Optimal Length BackTester [YinYangAlgorithms]This Indicator allows for a ‘Optimal Length’ to be inputted within the Settings as a Source. Unlike most Indicators and/or Strategies that rely on either Static Lengths or Internal calculations for the length, this Indicator relies on the Length being derived from an external Indicator in the form of a Source Input.
This may not sound like much, but this application may allows limitless implementations of such an idea. By allowing the input of a Length within a Source Setting you may have an ‘Optimal Length’ that adjusts automatically without the need for manual intervention. This may allow for Traditional and Non-Traditional Indicators and/or Strategies to allow modifications within their settings as well to accommodate the idea of this ‘Optimal Length’ model to create an Indicator and/or Strategy that adjusts its length based on the top performing Length within the current Market Conditions.
This specific Indicator aims to allow backtesting with an ‘Optimal Length’ inputted as a ‘Source’ within the Settings.
This ‘Optimal Length’ may be used to display and potentially optimize multiple different Traditional Indicators within this BackTester. The following Traditional Indicators are included and available to be backtested with an ‘Optimal Length’ inputted as a Source in the Settings:
Moving Average; expressed as either a: Simple Moving Average, Exponential Moving Average or Volume Weighted Moving Average
Bollinger Bands; expressed based on the Moving Average Type
Donchian Channels; expressed based on the Moving Average Type
Envelopes; expressed based on the Moving Average Type
Envelopes Adjusted; expressed based on the Moving Average Type
All of these Traditional Indicators likewise may be displayed with multiple ‘Optimal Lengths’. They have the ability for multiple different ‘Optimal Lengths’ to be inputted and displayed, such as:
Fast Optimal Length
Slow Optimal Length
Neutral Optimal Length
By allowing for the input of multiple different ‘Optimal Lengths’ we may express the ‘Optimal Movement’ of such an expressed Indicator based on different Time Frames and potentially also movement based on Fast, Slow and Neutral (Inclusive) Lengths.
This in general is a simple Indicator that simply allows for the input of multiple different varieties of ‘Optimal Lengths’ to be displayed in different ways using Tradition Indicators. However, the idea and model of accepting a Length as a Source is unique and may be adopted in many different forms and endless ideas.
Tutorial:
You may add an ‘Optimal Length’ within the Settings as a ‘Source’ as followed in the example above. This Indicator allows for the input of a:
Neutral ‘Optimal Length’
Fast ‘Optimal Length’
Slow ‘Optimal Length’
It is important to account for all three as they generally encompass different min/max length values and therefore result in varying ‘Optimal Length’s’.
For instance, say you’re calculating the ‘Optimal Length’ and you use:
Min: 1
Max: 400
This would therefore be scanning for 400 (inclusive) lengths.
As a general way of calculating you may assume the following for which lengths are being used within an ‘Optimal Length’ calculation:
Fast: 1 - 199
Slow: 200 - 400
Neutral: 1 - 400
This allows for the calculation of a Fast and Slow length within the predetermined lengths allotted. However, it likewise allows for a Neutral length which is inclusive to all lengths alloted and may be deemed the ‘Most Accurate’ for these reasons. However, just because the Neutral is inclusive to all lengths, doesn’t mean the Fast and Slow lengths are irrelevant. The Fast and Slow length inputs may be useful for seeing how specifically zoned lengths may fair, and likewise when they cross over and/or under the Neutral ‘Optimal Length’.
This Indicator features the ability to display multiple different types of Traditional Indicators within the ‘Display Type’.
We will go over all of the different ‘Display Types’ with examples on how using a Fast, Slow and Neutral length would impact it:
Simple Moving Average:
In this example above have the Fast, Slow and Neutral Optimal Length formatted as a Slow Moving Average. The first example is on the 15 minute Time Frame and the second is on the 1 Day Time Frame, demonstrating how the length changes based on the Time Frame and the effects it may have.
Here we can see that by inputting ‘Optimal Lengths’ as a Simple Moving Average we may see moving averages that change over time with their ‘Optimal Lengths’. These lengths may help identify Support and/or Resistance locations. By using an 'Optimal Length' rather than a static length, we may create a Moving Average which may be more accurate as it attempts to be adaptive to current Market Conditions.
Bollinger Bands:
Bollinger Bands are a way to see a Simple Moving Average (SMA) that then uses Standard Deviation to identify how much deviation has occurred. This Deviation is then Added and Subtracted from the SMA to create the Bollinger Bands which help Identify possible movement zones that are ‘within range’. This may mean that the price may face Support / Resistance when it reaches the Outer / Inner bounds of the Bollinger Bands. Likewise, it may mean the Price is ‘Overbought’ when outside and above or ‘Underbought’ when outside and below the Bollinger Bands.
By applying All 3 different types of Optimal Lengths towards a Traditional Bollinger Band calculation we may hope to see different ranges of Bollinger Bands and how different lookback lengths may imply possible movement ranges on both a Short Term, Long Term and Neutral perspective. By seeing these possible ranges you may have the ability to identify more levels of Support and Resistance over different lengths and Trading Styles.
