Customization & Inputs
TP/SL Modes: Choose between manual, ATR-multiplied, or hybrid take-profit and trailing logic.
Position Sizing: Fixed contracts/quantity per trade, with customizable splits for scaling out.
Session Settings: Restrict to any time window.
Confidence Engine: User-controlled weights and minimum score—tailor for your asset.
Risk & Volatility Filters: ATR length/multiplier, min/max range, and more.
How To Use
Add NOMA to your chart.
Customize your settings (session, TPs, confidence scores, etc.).
Set up TradingView alerts (“Any Alert() function call”) to receive notifications.
Monitor trade entries, profit targets, and stops directly on your chart or in your inbox.
Adjust confidence weights as you optimize for your favorite asset.
Pro Tips
Start with default settings—they are optimized for NQ micro futures, 15m timeframe.
Increase the minimum confidence score or weights for stricter filtering in volatile or low-liquidity markets.
Adjust your take-profit and trailing stop settings to match your trading style (scalping vs. swing).
Enable “No Chop Zone” during sideways conditions for cleaner signals.
Test in strategy mode before trading live to dial in your risk and settings.
Disclaimer
This script is for educational and research purposes only. No trading system guarantees future results.
Performance will vary by symbol, timeframe, and market regime—always test settings and use at your own risk. Not investment advice.
If alerts or strategy entries are not triggering as expected, try lowering the minimum confidence score or disabling certain boosters.
This will come with a user manual please do not hesitate to message me to gain access. TO THE MOON AND BEYOND
MÈGAS ALGO : MÈGAS Engine [STRATEGY]Overview
The MÈGAS Engine is an advanced algorithmic trading system that integrates a range of technical analysis tools to pinpoint high-probability opportunities in the market.
Key Features
Core Signal Generation:
-Structure Break Detection: Advanced breakout identification with adjustable
sensitivity controls
-Dual-Direction Analysis: Separate bullish and bearish signal parameters with customizable delta
thresholds and depth settings
-Dynamic Parameter Management: OverfitShield technology with pulsewave parameter cycling
to reduce overfitting risks
Filtering Alghoritm:
-Volatility Filter: Rogers-Satchell volatility estimation with RSI-based normalization to avoid
trading in unfavorable market conditions
-Volume Confirmation: Cumulative volume analysis ensuring adequate liquidity support for trade
entries
OverfitShield Method:
OverfitShield is a built-in function within the trading strategy designed to reduce overfitting bias by introducing parameter variability during execution. When the "variable" mode is activated, instead of relying on fixed values for key strategy parameters the system dynamically selects values from customizable ranges.
This approach mimics real-world market uncertainty and ensures that the strategy does not become overly dependent on a single optimal value found during backtesting — making it more robust across different market conditions and time periods.
Position Management
-Customizable Exit Set-up
The exit logic can be customized to 'CONTINUE', 'TAKE PROFIT', or 'TRAILING PROFIT' to suit
your trading approach and maximize performance.
-CONTINUE Mode:
This mode does not use predefined take profit levels. Instead, it remains in the market as long as the trend persists. By avoiding fixed exit points, this approach is often the most effective in backtesting, as it allows positions to run in favorable trends for longer periods.
-TAKE PROFIT Mode:
This mode allows you to set multiple grid-like take profit levels at different price points, effectively creating a multi-tier exit strategy. You can specify the number of profit levels you want, along with the percentage step between each level. This structured approach can be beneficial for capturing incremental profits in a trending market while allowing for more flexibility in trade management.
-TRAILING PROFIT Mode:
Similar to the Take Profit mode, this option allows you to set the trailing stop levels. The trailing stop moves with the market, ensuring that you lock in profits as the price continues to move in your favor. Once a profit level is hit, the trailing stop "follows" the price movement, adjusting dynamically to safeguard profits as the trade progresses.
3. Customizable Insight Alerts
Traders can configure personalized alert messages for every strategy action, including entries, exits, and profit targets. These alerts are fully compatible with TradingView's webhook system.
