Combined Strategy Trading Bot (RSI ADX 20SMA)Trading Bot V1, This code implements a combined trading strategy that uses several indicators and strategies to make buy and sell decisions in the market. The code is written in Pine Script™, which is a programming language used in the TradingView platform. By BraelonWhitfield.Eth
The strategy uses the Average Directional Movement Index (ADX) and the Pine SuperTrend indicator to identify trends and price movements in the market. The SuperTrend indicator is a popular technical analysis tool that helps to identify the direction of the current trend and provides entry and exit points for trades.
The strategy also uses the Relative Strength Index (RSI) to identify overbought and oversold conditions in the market. The RSI is a momentum indicator that measures the speed and change of price movements in the market.
The first part of the code defines the inputs for the ADX and DI Length, which are used to calculate the ADX and DI values. The dirmov() function is used to calculate the positive and negative directional indicators (plusDM and minusDM) based on the high and low prices. The truerange variable is then calculated using the True Range (TR) formula. Finally, the plus and minus variables are calculated using the smoothed moving average of the plusDM and minusDM values.
The adx() function is then used to calculate the ADX values based on the plus and minus variables. The Pine SuperTrend indicator is defined using the pine_supertrend() function. This function uses the high-low average (hl2) and the Average True Range (ATR) to calculate the upper and lower bands for the indicator. The direction of the current trend is then determined based on whether the current price is above or below the upper or lower bands.
The RSI values are then calculated using the ta.rsi() function, with the inputs for the close price and the RSI period. The overbought and oversold conditions are defined using the OB and OS inputs, which specify the threshold values for the RSI. The upTrend and downTrend variables are defined based on the direction of the Pine SuperTrend indicator.
The next part of the code defines the 20-period Simple Moving Average (SMA) using the ta.sma() function. The os and ob variables are then calculated based on the RSI values and the OB and OS inputs. The strategy.entry() function is used to define the buy and sell orders based on the upTrend and downTrend variables, as well as the Pine SuperTrend indicator, the 20-period SMA, and the os variable.
The final part of the code defines the Channel Breakout Strategy using the ta.highest() and ta.lowest() functions to calculate the upper and lower bounds of the channel. The strategy.entry() function is then used to define the buy and sell orders based on whether the current price is above or below the upper or lower bounds.
In summary, this code implements a combined trading strategy that uses several indicators and strategies to make buy and sell decisions in the market. The strategy is designed to identify trends and price movements in the market, as well as overbought and oversold conditions, to provide entry and exit points for trades. The strategy uses the Pine SuperTrend indicator, the ADX and DI indicators, the RSI, and the 20-period SMA, as well as the Channel Breakout Strategy to make informed trading decisions.
Cari dalam skrip untuk "the strat"
Reinforced RSI - The Quant Science This strategy was designed and written with the goal of showing and motivating the community how to integrate our 'Probabilities' module with their own script.
We have recreated one of the simplest strategies used by many traders. The strategy only trades long and uses the overbought and oversold levels on the RSI indicator.
We added stop losses and take profits to offer more dynamism to the strategy. Then the 'Probabilities' module was integrated to create a probabilistic reinforcement on each trade.
Specifically, each trade is executed, only if the past probabilities of making a profitable trade is greater than or equal to 51%. This greatly increased the performance of the strategy by avoiding possible bad trades.
The backtesting was calculated on the NASDAQ:TSLA , on 15 minutes timeframe.
The strategy works on Tesla using the following parameters:
1. Lenght: 13
2. Oversold: 40
3. Overbought: 70
4. Lookback: 50
5. Take profit: 3%
6. Stop loss: 3%
Time period: January 2021 to date.