Donchian Channels:
Above you’ll see two examples of Machine Learning: Optimal Length applied to Donchian Channels. These are displayed with both the 15 Minute Time Frame and the 1 Day Time Frame.
Donchian Channels are a way of seeing potential Support and Resistance within a given lookback length. They are a way of withholding the High’s and Low’s of a specific lookback length and looking for deviation within this length. By applying a Fast, Slow and Neutral Machine Learning: Optimal Length to these Donchian Channels way may hope to achieve a viable range of High’s and Low’s that one may use to Identify Support and Resistance locations for different ranges of Optimal Lengths and likewise potentially different Trading Strategies.
Envelopes / Envelopes Adjusted:
Envelopes are an interesting one in the sense that they both may be perceived as useful; however we deem that with the use of an ‘Optimal Length’ that the ‘Envelopes Adjusted’ may work best. We will start with examples of the Traditional Envelope then showcase the Adjusted version.
Envelopes:
As you may see, a Traditional form of Envelopes even produced with a Machine Learning: Optimal Length may not produce optimal results. Unfortunately this may occur with some Traditional Indicators and they may need some adjustments as you’ll notice with the ‘Envelopes Adjusted’ version. However, even without the adjustments, these Envelopes may be useful for seeing ‘Overbought’ and ‘Oversold’ locations within a Machine Learning: Optimal Length standpoint.
Envelopes Adjusted:
By adding an adjustment to these Envelopes, we may hope to better reflect our Optimal Length within it. This is caused by adding a ratio reflection towards the current length of the Optimal Length and the max Length used. This allows for the Fast and Neutral (and potentially Slow if Neutral is greater) to achieve a potentially more accurate result.
Envelopes, much like Bollinger Bands are a way of seeing potential movement zones along with potential Support and Resistance. However, unlike Bollinger Bands which are based on Standard Deviation, Envelopes are based on percentages +/- from the Simple Moving Average.
We will conclude our Tutorial here. Hopefully this has given you some insight into how useful adding a ‘Optimal Length’ within an external (secondary) Indicator as a Source within the Settings may be. Likewise, how useful it may be for automation sake in the sense that when the ‘Optimal Length’ changes, it doesn’t rely on an alert where you need to manually update it yourself; instead it will update Automatically and you may reap the benefits of such with little manual input needed (aside from the initial setup).
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Cari dalam skrip untuk "backtesting"
Captain Backtest Model [TFO]Created by @imjesstwoone and @mickey1984, this trade model attempts to capture the expansion from the 10:00-14:00 EST 4h candle using just 3 simple steps. All of the information presented in this description has been outlined by its creators, all I did was translate it to Pine Script. All core settings of the trade model may be edited so that users can test several variations, however this description will cover its default, intended behavior using NQ 5m as an example.
Step 1 is to identify our Price Range. In this case, we are concerned with the highest high and the lowest low created from 6:00-10:00 EST.
Step 2 is to wait for either the high or low of said range to be taken out. Whichever side gets taken first determines the long/short bias for the remainder of the Trade Window (i.e. if price takes the range high, bias is long, and vice versa). Bias must be determined by 11:15 EST, otherwise no trades will be taken. This filter is intended to weed out "choppy" trading days.
Step 3 is to wait for a retracement and enter with a close through the previous candle's high (if long biased) or low (if short biased). There are a couple toggleable criteria that we use to define a retracement; one is checking for opposite close candles that indicate a pullback; another is checking if price took the previous candle's low (if long biased) or high (if short biased).
This trade model was initially tested for index futures, particularly ES and NQ, using a 5m chart, however this indicator allows us to backtest any symbol on any timeframe. Creators @imjesstwoone and @mickey1984 specified a 5 point stop loss on ES and a 25 point stop loss on NQ with their testing.
I've personally found some success in backtesting NQ 5m using a 25 point stop loss and 75 point profit target (3:1 R). Enabling the Use Fixed R:R parameter will ensure that these stops and targets are utilized, otherwise it will enter and hold the position until the close of the Trade Window.
Backtest Strategy Optimizer AdapterBacktest Strategy Optimizer Adapter
With this library, you will be able to run one or multiple backtests with different variables (combinations). For example, you can run 100 backtests of Supertrend at once with an increment factor of 0.1. This way, you can easily fetch the most profitable settings and apply them to your strategy.
To get a better understanding of the code, you can check the code below.
Single backtest results
= backtest.results(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Add backtest results to a table
backtest.table(initial_capital, profit_and_loss, open_balance, winrate, entries, exits, wins, losses, backtest_table_position, backtest_table_margin, backtest_table_transparency, backtest_table_cell_color, backtest_table_title_cell_color, backtest_table_text_color)
Backtest result without chart labels
= backtest.run(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result profit
profit = backtest.profit(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result winrate
winrate = backtest.winrate(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Start Date
You can set the start date either by using a timestamp or a number that refers to the number of bars back.