Advantages
Customization: Fully customizable exit set-up and alerts allow traders to tailor the strategy to their personal trading objectives.
How It Works — Step by Step
Step 1: Apply the Strategy
Open the chart for your selected symbol and timeframe. Add the MÈGAS Engine to the chart.
Step 2:Backtesting and Optimization
Run a full backtest and optimize the strategy parameters across the chosen trading pairs to:
Identify robust settings that perform consistently well
Avoid overfitting through validation techniques
Select the most profitable and stable configuration for live or forward testing.
Step 3: Review Results and Alerts
Check the backtest results on the chart and confirm that the custom alert messages are displaying as expected. This helps verify that everything is functioning correctly before moving forward.
Step 4: Configure Portfolio Management
Set up the exit logic based on your specific requirements. Tailor the exit strategy to match your trading approach, whether you prefer predefined take profit levels, trailing stops, or a trend-following method. This flexibility ensures the exit logic aligns with your overall strategy for optimal performance.
Open the strategy settings window. In the dedicated portfolio management section, choose your preferred capital allocation method based on your trading style and risk preferences. Once set, save the configuration as the default.
Step 5: Set Up Alerts
Click "Add Alert" on the strategy
-In the message field, use: {{strategy.order.comment}}
Under the Notifications tab:
-Enable Webhook URL
-Enter your external webhook address
-Click 'Create' to activate alerts for your strategy
Please Note:
The results and visualizations presented are derived from optimized backtesting iterations using historical and paid real-time market data sourced via TradingView. While these results are intended to demonstrate potential performance, they do not guarantee future outcomes or accuracy. Past performance is not indicative of future results, and all trading involves risk.
We strongly recommend that users review and adjust the Properties within the script settings to align with their specific account configurations and preferred trading platforms. This ensures that the strategy outputs are reflective of real-world conditions and enhances the reliability of the results obtained. Use this tool responsibly and at your own risk.
Dual MACD Strategy [Js.k]Strategy Overview
The Dual MACD Strategy leverages two MACD indicators with different parameters to generate buy and sell signals. By combining the trend-following properties of MACD with specific entry/exit criteria, this strategy aims to capture significant price movements while effectively managing risk.
Entry and Exit Conditions
Long Entry: A buy signal is triggered when:
The histogram of MACD1 crosses above zero.
The histogram of MACD2 is positive and rising.
Short Entry: A sell signal is triggered when:
The histogram of MACD1 crosses below zero.
The histogram of MACD2 is negative and declining.
Risk Management
Stop Loss and Take Profit:
Stop Loss is set at 1% below the entry price for long positions and 1% above the entry price for short positions.
Take Profit is set at 1.5% above the entry price for long positions and 1.5% below the entry price for short positions.
Position Sizing: Each trade risks a maximum of 10% of account equity, keeping potential losses manageable and in line with standard trading practices.
Backtesting Results
The strategy is tested on BTCUSDT with a time frame of 1 hour, resulting in 200+ trades.
The initial capital for backtesting is set to $10,000, with a realistic commission of 0.04% and a slippage of 2 ticks.
Conclusion
This strategy is inspired by Dreadblitz's Double MACD Buy and Sell, as well as some YouTube videos. My purpose in redeveloping them into this strategy is to validate the practicality of the Double MACD. After multiple modifications, this is the final version. I believe its profitability is limited and may lead to losses; please do not use this strategy for live trading.
magic wand STSM"Magic Wand STSM" Strategy: Trend-Following with Dynamic Risk Management
Overview:
The "Magic Wand STSM" (Supertrend & SMA Momentum) is an automated trading strategy designed to identify and capitalize on sustained trends in the market. It combines a multi-timeframe Supertrend for trend direction and potential reversal signals, along with a 200-period Simple Moving Average (SMA) for overall market bias. A key feature of this strategy is its dynamic position sizing based on a user-defined risk percentage per trade, and a built-in daily and monthly profit/loss tracking system to manage overall exposure and prevent overtrading.