Our Probabilities Module, used in the strategy example:
Power Of Stocks - Bollinger Band & 5Ema Indicator - Keanu_RiTz
Power of Stocks - Bollinger band & 5ema Strategy
In this script you get to take Buy/Sell trades using the 3 options mentioned below.(Alerts with price levels for buy/sell at , SL & Target are included in this one)
1. Combined Strategy :- uses confirmation from both strategies to trade.
2. Bollinger band Strategy :- use the Bollinger band Strategy to trade.
3. 5ema Strategy :- use the 5ema Strategy to trade.
1. Combined Strategy :-
for Selling :- we will go short/sell only when conditions of both strategies are satisfied.
i.e. when a candle is completely above the upper Bollinger band & completely above the 5ema then it will be our Alert Candle.
We Short/Sell only when the low of the Alert candle is broken or when the candle closes below the close of the Alert Candle.
SL will be above high of the Alert Candle. Target will be minimum 1:3 or as per your emotions.
for Buying:- we will go Long/Buy only when conditions of both strategies are satisfied.
i.e. when a candle is completely below the lower Bollinger band & completely below the 5ema then it will be our Alert Candle.
We go Long/Buy only when the high of the Alert candle is broken or when the candle closes above the close of the Alert Candle.
SL will be below low of the Alert Candle. Target will be minimum 1:3 or as per your emotions.
2. Power of Stocks - Bollinger Band Strategy :-
Bollinger band with standard deviation = 1.5
when a candle is completely above the upper Bollinger band, that candle will be called a signal/alert candle.
Initiate a Sell trade when that alert candles low is broken. SL will be above high of that alert candle.
Risk to reward ratio will be 1:4 i.e. target will be 4 times the SL.
when a candle is completely below the lower Bollinger band, that candle will be called a signal/alert candle.
Initiate a Buy trade when that alert candles high is broken. SL will be below low of that alert candle.
Risk to reward ratio will be 1:4 i.e. target will be 4 times the SL.
other rules for Options buying:- minimum 15min timeframe
The day you initiate the position , you should be in profit above 10%-15% then only you should carry forward that position overnight, otherwise squareoff your trade on that day only.
Buy ATM or slightly OTM, SL max 100 points , target 1:4
for Long-term/Investing :- Minimum Weekly
If candle is outside the lower band then initiate a Buy trade when that candles High is broken. Sl will be below Low of that candle.
for Long-term Target will be according to your emotions.
3. Power of Stocks - 5ema Strategy (target minimum 1:3)
Timeframe -
5 min for Selling (Sell Futures/index/stocks or buy Put)
15 min for Buying (Buy Futures/index/stocks or sell Put)
for selling stocks :-
you should enter trade within 10am , don't look for entries after that time. take only 2 entries a day.
for selling Index(Banknifty) :-
you can take trade at anytime of the day whenever conditions get satisfied. you can take multiple entries in banknifty as it is very volatile.
for options choose atm strikes: selling trade
sl for premium between 200-300 :- 20-30 points SL
sl for premium between 400-500 :- 40-50 points SL
sl for premium between 500-600 :- 50-60 points SL
Subhashish Pani's (power of stocks) 5 EMA Strategy:-
It plots 5 EMA and Buy/Sell signals with Target & Stoploss levels.
What is Subhashish Pani's (power of stocks) 5 EMA Strategy :-
His strategy is very simple to understand. for intraday use 5 minutes timeframe for selling. You can sell futures, sell call or buy Puts in selling strategy.
What this strategy tries to do is , it tries to catch the tops, so when you sell at top & it turns out to be a reversal point then you can get good profit.
this will hit stop losses often, but stop losses are small and minimum target should be 1:3. but if you stay with the trend you can get big profits.
According to Subhashish Pani this strategy has 60% success rate.
Strategy for Selling (Short future/Call/stock or buy Put)
When ever a Candle closes completely above 5 ema (no part of candle should be touching the 5ema), then that candle should be considered as Alert Candle.
If the next candle is also completely above 5 ema and it has not broken the low of previous alert candle, Then the previous Alert Candle should be ignored and the new candle should be considered as new Alert Candle.
so if this goes on then continue shifting the Alert Candle, but whenever the next candle breaks the low of the Alert Candle we should take the Short trade (Short future/Call/stock or buy Put).