Stop Loss / Take Profit Issue
Unfortunately, I did not manage to achieve 100% accuracy for the take profit and stop loss. The original TradingView backtest can stop at the correct position within a bar using the strategy.exit stop and limit variables. However, it seems unachievable with a crossunder/crossover function in PineScript unless it is calculated on every tick (which would make the backtesting results invalid). So far, I have not found a workaround, and I would be grateful if someone could solve this issue, if it is even possible. If you have any solutions or fixes, please let me know!
Multiple Backtest Results / Optimizer
You can run multiple backtests in a single strategy or indicator, but there are certain requirements for placing the correct code in the right way. To view examples of running multiple backtests, you can refer to the links provided in the updates I posted below. In the samples I have also explained how you can auto-generate code for your backtest strategy.
HK Percentile Interpolation One
This script is designed to execute a trading strategy based on Heikin Ashi candlesticks, moving averages, and percentile levels.
Please note that you should keep your original chart in normal candlestick mode and not switch it to Heikin Ashi mode. The script itself calculates Heikin Ashi values from regular candlesticks. If your chart is already in Heikin Ashi mode, the script would be calculating Heikin Ashi values based on Heikin Ashi values, which would produce incorrect results.
The strategy begins trading from a start date that you can specify by modifying the `startDate` parameter. The format of the date is "YYYY MM DD". So, for example, to start the strategy from January 1, 2022, you would set `startDate = timestamp("2022 01 01")`.
The script uses Heikin Ashi candlesticks, which are plotted in the chart. This approach can be useful for spotting trends and reversals more easily than with regular candlestick charts. This is particularly useful when backtesting in TradingView's "Rewind" mode, as you can see how the Heikin Ashi candles behaved at each step of the strategy.
Buy and sell signals are generated based on two factors:
1. The crossing over or under of the Heikin Ashi close price and the 75th percentile price level.
2. The Heikin Ashi close price being above certain moving averages.
You have the flexibility to adjust several parameters in the script, including:
1. The stop loss and trailing stop percentages (`stopLossPercentage` and `trailStopPercentage`). These parameters allow the strategy to exit trades if the price moves against you by a certain percentage.
2. The lookback period (`lookback`) used to calculate percentile levels. This determines the range of past bars used in the percentile calculation.
3. The lengths of the two moving averages (`yellowLine_length` and `purplLine_length`). These determine how sensitive the moving averages are to recent price changes.
4. The minimum holding period (`holdPeriod`). This sets the minimum number of bars that a trade must be kept open before it can be closed.
Please adjust these parameters according to your trading preferences and risk tolerance. Happy trading!
Rocket Grid Algorithm - The Quant ScienceThe Rocket Grid Algorithm is a trading strategy that enables traders to engage in both long and short selling strategies. The script allows traders to backtest their strategies with a date range of their choice, in addition to selecting the desired strategy - either SMA Based Crossunder or SMA Based Crossover.
The script is a combination of trend following and short-term mean reversing strategies. Trend following involves identifying the current market trend and riding it for as long as possible until it changes direction. This type of strategy can be used over a medium- to long-term time horizon, typically several months to a few years.
Short-term mean reversing, on the other hand, involves taking advantage of short-term price movements that deviate from the average price. This type of strategy is usually applied over a much shorter time horizon, such as a few days to a few weeks. By rapidly entering and exiting positions, the strategy seeks to capture small, quick gains in volatile market conditions.
Overall, the script blends the best of both worlds by combining the long-term stability of trend following with the quick gains of short-term mean reversing, allowing traders to potentially benefit from both short-term and long-term market trends.
Traders can configure the start and end dates, months, and years, and choose the length of the data they want to work with. Additionally, they can set the percentage grid and the upper and lower destroyers to manage their trades effectively. The script also calculates the Simple Moving Average of the chosen data length and plots it on the chart.
The trigger for entering a trade is defined as a crossunder or crossover of the close price with the Simple Moving Average. Once the trigger is activated, the script calculates the total percentage of the side and creates a grid range. The grid range is then divided into ten equal parts, with each part representing a unique grid level. The script keeps track of each grid level, and once the close price reaches the grid level, it opens a trade in the specified direction.
The equity management strategy in the script involves a dynamic allocation of equity to each trade. The first order placed uses 10% of the available equity, while each subsequent order uses 1% less of the available equity. This results in the allocation of 9% for the second order, 8% for the third order, and so on, until a maximum of 10 open trades. This approach allows for risk management and can help to limit potential losses.
Overall, the Rocket Grid Algorithm is a flexible and powerful trading strategy that can be customized to meet the specific needs of individual traders. Its user-friendly interface and robust backtesting capabilities make it an excellent tool for traders looking to enhance their trading experience.
iMoku (Ichimoku Complete Tool) - The Quant Science iMoku™ is a professional all-in-one solution for the famous Ichimoku Kinko Hyo indicator.