How it Works (Underlying Concepts):
Multi-Timeframe Trend Confirmation (Supertrend):
The strategy uses two Supertrend indicators: one on the current chart timeframe and another on a higher timeframe (e.g., if your chart is 5-minute, the higher timeframe Supertrend might be 15-minute).
Trend Identification: The Supertrend's direction output is crucial. A negative direction indicates a bearish trend (price below Supertrend), while a positive direction indicates a bullish trend (price above Supertrend).
Confirmation: A core principle is that trades are only considered when the Supertrend on both the current and the higher timeframe align in the same direction. This helps to filter out noise and focus on stronger, more confirmed trends. For example, for a long trade, both Supertrends must be indicating a bearish trend (price below Supertrend line, implying an uptrend context where price is expected to stay above/rebound from Supertrend). Similarly, for short trades, both must be indicating a bullish trend (price above Supertrend line, implying a downtrend context where price is expected to stay below/retest Supertrend).
Trend "Readiness": The strategy specifically looks for situations where the Supertrend has been stable for a few bars (checking barssince the last direction change).
Long-Term Market Bias (200 SMA):
A 200-period Simple Moving Average is plotted on the chart.
Filter: For long trades, the price must be above the 200 SMA, confirming an overall bullish bias. For short trades, the price must be below the 200 SMA, confirming an overall bearish bias. This acts as a macro filter, ensuring trades are taken in alignment with the broader market direction.
"Lowest/Highest Value" Pullback Entries:
The strategy employs custom functions (LowestValueAndBar, HighestValueAndBar) to identify specific price action within the recent trend:
For Long Entries: It looks for a "buy ready" condition where the price has found a recent lowest point within a specific number of bars since the Supertrend turned bearish (indicating an uptrend). This suggests a potential pullback or consolidation before continuation. The entry trigger is a close above the open of this identified lowest bar, and also above the current bar's open.
For Short Entries: It looks for a "sell ready" condition where the price has found a recent highest point within a specific number of bars since the Supertrend turned bullish (indicating a downtrend). This suggests a potential rally or consolidation before continuation downwards. The entry trigger is a close below the open of this identified highest bar, and also below the current bar's open.
Candle Confirmation: The strategy also incorporates a check on the candle type at the "lowest/highest value" bar (e.g., closevalue_b < openvalue_b for buy signals, meaning a bearish candle at the low, suggesting a potential reversal before a buy).
Risk Management and Position Sizing:
Dynamic Lot Sizing: The lotsvalue function calculates the appropriate position size based on your Your Equity input, the Risk to Reward ratio, and your risk percentage for your balance % input. This ensures that the capital risked per trade remains consistent as a percentage of your equity, regardless of the instrument's volatility or price. The stop loss distance is directly used in this calculation.
Fixed Risk Reward: All trades are entered with a predefined Risk to Reward ratio (default 2.0). This means for every unit of risk (stop loss distance), the target profit is rr times that distance.
Daily and Monthly Performance Monitoring:
The strategy tracks todaysWins, todaysLosses, and res (daily net result) in real-time.
A "daily profit target" is implemented (day_profit): If the daily net result is very favorable (e.g., res >= 4 with todaysLosses >= 2 or todaysWins + todaysLosses >= 8), the strategy may temporarily halt trading for the remainder of the session to "lock in" profits and prevent overtrading during volatile periods.
A "monthly stop-out" (monthly_trade) is implemented: If the lres (overall net result from all closed trades) falls below a certain threshold (e.g., -12), the strategy will stop trading for a set period (one week in this case) to protect capital during prolonged drawdowns.
Trade Execution:
Entry Triggers: Trades are entered when all buy/sell conditions (Supertrend alignment, SMA filter, "buy/sell situation" candle confirmation, and risk management checks) are met, and there are no open positions.
Stop Loss and Take Profit:
Stop Loss: The stop loss is dynamically placed at the upTrendValue for long trades and downTrendValue for short trades. These values are derived from the Supertrend indicator, which naturally adjusts to market volatility.
Take Profit: The take profit is calculated based on the entry price, the stop loss, and the Risk to Reward ratio (rr).