Stoploss will be above high of the Alert Candle and minimum target will be 1:3.
Strategy for Buying (Buy future/Call/stock or sell Put)
When ever a Candle closes completely below 5 ema (no part of candle should be touching the 5ema), then that candle should be considered as Alert Candle.
If the next candle is also completely below 5 ema and it has not broken the high of previous alert candle, Then the previous Alert Candle should be ignored and the new candle should be considered as new Alert Candle.
so if this goes on then continue shifting the Alert Candle, but whenever the next candle breaks the high of the Alert Candle we should take the Long trade (Buy future/Call/stock or sell Put).
Stoploss will be below low of the Alert Candle and minimum target will be 1:3.
Buy/Sell with extra conditions :
it just adds 1 more condition to buying/selling
1. checks if closing of current candle is lower than alert candles closing for Selling & checks if closing of current candle is higher than alert candles closing for Buyling.
This can sometimes save you from false moves but by using this, you can also miss out on big moves as you'll enter trade after candle closing instead of entering at break of high/low.
Note :- According to Subhashish Pani Timeframe for intraday buying should be 15 minutes Timeframe.
If you haven't understood the strategy by reading above description, then search for "Subhashish Pani's (power of stocks) 5 EMA Strategy" on YouTube to get a deeper understanding.
Note:- This is not only for Intraday trading , you can use this strategy for Positional/Swing trading as well. If you use this on Monthly Timeframe then it can be very good for Long Term Investing as well.
Rules will be same for all types of trades & Timeframes.
[fpemehd] Strategy TemplateHello Guys! Nice to meet you all!
This is my fourth script!
This is the Strategy Template for traders who wants to make their own strategy.
I made this based on the open source strategies by jason5480, kevinmck100, myncrypto. Thank you All!
### StopLoss
1. Can Choose Stop Loss Type: Percent, ATR, Previous Low / High.
2. Can Chosse inputs of each Stop Loss Type.
### Take Profit
1. Can set Risk Reward Ratio for Take Profit.
- To simplify backtest, I erased all other options except RR Ratio.
- You can add Take Profit Logic by adding options in the code.
2. Can set Take Profit Quantity.
### Risk Manangement
1. Can choose whether to use Risk Manangement Logic.
- This controls the Quantity of the Entry.
- e.g. If you want to take 3% risk per trade and stop loss price is 6% below the long entry price,
then 50% of your equity will be used for trade.
2. Can choose How much risk you would take per trade.
### Plot
1. Added Labels to check the data of entry / exit positions.
2. Changed and Added color different from the original one. (green: #02732A, red: #D92332, yellow: #F2E313)
VIDYA Trend StrategyOne of the most common messages I get is people reaching out asking for quantitative strategies that trade cryptocurrency. This has compelled me to write this script and article, to help provide a quantitative/technical perspective on why I believe most strategies people write for crypto fail catastrophically, and how one might build measures within their strategies that help reduce the risk of that happening. For those that don't trade crypto, know that these approaches are applicable to any market.
I will start off by qualifying up that I mainly trade stocks and ETFs, and I believe that if you trade crypto, you should only be playing with money you are okay with losing. Most published crypto strategies I have seen "work" when the market is going up, and fail catastrophically when it is not. There are far more people trying to sell you a strategy than there are people providing 5-10+ year backtest results on their strategies, with slippage and commissions included, showing how they generated alpha and beat buy/hold. I understand that this community has some really talented people that can create some really awesome things, but I am saying that the vast majority of what you find on the internet will not be strategies that create alpha over the long term.
So, why do so many of these strategies fail?