The algorithm includes:
1. Backtesting spot
2. Visual tool
3. Auto-trading functions
With iMoku you can test four different strategies.
Strategy 1: Cross Tenkan Sen - Kijun Sen
A long position is opened with 100% of the invested capital ($1000) when "Tenkan Sen" crossover "Kijun Sen".
Closing the long position on the opposite condition.
There are 3 different strength signals for this strategy: weak, normal, strong.
Weak : the signal is weak when the condition is true and the price is above the 'Kumo'
Normal : the signal is normal when the condition is true and the price is within the 'Kumo'
Strong : the signal is strong when the condition is true and the price is below the 'Kumo'
Strategy 2: Cross Price - Kijun Sen
A long position is opened with 100% of the invested capital ($1000) when the price crossover the 'Kijun Sen'.
Closing the long position on the opposite condition.
There are 3 different strength signals for this strategy: weak, normal, strong.
Weak : the signal is weak when the condition is true and the price is above the 'Kumo'
Normal : the signal is normal when the condition is true and the price is inside the 'Kumo'
Strong : the signal is strong when the condition is true and the price is below the 'Kumo'
Strategy 3: Kumo Breakout
A long position is opened with 100% of the invested capital ($1000) when the price breakup the 'Kumo'.
Closing the long position with a percentage stop loss and take profit on the invested capital.
Strategy 4: Kumo Twist
A long position is opened with 100% of the invested capital ($1000) when the 'Kumo' goes from negative to positive (called "Twist").
Closing the long position on the opposite condition.
There are 2 different strength signals for this strategy: weak, and strong.
Weak : the signal is weak when the condition is true and the price is above the 'Kumo'
Strong : the signal is strong when the condition is true and the price is below the 'Kumo'
This script is compliant with algorithmic trading.
You can use this script with trading terminals such as 3Commas or CryptoHopper. Connecting this script is very easy.
1. Enter the user interface
2. Select and activate a strategy
3. Copy your bot's links into the dedicated fields
4. Create and activate alert
Disclaimer: algorithmic trading involves risk, the user should consider aspects such as slippage, liquidity and costs when evaluating an asset. The Quant Science is not responsible for any kind of damage resulting from use of this script. By using this script you take all the responsibilities and risks.
Month/Month Percentage % Change, Historical; Seasonal TendencyTable of monthly % changes in Average Price over the last 10 years (or the 10 yrs prior to input year).
Useful for gauging seasonal tendencies of an asset; backtesting monthly volatility and bullish/bearish tendency.
~~User Inputs~~
Choose measure of average: sma(close), sma(ohlc4), vwap(close), vwma(close).
Show last 10yrs, with 10yr average % change, or to just show single year.
Chose input year; with the indicator auto calculating the prior 10 years.
Choose color for labels and size for labels; choose +Ve value color and -Ve value color.
Set 'Daily bars in month': 21 for Forex/Commodities/Indices; 30 for Crypto.
Set precision: decimal places
~~notes~~
-designed for use on Daily timeframe (tradingview is buggy on monthly timeframe calculations, and less precise on weekly timeframe calculations).
-where Current month of year has not occurred yet, will print 9yr average.
-calculates the average change of displayed month compared to the previous month: i.e. Jan22 value represents whole of Jan22 compared to whole of Dec21.
-table displays on the chart over the input year; so for ES, with 2010 selected; shows values from 2001-2010, displaying across 2010-2011 on the chart.
-plots on seperate right hand side scale, so can be shrunk and dragged vertically.
-thanks to @gabx11 for the suggestion which inspired me to write this
Gann HiLo Activator Strategy█ OVERVIEW
Strategy based on the Gann Hilo Activator . This is a trend following strategy, which means it will go long (and close the previous short position) once the price closes above the high SMA, and go short (and close the previous long position) once the price closes below the low SMA.
█ PARAMETERS
- Length
- Displace (or offset): default is 1
- Begin from start: strategy will run since the beggining
- From year, month, day: Choose an specific date to start backtesting (must disable the parameter above to work)
█ HOW TO USE
After choosing the start date to run the strategy, you can change the length field and look at the backtest results to find the most optimal settings for the current symbol.
This strategy was tested on the stock and crypto market with good results. Hope you enjoy!
WhaleCrew BacktesterBacktesting indicators is easy , just add the following line of code to your script:
plot(longEntry ? 1 : shortEntry ? -1 : longTP ? 0.5 : shortTP ? -0.5 : 0, color=na, editable=false, title='Backtest')
These numbers are defined as constants in the backtester source-code.
After adding this indicator to your chart:
1. Open Settings
2. Select supported indicator to backtest
3. Select if you want to enter Longs and/or Shorts
4. Open the 'Strategy Tester' at the bottom to check the performance
Remember:
past performance is not indicative of future results
repainting indicators will create wrong/unrealistic results
EMR Strategy [H1 Backtesting]EMR Strategy base on EMA, MACD and RSI to supply signal on time frame H1.