Position Locks: lock_long and lock_short variables prevent immediate re-entry into the same direction once a trade is initiated, or after a trend reversal based on Supertrend changes.
Visual Elements:
The 200 SMA is plotted in yellow.
Entry, Stop Loss, and Take Profit lines are plotted in white, red, and green respectively when a trade is active, with shaded areas between them to visually represent risk and reward.
Diamond shapes are plotted at the bottom of the chart (green for potential buy signals, red for potential sell signals) to visually indicate when the buy_sit or sell_sit conditions are met, along with other key filters.
A comprehensive trade statistics table is displayed on the chart, showing daily wins/losses, daily profit, total deals, and overall profit/loss.
A background color indicates the active trading session.
Ideal Usage:
This strategy is best applied to instruments with clear trends and sufficient liquidity. Users should carefully adjust the Your Equity, Risk to Reward, and risk percentage inputs to align with their individual risk tolerance and capital. Experimentation with different ATR Length and Factor values for the Supertrend might be beneficial depending on the asset and timeframe.
EMA 12/26 With ATR Volatility StoplossThe EMA 12/26 With ATR Volatility Stoploss
The EMA 12/26 With ATR Volatility Stoploss strategy is a meticulously designed systematic trading approach tailored for navigating financial markets through technical analysis. By integrating the Exponential Moving Average (EMA) and Average True Range (ATR) indicators, the strategy aims to identify optimal entry and exit points for trades while prioritizing disciplined risk management. At its core, it is a trend-following system that seeks to capitalize on price momentum, employing volatility-adjusted stop-loss mechanisms and dynamic position sizing to align with predefined risk parameters. Additionally, it offers traders the flexibility to manage profits either by compounding returns or preserving initial capital, making it adaptable to diverse trading philosophies. This essay provides a comprehensive exploration of the strategy’s underlying concepts, key components, strengths, limitations, and practical applications, without delving into its technical code.
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Core Philosophy and Objectives
The EMA 12/26 With ATR Volatility Stoploss strategy is built on the premise of capturing short- to medium-term price trends with a high degree of automation and consistency. It leverages the crossover of two EMAs—a fast EMA (12-period) and a slow EMA (26-period)—to generate buy and sell signals, which indicate potential trend reversals or continuations. To mitigate the inherent risks of trading, the strategy incorporates the ATR indicator to set stop-loss levels that adapt to market volatility, ensuring that losses remain within acceptable bounds. Furthermore, it calculates position sizes based on a user-defined risk percentage, safeguarding capital while optimizing trade exposure.
A distinctive feature of the strategy is its dual profit management modes:
SnowBall (Compound Profit): Profits from successful trades are reinvested into the capital base, allowing for progressively larger position sizes and potential exponential portfolio growth.
ZeroRisk (Fixed Equity): Profits are withdrawn, and trades are executed using only the initial capital, prioritizing capital preservation and minimizing exposure to market downturns.
This duality caters to both aggressive traders seeking growth and conservative traders focused on stability, positioning the strategy as a versatile tool for various market environments.
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Key Components of the Strategy
1. EMA-Based Signal Generation
The strategy’s trend-following mechanism hinges on the interaction between the Fast EMA (12-period) and Slow EMA (26-period). EMAs are preferred over simple moving averages because they assign greater weight to recent price data, enabling quicker responses to market shifts. The key signals are:
Buy Signal: Triggered when the Fast EMA crosses above the Slow EMA, suggesting the onset of an uptrend or bullish momentum.
Sell Signal: Occurs when the Fast EMA crosses below the Slow EMA, indicating a potential downtrend or the end of a bullish phase.
To enhance signal reliability, the strategy employs an Anchor Point EMA (AP EMA), a short-period EMA (e.g., 2 days) that smooths the input price data before calculating the primary EMAs. This preprocessing reduces noise from short-term price fluctuations, improving the accuracy of trend detection. Additionally, users can opt for a Consolidated EMA (e.g., 18-period) to display a single trend line instead of both EMAs, simplifying chart analysis while retaining trend insights.