There is an assumption many people make that cryptocurrency will act just like stocks and ETFs, and it does not. ETF returns have more of a Gaussian probability distribution. Because of this, ETFs have a short term mean reverting behavior that can be capitalized on consistently. Many technical indicators are built to take advantage of this on the equities market. Many people apply them to crypto. Many of those people are drawn down 60-70% right now while there are mean reversion strategies up YTD on equities, even though the equities market is down. Crypto has many more "tail events" that occur 3-4+ standard deviations from the mean.
There is a correlation in many equities and ETF markets for how long an asset continues to do well when it is currently doing well. This is known as momentum, and that correlation and time-horizon is different for different assets. Many technical indicators are built based on this behavior, and then people apply them to cryptocurrency with little risk management assuming they behave the same and and on the same time horizon, without pulling in the statistics to verify if that is actually the case. They do not.
People do not take into account the brokerage commissions and slippage. Brokerage commissions are particularly high with cryptocurrency. The irony here isn't lost to me. When you factor in trading costs, it blows up most short-term trading strategies that might otherwise look profitable.
There is an assumption that it will "always come back" and that you "HODL" through the crash and "buy more." This is why Three Arrows Capital, a $10 billion dollar crypto hedge fund is now in bankruptcy, and no one can find the owners. This is also why many that trade crypto are drawn down 60-70% right now. There are bad risk practices in place, like thinking the martingale gambling strategy is the same as dollar cost averaging while also using those terms interchangeably. They are not the same. The 1st will blow up your trade account, and the 2nd will reduce timing risk. Many people are systematically blowing up their trade accounts/strategies by using martingale and calling it dollar cost averaging. The more risk you are exposing yourself too, the more important your risk management strategy is.
There is an odd assumption some have that you can buy anything and win with technical/quantitative analysis. Technical analysis does not tell you what you should buy, it just tells you when. If you are running a strategy that is going long on an asset that lost 80% of its value in the last year, then your strategy is probably down. That same strategy might be up on a different asset. One might consider a different methodology on choosing assets to trade.
Lastly, most strategies are over-fit, or curve-fit. The more complicated and more parameters/settings you have in your model, the more likely it is just fit to historical data and will not perform similar in live trading. This is one of the reasons why I like simple models with few parameters. They are less likely to be over-fit to historical data. If the strategy only works with 1 set of parameters, and there isn't a range of parameters around it that create alpha, then your strategy is over-fit and is probably not suitable for live trading.
So, what can I do about all of this!?
I created the VIDYA Trend Strategy to provide an example of how one might create a basic model with a basic risk management strategy that might generate long term alpha on a volatile asset, like cryptocurrency. This is one (of many) risk management strategies that can reduce the volatility of your returns when trading any asset. I chose the Variable Index Dynamic Average (VIDYA) for this example because it's calculation filters out some market noise by taking into account the volatility of the underlying asset. I chose a trend following strategy because regressions are capturing behaviors that are not just specific to the equities market.
The more volatile an asset, the more you have to back-off the short term price movement to effectively trend-follow it. Otherwise, you are constantly buying into short term trends that don't represent the trend of the asset, then they reverse and loose money. This is why I am applying a trend following strategy to a 4 hour chart and not a 4 minute chart. It is also important to note that following these long term trends on a volatile asset exposes you to additional risk. So, how might one mitigate some of that risk?
One of the ways of reducing timing risk is scaling into a trade. This is different from "doubling down" or "trippling down." It is really a basic application of dollar cost averaging to reduce timing risk, although DCA would typically happen over a longer time period. If it is really a trend you are following, it will probably still be a trend tomorrow. Trend following strategies have lower win rates because the beginning of a trend often reverses. The more volatile the asset, the more likely that is to happen. However, we can reduce risk of buying into a reversal by slowly scaling into the trend with a small % of equity per trade.
Our example "VIDYA Trend Strategy" executes this by looking at a medium-term, volatility adjusted trend on a 4 hour chart. The script scales into it with 4% of the account equity every 4-hours that the trend is still up. This means you become fully invested after 25 trades/bars. It also means that early in the trade, when you might be more likely to experience a reversal, most of your account equity is not invested and those losses are much smaller. The script sells 100% of the position when it detects a trend reversal. The slower you scale into a trade, the less volatile your equity curve will be. This model also includes slippage and commissions that you can adjust under the "settings" menu.