Details of Rule as below:
===
1.EMA
+ Time frame: H1
+ Periods: 25, 100 (~ EMA 25 H4), 600 (~ EMA 25 D1)
===
2.MACD
+ Time frame: H1
+ Periods: 12,26,9
===
3.RSI
+ Time frame: H1
+ Periods: 14
===
4.Trading Rule
4.1.Long Position
+ MACD>0 and RSI>50 and close price moving above EMA 25
+ Close price crossed EMA 100 or crossed EMA 600 at the first time
4.2.Short Position
+ MACD<0 and RSI<50 and close price moving below EMA 25
+ Close price crossed EMA 100 or crossed EMA 600 at the first time
===
5.Money Management
+ This strategy concentrate into winrate.
+ So use trailing stop to protect your profits.
+ And use stoploss to avoid big loss on trades.
Inverse Fisher Transform on RSI for backtest w/alertsThis version of the Inverse Fisher Transform on RSI comes with support for
1) Backtesting with Gavin's backtest script
2) Bypass, you can use another indicator to pause buy signals from this indicator. Just create another indicator that plots "1" whenever you want to activate the bypass on the IFTRSI signal.
3) Independent buy and sell level thresholds. Some tokens perform better with a higher sell level, even levels as high as 0.996, sometimes the buy level can also be relaxed to even 0.6 and get incredible results on the 5 minute chart.
4) alerts for Buy and Sell signals
Make sure you add Gavin's backtest and select external signal and this indicator as the source.
72s Strat: Backtesting Adaptive HMA+ pt.1This is a follow up to my previous publication of Adaptive HMA+ few months ago, as a mean to provide some kind of initial backtesting tools. Which can be use to explore many possible strategies, optimise its settings to better conform user's pair/tf, and hopefully able to help tweaking your general strategy.
If you haven't read the study or use the indicator, kindly go here first to get the overall idea.
The first strategy introduce in this backtest is one most basic already described in the study; buy/sell is when movement is there and everything is on the right side; When RSI has turned to other side, we can use it as exit point (if in profit of course, else just let it hit our TP/SL, why would we exit before profit). Also, base on RSI when we make entry, we can further differentiate type of signals. --Please check all comments in code directly where the signals , entries , and exits section are.
Second additional strategy to check; is when we also use second faster Adaptive HMA+ for exit. So this is like a double orders on a signal but with different exit-rule (/more on this on snapshots below). Alternatively, you can also work the code so to only use this type of exit.
There's also an additional feature which you can enable its visuals, the Distance Zone , is to help measuring price distance to our xHMA+. It's just a simple atr based envelope really, I already put the sample code in study's comment section, but better gonna update it there directly for non-coder too, after this.
In this sample I use Lot for order quantity size just because that's what I use on my broker. Also what few friends use while we forward-testing it since the study is published, so we also checked/compared each profit/loss report by real number. To use default or other unit of measurement, change the entry code accordingly.
If you change your order size, you should also change the commission in Properties Tab. My broker commission is 5 USD per order/lot, so in there with example order size 0.1 lot I put commission 0.5$ per order (I'll put 2.5$ for 0.5 lot, 10$ for 2 lot, and so on). Crypto usually has higher charge. --It is important that you should fill it base on your broker.
SETTINGS
I'm trying to keep it short. Please explore it further again. (Beginner should also first get acquaintance with terms use here.)
ORDERS:
Base Minimum Profit Before Exit:
The number is multiplier of ongoing ATR. Means that when basic exit condition is met, algo will check whether you're already in minimum profit or not, if not, let it still run to TP or SL, or until it meets subsequent exit condition, then it will check again.
Default Target Profit:
Multiplier of ATR at signal. If reached before any eligible exit condition is met, exit TP.
Base StopLoss Point:
You can change directly in code to use other like ATR Trailing SL, fix percent SL, or whatever. In the sample, 4 options provided.
Maximum StopLoss:
This is like a safety-net, that if at some point your chosen SL point from input above happens to be exceeding this maximum input that you can tolerate, then this max point is the one will be use as SL.
Activate 2nd order...:
The additional doubling of certain buy/sell with different exits as described above. If enable, you should also set pyramiding to at least: 2. If not, it does nothing.
ADAPTIVE HMA+ PERIOD
Many users already have their own settings for these. So in here I only sample the default as first presented in the study. Make it to your adaptive.
MARKET MOVEMENT
(1) Now you can check in realtime how much slope degree is best to define your specific pair/tf is out of congestion (yellow) area. And (2) also able to check directly what ATR lengths are more suitable defining your pair's volatility.
DISTANCE ZONE
Distance Multiplier. Each pair/tf has its own best distance zone (in xHMA+ perspective). The zone also determine whether a signal should appear or not. (Or what type of signal, if you wanna go more detail in constructing your strategy)
USAGE
(Provided you already have your own comfortable settings for minimum-maximum period of Adaptive HMA+. Best if you already have backtested it manually too and/or apply as an add-on to your working strategy)
1. In our experiences, first most important to define is both elements in the Market Movement Settings . These also tend to be persistent for whole season since it's kinda describing that pair/tf overall behaviour. Don't worry if you still get a low Profit Factor here, but by tweaking you should start to see positive changes in one of Max Drawdown and Net Profit, or Percent Profitable.