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2. Volatility-Adjusted Risk Management with ATR
Risk management is a cornerstone of the strategy, achieved through the use of the Average True Range (ATR), which quantifies market volatility by measuring the average price range over a specified period (e.g., 10 days). The ATR informs the placement of stop-loss levels, which are set at a multiple of the ATR (e.g., 2x ATR) below the entry price for long positions. This approach ensures that stop losses are proportionate to current market conditions—wider during high volatility to avoid premature exits, and narrower during low volatility to protect profits.
For example, if a stock’s ATR is $1 and the multiplier is 2, the stop loss for a buy at $100 would be set at $98. This dynamic adjustment enhances the strategy’s adaptability, preventing stop-outs from normal market noise while capping potential losses.
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3. Dynamic Position Sizing
The strategy calculates position sizes to align with a user-defined Risk Per Trade, typically expressed as a percentage of capital (e.g., 2%). The position size is determined by:
The available capital, which varies depending on whether SnowBall or ZeroRisk mode is selected.
The distance between the entry price and the ATR-based stop-loss level, which represents the per-unit risk.
The desired risk percentage, ensuring that the maximum loss per trade does not exceed the specified threshold.
For instance, with a $1,000 capital, a 2% risk per trade ($20), and a stop-loss distance equivalent to 5% of the entry price, the strategy computes the number of units (shares or contracts) to ensure the total loss, if the stop loss is hit, equals $20. To prevent over-leveraging, the strategy includes checks to ensure that the position’s dollar value does not exceed available capital. If it does, the position size is scaled down to fit within the capital constraints, maintaining financial discipline.
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4. Flexible Capital Management
The strategy’s dual profit management modes—SnowBall and ZeroRisk—offer traders strategic flexibility:
SnowBall Mode: By compounding profits, traders can increase their capital base, leading to larger position sizes over time. This is ideal for those with a long-term growth mindset, as it harnesses the power of exponential returns.
ZeroRisk Mode: By withdrawing profits and trading solely with the initial capital, traders protect their gains and limit exposure to market volatility. This conservative approach suits those prioritizing stability over aggressive growth.
These options allow traders to tailor the strategy to their risk tolerance, financial goals, and market outlook, enhancing its applicability across different trading styles.
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5. Time-Based Trade Filtering
To optimize performance and relevance, the strategy includes an option to restrict trading to a specific time range (e.g., from 2018 onward). This feature enables traders to focus on periods with favorable market conditions, avoid historically volatile or unreliable data, or align the strategy with their backtesting objectives. By confining trades to a defined timeframe, the strategy ensures that performance metrics reflect the intended market context.
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Strengths of the Strategy
The EMA 12/26 With ATR Volatility Stoploss strategy offers several compelling advantages:
Systematic and Objective: By adhering to predefined rules, the strategy eliminates emotional biases, ensuring consistent execution across market conditions.
Robust Risk Controls: The combination of ATR-based stop losses and risk-based position sizing caps losses at user-defined levels, fostering capital preservation.
Customizability: Traders can adjust parameters such as EMA periods, ATR multipliers, and risk percentages, tailoring the strategy to specific markets or preferences.
Volatility Adaptation: Stop losses that scale with market volatility enhance the strategy’s resilience, accommodating both calm and turbulent market phases.
Enhanced Visualization: The use of color-coded EMAs (green for bullish, red for bearish) and background shading provides intuitive visual cues, simplifying trend and trade status identification.
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Limitations and Considerations
Despite its strengths, the strategy has inherent limitations that traders must address:
False Signals in Range-Bound Markets: EMA crossovers may generate misleading signals in sideways or choppy markets, leading to whipsaws and unprofitable trades.
Signal Lag: As lagging indicators, EMAs may delay entry or exit signals, causing traders to miss rapid trend shifts or enter trades late.
Overfitting Risk: Excessive optimization of parameters to fit historical data can impair the strategy’s performance in live markets, as past patterns may not persist.
Impact of High Volatility: In extremely volatile markets, wider stop losses may result in larger losses than anticipated, challenging risk management assumptions.