This fundamental concept of reducing timing risk by scaling into a trade can be applied to any market.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Soren test 222Say we use strategy.risk.allow_entry_in() to only trade longs. When our script uses the strategy.entry() function to open a short trade, TradingView of course won’t allow our strategy to go short. But that doesn’t mean the trade is ignored. Instead the ‘enter short’ trade – which is actually a sell command – becomes an ‘exit long’ order.
Another way to think about this is the following. The strategy.entry() function can reverse positions: longs into shorts, and shorts into longs. That reverse behaviour gets stopped by strategy.risk.allow_entry_in(). What strategy.entry() instead ends doing is close positions: from long to flat, or from short to flat.
(The example strategies that we discuss later in this article show how strategy.risk.allow_entry_in() makes strategy.entry() close instead of open trades.)
# Can still trade both long or short
strategy.risk.allow_entry_in() can also allow our strategy to trade both long or short. That’s a bit silly, since this is already the default behaviour. But to cod
Strategy Template - V2This is an educational script created to demonstrate few basic building blocks of a trend based strategy and how to achieve different entry and exit types. My initial intention was to create a comprehensive strategy template which covers all the aspects of strategy. But, ended up creating fully fledged strategy based on trend following.
This is an enhancement on Strategy-Template But this script is comparitively more complex. Hence I decided to create new version instead of updating the existing one.
Lets dive deep.
SIMPLE COMPONENTS OF TREND FOLLOWING STRATEGY
TREND BIAS - This defines the direction of trend. Idea is not to trade against the trend direction. If the bias is bullish, look for long opportunities and if bias is bearish, look for short opportunities. Stay out of the market when the bias is neutral.
Often, trend bias is determined based on longer timeframe conditions. Example - 200 Moving Average, Higher timeframe moving averages, Higher timeframe high-lows etc. can be used for determining the trend bias.
In this script, I am using Weekly donchian channels combined with daily donchian channels to define trend bias.
Long Bias - 40 Day donchian channel sits completely in upper portion of 40 Week dochnial channel.
Short Bias - 40 Day donchian channel sits completely in lower portion of 40 Week donchian channel.
ENTRY CONDITION - Entry signals are generated only in the direction of bias. Hence, when in LongBias, we only get Long signals and when in short bias, we only get short signals.
In our case, when in Long Bias - if price hits 40 day high for the first time, this creates our long entry signal. Similarly when in Short Bias , price hitting 40 day low will create signal for going short. Since we do not take trades opposite to trend, no entry conditions are formed when price hits 40 day high in Short Bias or 40 day low in Long Bias.
EXIT CONDITION - Exit conditions are formed when we get signals of trend failure.
In our case, when in long trade, price hitting 40 day low creates exit signal. Similarly when in short trade price hitting 40 day high creates exit signal for short trade.
DIFFERENT TYPES OF ENTRY AND EXIT
In this script, I have tried to demonstrate different entry and exit types.
Entry types
Market - Enter immediately when entry signal is received. That is, in this case when price crossover over high in long bias and crosses under low in short bias
Stop - This method includes estimating at what level new highs are made and creating a stop buy order at that level. This way, we do not miss if the break out is stronger. But, susciptible to fail during fakeouts.
Limit - This method includes executing a limit order to buy at lower price or sell at higher price. In trend following methods, downside of limit order is when there is genuine breakout, these limit orders may not hit and during trend failures the limit orders are likely to hit and go straight to stop.
Stop-Limit - this is same as stop order but will also place a limit condition to avoid buying on overextended breakout or with lots of slippage.
Exit types
Market - whether to keep the existing trade running or whether to close it is determined after close of each bar and exit orders are executed manually upon receiving exit signal.