2. Afterwards, find your pair/tf Distance Zone . When optimising this, what we seek is just a "not to bad" equity curves to start forming. At least Max Drawdown should lessen more. Doesn't have to be great already, but should be better, no red in Net Profit.
3. Then go manage the "Trailing Minimum Profit", TP, SL, and max SL.
4. Repeat 1,2,3. 👻
5. Manage order size, commission, and/or enable double-order (need pyramiding) if you like. Check if your equity can handle max drawdown before margin call.
6. After getting an acceptable backtest result, go to List of Trades tab and find the biggest loss or when many sequencing loss in a row happened. Click on it to go to exact point on chart, observe why the signal failed and get at least general idea how it can be prevented . The rest is yours, you should know your pair/tf more than other.
You can also re-explore your minimum-maximum period for both Major and minor xHMA+.
Keep in mind that all numbers in Setting are conceptually in a form of range . You don't want to get superb equity curves but actually a "fragile" , means one can easily turn it to disaster just by changing only a fraction in one/two of the setting.
---
If you just wanna test the strength of the indicator alone, you can disable "Use StopLoss" temporarily while optimising settings.
Using no SL might be tempting in overall result data in some cases, but NOTE: It is not recommended to not using SL, don't forget that we deliberately enter when it's in high volatility. If want to add flexibility or trading for long-term, just maximise your SL. ie.: chose SL Point>ATR only and set it maximum. (Check your max drawdown after this).
I think this is quite important specially for beginners, so here's an example; Hypothetically in below scenario, because of some settings, the buy order after the loss sell signal didn't appear. Let's say if our initial capital only 1000$ using leverage and order size 0,5 lot (risky position sizing already), moreover if this happens at the beginning of your trading season, that's half of account gone already in one trade . Your max SL should've made you exit after that pumping bar.
The Trailing Minimum Profit is actually look like this. Search in the code if you want to plot it. I just don't like too many lines on chart.
To maximise profit we can try enabling double-order. The only added rule coded is: RSI should rising when buy and falling when sell. 2nd signal will appears above or below default buy/sell signal. (Of course it's also prone to double-loss, re-check your max drawdown after. Profit factor play its part in here for a long run). Snapshot in comparison:
Two default sell signals on left closed at RSI exit, the additional sell signal closed later on when price crossover minor xHMA+. On buy side, price haven't met our minimum profit when first crossunder minor xHMA+. If later on we hit SL on this "+buy" signal, at least we already profited from default buy signal. You can also consider/treat this as multiple TP points.
For longer-term trading, what you need to maximise is the Minimum Profit , so it won't exit whenever an exit condition happened, it can happen several times before reaching minimum profit. Hopefully this snapshot can explain:
Notice in comparison default sell and buy signal now close in average after 3 days. What's best is when we also have confirmation from higher TF. It's like targeting higher TF by entering from smaller TF.
As also mention in the study, we can still experiment via original HMA by putting same value for minimum-maximum period setting. This is experimental EU 1H with Major xHMA+: 144-144, Flat market 13, Distance multiplier 3.6, with 2nd order activated.
Kiwi was a bit surprising for me. It's flat market is effectively below 6, with quite far distance zone of 3.5. Probably because I'm using big numbers in adaptive period.
---
The result you see in strategy tester report below for EURUSD 15m is using just default settings you see in code, as follow:
0,1 lot for each order (which is the smallest allowed by my broker).
No pyramiding. Commission: 0.5 usd per order. Slippage: 3
Opening position is only using basic strategy #1 (RSI exit). Additional exit not activated.
Minimum Profit: 1. TP: 3.
SL use: Half-distance zone. Max SL: 4.5.
Major xHMA+: 172-233. minor xHMA+: 89-121
Distance Zone Multiplier: 2.7
RSI: Standard 14.
(From our forward-testing, the difference we get from net profit is because of the spread, our entry isn't exactly at the close/open price. Not so much though, but not the same. If somebody can direct me to any example where we can code our entry via current bid/ask price, that would be awesome!)
It's already a long post (sorry), think I'm gonna pause here. Check out the code :)
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DISCLAIMER: Past performance is no guarantee of future results , and so on.. you know the drill ;)
Please read whole description first before using, don't take 1-2 paragraph and claim it's the whole logic, you are responsible of your own actions and understanding.