Data Reliability: The strategy’s effectiveness depends on accurate, continuous price data, and discrepancies or gaps can undermine signal accuracy.
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Practical Applications
The EMA 12/26 With ATR Volatility Stoploss strategy is versatile, applicable to diverse markets such as stocks, forex, commodities, and cryptocurrencies, particularly in trending environments. To maximize its potential, traders should adopt a rigorous implementation process:
Backtesting: Evaluate the strategy’s historical performance across various market conditions to assess its robustness and identify optimal parameter settings.
Forward Testing: Deploy the strategy in a demo account to validate its real-time performance, ensuring it aligns with live market dynamics before risking capital.
Ongoing Monitoring: Continuously track trade outcomes, analyze performance metrics, and refine parameters to adapt to evolving market conditions.
Additionally, traders should consider market-specific factors, such as liquidity and volatility, when applying the strategy. For instance, highly liquid markets like forex may require tighter ATR multipliers, while less liquid markets like small-cap stocks may benefit from wider stop losses.
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Conclusion
The EMA 12/26 With ATR Volatility Stoploss strategy is a sophisticated, systematic trading framework that blends trend-following precision with disciplined risk management. By leveraging EMA crossovers for signal generation, ATR-based stop losses for volatility adjustment, and dynamic position sizing for risk control, it offers a balanced approach to capturing market trends while safeguarding capital. Its flexibility—evident in customizable parameters and dual profit management modes—makes it suitable for traders with varying risk appetites and objectives. However, its limitations, such as susceptibility to false signals and signal lag, necessitate thorough testing and prudent application. Through rigorous backtesting, forward testing, and continuous refinement, traders can harness this strategy to achieve consistent, risk-adjusted returns in trending markets, establishing it as a valuable tool in the arsenal of systematic trading.
SuperTrade ST1 StrategyOverview
The SuperTrade ST1 Strategy is a long-only trend-following strategy that combines a Supertrend indicator with a 200-period EMA filter to isolate high-probability bullish trade setups. It is designed to operate in trending markets, using volatility-based exits with a strict 1:4 Risk-to-Reward (R:R) ratio, meaning that each trade targets a profit 4× the size of its predefined risk.
This strategy is ideal for traders looking to align with medium- to long-term trends, while maintaining disciplined risk control and minimal trade frequency.
How It Works
This strategy leverages three key components:
Supertrend Indicator
A trend-following indicator based on Average True Range (ATR).
Identifies bullish/bearish trend direction by plotting a trailing stop line that moves with price volatility.
200-period Exponential Moving Average (EMA) Filter
Trades are only taken when the price is above the EMA, ensuring participation only during confirmed uptrends.
Helps filter out counter-trend entries during market pullbacks or ranges.
ATR-Based Stop Loss and Take Profit
Each trade uses the ATR to calculate volatility-adjusted exit levels.
Stop Loss: 1× ATR below entry.
Take Profit: 4× ATR above entry (1:4 R:R).
This asymmetry ensures that even with a lower win rate, the strategy can remain profitable.
Entry Conditions
A long trade is triggered when:
Supertrend flips from bearish to bullish (trend reversal).
Price closes above the Supertrend line.
Price is above the 200 EMA (bullish market bias).
Exit Logic
Once a long position is entered:
Stop loss is set 1 ATR below entry.
Take profit is set 4 ATR above entry.
The strategy automatically exits the position on either target.
Backtest Settings
This strategy is configured for realistic backtesting, including:
$10,000 account size
2% equity risk per trade
0.1% commission
1 tick slippage
These settings aim to simulate real-world conditions and avoid overly optimistic results.
How to Use
Apply the script to any timeframe, though higher timeframes (1H, 4H, Daily) often yield more reliable signals.
Works best in clearly trending markets (especially in crypto, stocks, indices).
Can be paired with alerts for live trading or analysis.
Important Notes
This version is long-only by design. No short positions are executed.
Ideal for swing traders or position traders seeking asymmetric returns.
Users can modify the ATR period, Supertrend factor, or EMA filter length based on asset behavior.