Stop - We place stop loss orders beforehand when there is a trade in place. This can help in avoiding big movements against trade within bar. But, this may also stop on false signals or fakeouts.
Take profit
Stop - No take profits are configured.
Target - 30% of the positions are closed when take profit levels are hit. Take profit levels are defined by risk reward.
USING THE CODE AS TEMPLATE
As mentioned earlier, I intended to create a fully fledged strategy template. But, ended up creating a fully fledged stratgy. However, you can take some part of this code and use it to start your own strategy. Will explain what all things can be adopted without worrying about the strategy implementation within
Strategy definition : This can be copied as is and just change the title of strategy. This defines some of the commonly used parameters of strategy which can help with close to realistic backtesting results for your coded strategy and comparison with buy and hold.
Generic Strategy Parameters : The parameter which defines controlling alllowed trade direction and trading window are present here. This again can be copied as is and variable inDateRange can be directly used in entry conditions.
Generic Methods : f_getMovingAverage and f_secureSecurity are handy and can be used as is. atr method provideded by pine gives you ATR based on RMA. If you want SMA or any other moving average based ATR, you can use the method f_getCustomAtr
Trade Statements : This section has all types of trading instructions which includes market/stop/limit/stop-limit type of entries and exits and take profit statements. You can adopt the type of entry you are interested in and change when condition to suit your strategy.
Trade conditions and levels : This section is required. But, cannot be copied. All the trade logic goes here which also sets parameters which are used in when of Trade Statements.
Hope this helps.
How to use Leverage and Margin in PineScriptEn route to being absolutely the best and most complete trading platform out there, TradingView has just closed 2 gaps in their PineScript language.
It is now possible to create and backtest a strategy for trading with leverage.
Backtester now produces Margin Calls - so recognizes mid-trade drawdown and if it is too big for the broker to maintain your trade, some part of if will be instantly closed.
New additions were announced in official blogpost , but it lacked code examples, so I have decided to publish this script. Having said that - this is purely educational stuff.
█ LEVERAGE
Let's start with the Leverage. I will discuss this assuming we are always entering trades with some percentage of our equity balance (default_qty_type = strategy.percent_of_equity), not fixed order quantity.
If you want to trade with 1:1 leverage (so no leverage) and enter a trade with all money in your trading account, then first line of your strategy script must include this parameter:
default_qty_value = 100 // which stands for 100%
Now, if you want to trade with 30:1 leverage, you need to multipy the quantity by 30x, so you'd get 30 x 100 = 3000:
default_qty_value = 3000 // which stands for 3000%
And you can play around with this value as you wish, so if you want to enter each trade with 10% equity on 15:1 leverage you'd get default_qty_value = 150.
That's easy. Of course you can modify this quantity value not only in the script, but also afterwards in Script Settings popup, "Properties" tab.
█ MARGIN
Second newly released feature is Margin calculation together with Margin Calls. If the market goes against your trades and your trading account cannot maintain mid-trade drawdown - those trades will be closed in full or partly. Also, if your trading account cannot afford to open more trades (pyramiding those trades), Margin mechanism will prevent them from being entered.
I will not go into details about how Margin calculation works, it was all explainged in above mentioned blogpost and documentation .
All you need to do is to add two parameters to the opening line of your script:
margin_long = 1./30*50, margin_short = 1./30*50
Whereas "30" is a leverage scale as in 30:1, and "50" stands for 50% of Margin required by your broker. Personally the Required Margin number I've met most often is 50%, so I'm using value 50 here, but there are literally 1000+ brokers in this world and this is individual decision by each of them, so you'd better ask yourself.
--------------------
Please note, that if you ever encounter a strategy which triggers Margin Call at least once, then it is probably a very bad strategy. Margin Call is a last resort, last security measure - all the risks should be calculated by the strategy algorithm before it is ever hit. So if you see a Margin Call being triggred, then something is wrong with risk management of the strategy. Therefore - don't use it!