Flawless Victory Strategy - 15min BTC Machine Learning StrategyHello everyone, I am a heavy Python programmer bringing machine learning to TradingView. This 15 minute Bitcoin Long strategy was created using a machine learning library and 1 year of historical data in Python. Every parameter is hyper optimized to bring you the most profitable buy and sell signals for Bitcoin on the 15min chart. The historical Bitcoin data was gathered from Binance API, in case you want to know the best exchange to use this long strategy. It is a simple Bollinger Band and RSI strategy with two versions included in the tradingview settings. The first version has a Sharpe Ratio of 7.5 which is amazing, and the second version includes the best stop loss and take profit positions with a Sharpe Ratio of 2.5 . Let me talk a little bit more about how the strategy works. The buy signal is triggered when close price is less than lower Bollinger Band at Std Dev 1, and the RSI is greater than a certain value. The sell signal is triggered when close price is greater than upper Bollinger Band at Std Dev 1, and the RSI is greater than a certain value. What makes this strategy interesting is the parameters the Machine Learning library found when backtesting for the best Sharpe Ratio. I left my computer on for about 28 hours to fully backtest 5000 EPOCHS and get the results. I was able to create a great strategy that might be one of TradingView's best strategies out on the website today. I will continue to apply machine learning to all my strategies from here on forward. Please Let me know if you have any questions or certain strategies you would like me to hyper optimize for you. I'm always willing to create profitable strategies!
P.S. You can always pyramid this strategy for more gains! I just don't add pyramiding when creating my strategies because I want to show you the true win/loss ratio based buying one time and one selling one time. I feel like when creating a strategy that includes pyramiding right off the bat falsifies the win rate. This is my way of being transparent with you all. Have fun trading!
Ultimate Strategy TemplateHello Traders
As most of you know, I'm a member of the PineCoders community and I sometimes take freelance pine coding jobs for TradingView users.
Off the top of my head, users often want to:
- convert an indicator into a strategy, so as to get the backtesting statistics from TradingView
- add alerts to their indicator/strategy
- develop a generic strategy template which can be plugged into (almost) any indicator
My gift for the community today is my Ultimate Strategy Template
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
For doing so:
1) Find in your indicator where are the conditions printing the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator wether it's a MACD, ZigZag, Pivots, higher-highs, lower-lows or whatever indicator with clear buy and sell conditions
//@version=4
study(title='Moving Average Cross', shorttitle='Moving Average Cross', overlay=true, precision=6, max_labels_count=500, max_lines_count=500)
type_ma1 = input(title="MA1 type", defval="SMA", options= )
length_ma1 = input(10, title = " MA1 length", type=input.integer)
type_ma2 = input(title="MA2 type", defval="SMA", options= )
length_ma2 = input(100, title = " MA2 length", type=input.integer)
// MA
f_ma(smoothing, src, length) =>
iff(smoothing == "RMA", rma(src, length),
iff(smoothing == "SMA", sma(src, length),
iff(smoothing == "EMA", ema(src, length), src)))
MA1 = f_ma(type_ma1, close, length_ma1)
MA2 = f_ma(type_ma2, close, length_ma2)
// buy and sell conditions
buy = crossover(MA1, MA2)
sell = crossunder(MA1, MA2)
plot(MA1, color=color_ma1, title="Plot MA1", linewidth=3)
plot(MA2, color=color_ma2, title="Plot MA2", linewidth=3)
plotshape(buy, title='LONG SIGNAL', style=shape.circle, location=location.belowbar, color=color_ma1, size=size.normal)
plotshape(sell, title='SHORT SIGNAL', style=shape.circle, location=location.abovebar, color=color_ma2, size=size.normal)
/////////////////////////// SIGNAL FOR STRATEGY /////////////////////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title="🔌Connector🔌", transp=100)
Basically, I identified my buy, sell conditions in the code and added this at the bottom of my indicator code
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title="🔌Connector🔌", transp=100)
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal , and -1 for the bearish signal
Now you can connect your indicator to the Strategy Template using the method below or that one
Step 2: Connect the connector
1) Add your updated indicator to a TradingView chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings and in the Data Source field select your 🔌Connector🔌 (which comes from your indicator)
From then, you should start seeing the signals and plenty of other stuff on your chart
🔥 Note that whenever you'll update your indicator values, the strategy statistics and visual on your chart will update in real-time
Settings
- Color Candles : Color the candles based on the trade state (bullish, bearish, neutral)
- Close positions at market at the end of each session : useful for everything but cryptocurrencies
- Session time ranges : Take the signals from a starting time to an ending time
- Close Direction : Choose to close only the longs, shorts, or both
- Date Filter : Take the signals from a starting date to an ending date
- Set the maximum losing streak length with an input
- Set the maximum winning streak length with an input
- Set the maximum consecutive days with a loss
- Set the maximum drawdown (in % of strategy equity)
- Set the maximum intraday loss in percentage
- Limit the number of trades per day
- Limit the number of trades per week
- Stop-loss: None or Percentage or Trailing Stop Percentage or ATR
- Take-Profit: None or Percentage or ATR
- Risk-Reward based on ATR multiple for the Stop-Loss and Take-Profit
This script is open-source so feel free to use it, and optimize it as you want
Alerts
Maybe you didn't know it but alerts are available on strategy scripts.
I added them in this template - that's cool because:
- if you don't know how to code, now you can connect your indicator and get alerts
- you have now a cool template showing you how to create alerts for strategy scripts
Source: www.tradingview.com
I hope you'll like it, use it, optimize it and most importantly....make some optimizations to your indicators thanks to this Strategy template
Special Thanks
Special thanks to @JosKodify as I borrowed a few risk management snippets from his website: kodify.net
Additional features
I thought of plenty of extra filters that I'll add later on this week on this strategy template
Best
Dave
Backtest Custom HiLo StrategyThis script implements the HiLo trend follower strategy for backtesting the HiLo indicator. It comes with some custom options. Among them is the Type option that allows to choose between the moving averages for the highs and lows (HiLo), or highest and lowest values for those moving averages, respectively (HiLo Activator).
Hull Suite StrategyConverted the hull suite into a strategy script for easy backtesting and added ability to specify a time periods to backtest over.
SupterTrend I created this script for basically two reasons
1. there is not simple suptertrend indicator available on tv rather you will find many fancy suptrend indicators with confusing other indicators and absurd background colors i dont know why some of the trader coders are obsessed with is using over the top color and designing phenomena.
2. I want to let people know about the accuracy of suptertrend indicator on multiple time frames i am plaining to create a backtesting tool for almost all Famous indicators so that specially new folks know what should they expect from any particular Indicator
Also i added intraday filter to check the results for intrday signals . the sqaure off timings are for Indian markets only but you can edit the hours and minutes in the code for using other than indian markets. No need to do anything if you only want positional trading trading results
Patient Trendfollower (7)(alpha) Backtesting AlgorithmThis is an alpha version of backtesting algorithm for my Patient Trendfollower (7) strategy. It can help you adapt the indicator to other charts than EURUSD. Please bear in mind that price action, volume profiles and supzistences are a catalyst for successful trading, not an indicator. You can get significantly better results if you use these things in your trading and use Trendfollower only as a secondary tool.
Patient Trendfollower Indicator
Thanks belongs to @everget and Satik FX, their contributions are highlighted on an indicator page.
BO - RSI - M5 BacktestingBO - RSI - M5 Backtesting -Rule of Strategy
A. Data
1. Chart M5 IDC
2. Symbol: EURJPY
B. Indicator
1. RSI
2. Length: 12 (adjustable)
3. Extreme Top: 75 (adjustable)
4. Extreme Bottom: 25 (adjustable)
C. Rule of Signal
1. Put Signal
* Rsi create a temporary peak over Extreme Top
row61: peak_rsi= rsi >rsi and rsi >rsi and rsi rsi_top
2. Call Signal
* Rsi create a temporary bottom under Extreme Bottom
row62: bott_rsi= rsi rsi and rsi
UT Bot Strategy with Backtesting Range [QuantNomad]UT Bot indicator was inially developer by @Yo_adriiiiaan
Idea of original code belongs @HPotter
I can't update my original UT Bot Strategy so I publishing new strategy with backtesting range included.
I just took code of Yo_adriiiiaan, cleaned it, deleted all useless pieces of code, transformet to v4 and created a strategy from it.
Also I added an input that allows you to swich to signals from Heiking Ashi. I saw that author uses HA for the indicator and on HA it look much nices then on real candles.
Do not add this strategy to HA candles, use usual candles and this checkbox.
Original script:
UT Bot
SuperTrend BacktesterThis is a backtesting script for the famous Super Trend.
Features
- Custom Date Range
- Custom Targets and Risks
Requested by Dlatrella
CS Basic Scripts - Stochastic Special (Strategy)This Stochastic Special Strategy features inputs for:
- Custom Backtesting Date Range
- Long and Short Strategy Discinctions
- Utilize SMI, RSI, Martingale, and Body-Filter Strategy
- Adjust the SMI Percent Lengths and Limit
- Automate with the Autoview Trading Bot
Strategy script may be tested by favoriting and adding to any chart.
Study script is available for automated trading at www.cryptoscores.org
Deviation Back Tester (Great for Credit Spreads)!Error with math fixed in this one. Please use this one.
This is great for credit spreads! Lets say you wanted to know if you had sold a 15% OTM Bull Put vertical 2 months out, how often would you win? This Turns green if you would have been correct with your credit spread had it expired on that date, or red if you would've been wrong. Great for Back testing!
This could also be used for ATM debit spreads credit spreads etc. Example, how often does SPY deviate outside a 10% range relative to two months, 5% (if your doing straddles perhaps) etc.
This Can be used with any stock.
PLEASE KEEP IN MIND THAT IT TESTS DEVIATION IN BOTH DIRECTIONS. THEREFORE IT WILL HIGHLIGHT RED ON BOTH THE UPSIDE AND DOWNSIDE. WHEN BACKTESTING BE SURE TO CHECK WHETHER IT IS RED BECAUSE OF DOWNSIDE OR UPSIDE.