[laoowai]BNB_USDT_3m_3Commas_Bollinger_MACD_RSI_StrategyBNB_USDT _3m
Release Notes:
Time: 3min
Pair: BNB_USDT
Use: {{strategy.order.alert_message}}
What's the difference with 3Commas Bollinger Strategy by tedwardd:
1. Initial capital: 1210 USDT (10$ Base order / 400$*3 Safety order), if you will change, please change JUST safety order volume or number of safety orders 2-3
2. Using just 2(3) safety order (original script 4)
3. More high-performance strategy for BNB_USDT
4. Using MACD to sell order (original script take profit by scale), thanks Drun30 .
5. Using RSI to analyze the market conditions.
Need to change:
bot_id = input(title="3Commas Bot ID", defval=" YOUR DATA ")
email_token = input(title="Bot Email Token", defval=" YOUR DATA ")
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FAQ copy from tedwardd
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This strategy is intended for use as a way of backtesting various parameters available on 3commas.
The primary inputs for the strategy are:
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// USER INPUTS
Short MA Window - The length of the Short moving average
Long MA Window - The length of the Long moving average
Upper Band Offset - The offset to use for the upper bollinger offset
Lower Band Offset - The offset to use for the lower bollinger offset
Long Stop Loss % - The stop loss percentage to test
Long Take Profit % - The Take profit percentage to test
Initial SO Deviation % - The price deviation percentage required to place to first safety order
Safety Order Vol Step % - The volume scale to test
3Commas Bot ID - (self-explanatory)
Bot Email Token - Found in the deal start message for your bot (see link in the previous section for details)
3Commas Bot Trading Pair - The pair to include for composite bot start deals (should match the format of 3commas, not TradingView IE. USDT_BTC not BTCUSDT )
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Start Date, Month, Year and End Date, Month, and Year all apply to the backtesting window. By default, it will use as much data as it can give the current period select (there is less historical data available for periods below 1H) back as far as 2016 (there appears to be no historical data on Trading view much before this). If you would like to test a different period of time, just change these values accordingly.
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Composite bot using a Bollinger band type trading strategy. While its primary intention is to provide users a way of backtesting bot parameters, it can also be used to trigger a deal start by either using the {{strategy.order.alert_message}} field in your alert and providing the bot details in the configuration screen for the strategy or by including the usual deal start message provided by 3commas.
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Original script:
1. 3Commas Bollinger Strategy by tedwardd
2. Momentum Strategy ( BTC /USDT; 1h) - MACD (with source code) by Drun30
RSI+PA+DCA StrategyDear Tradingview community,
This RSI based trading strategy is created as a training exercise. I am not a professional trader, but a committed hobbyist. This not a finished trading strategy meant for trading, but more a combination of different trading ideas I liked to explore deeper. The aim with this exercise was to gain more knowledge and understanding about price averaging and dollar cost averaging strategies. Aside that I wanted to learn how to program a pyramiding strategy, how to plot different order entry layers and how to open positions on a specific time interval.
In this script I adapted code from a couple of strategy examples by Coinrule . Who wrote simple and powerful examples of RSI based strategies and pyramiding strategies.
Also the HOWTO scripts shared by vitvlkv were very helpful for this exercise. In the script description you can find all the sources to the code.
A PA strategy could be a helpful addition to ease the 'stress-management to buy when price drops and resolution in selling when the price is rising' (Coinrule).
The idea behind the strategy is fairly simple and is based on an RSI strategy of buying low. A position is entered when the RSI and moving average conditions are met. The position is closed when it reaches a specified take profit percentage. As soon as the first the position is openend multiple PA (price average) layers are setup based on a specified percentage of price drop. When the price crosses the layer another position with somewhat the same amount of assets is entered. This causes the average cost price (the red plot line) to decrease. If the price drops more, another similar amount of assets is bought with another price average decrease as result. When the price starts rising again the different positions are separately closed when each reaches its specified take profit. The positions can be re-openend when the price drops again. And so on. When the price rises more and crosses over the average price and reached the specified take profit on top of it, it closes all the positions at once and cancels all orders. From that moment on it waits for another price dip before it opens a new position.
Another option is to activate a DCA function that opens a position based on a fixed specified amount. It enters a position at the start of every week and only when there are already other positions openend and if the current price is below the average price of the position. Like this buying on a time interval can help lowering the average price in case the market is down.
I read in some articles that price averaging is also called dollar cost averaging as the result is somewhat the same. Although DCA is really based on buying on fixed time intervals. These strategies are both considered long term investment strategies that can be profitable in the long run and are not suitable for short term investment schemes. The downturn is that the postion size increases when the general market trend is going down and that you have to patiently wait until the market start rising again.
Another notable aspect is that the logic in this strategy works the way it does because the entries are exited based on the FIFO (first in first out) close entry rule. This means that the first exit is applied to the first entry position that is openend. In other words that when the third entry reaches its take profit level and exits, it actually exits the first entry. If you take a close look in the 'List of Trades' of your Strategy Tester panel, you can see that some 'Long1' entries are closed by an 'Exit 3' and not by an 'Exit 1'. This means that your trade partly loses, but causes a decrease in average price that is later balanced out by lower or repeated entering and closing other positions. You can change this logic to a real sequential way of closing your entries, but this changes the averaging logic considerably. In case you want to test this you need to change, in this line in the strategy call 'close_entries_rule = "FIFO"', the word FIFO to ANY.
In the settings you can specify the percentage of portfolio to use for each trade to spread the risk and for each order a trading fee of 0.075% is calculated.
TradingView Alerts to MT4 MT5 - Forex, indices, commoditiesHowdy Algo-Traders! This example script has been created for educational purposes - to present how to use and automatically execute TradingView Alerts on real markets.
I'm posting this script today for a reason. TradingView has just released a new feature of the PineScript language - ALERT() function. Why is it important? It is finally possible to set alerts inside PineScript strategy-type script, without the need to convert the script into study-type. You may say triggering alerts straight from strategies was possible in PineScript before (since June 2020), but it had its limitations. Starting today you can attach alert to any custom event you might want to include in your PineScript code.
With the new feature, it is easier not only to execute strategies, but to maintain codebase - having to update 2 versions of the code with each single modification was... ahem... inconvenient. Moreover, the need to convert strategy into study also meant it was required to rip the code from all strategy...() calls, which carried a lot of useful information, like entry price, position size, and more, definitely influencing results calculated by strategy backtest. So the strategy without these features very likely produced different results than with them. While it was possible to convert these features into study with some advanced "coding gymnastics", it was also quite difficult to test whether those gymnastics didn't introduce serious, bankrupting bugs.
//////
How does this new feature work? It is really simple. On your custom events in the code like "GoLong" or "GoShort", create a string variable containing all the values you need inside your alert and this string variable will be your alert's message. Then, invoke brand new alert() function and that's it (see lines 67 onwards in the script). Set it up in CreateAlert popup and enjoy. Alerts will trigger on candle close as freq= parameter specifies. Detailed specification of the new alert() function can be found in TradingView's PineScript Reference (www.tradingview.com), but there's nothing more than message= and freq= parameters. Nothing else is needed, it is very simple. Yet powerful :)
//////
Alert syntax in this script is prepared to work with TradingConnector. Strategy here is not too complex, but also not the most basic one: it includes full exits, partial exits, stop-losses and it also utilizes dynamic variables calculated by the code (such as stop-loss price). This is only an example use case, because you could handle variety of other functionalities as well: conditional entries, pending entries, pyramiding, hedging, moving stop-loss to break-even, delivering alerts to multiple brokers and more.
//////
This script is a spin-off from my previous work, posted over a year ago here: Some comments on strategy parameters have been discussed there, but let me copy-paste most important points:
* Commission is taken into consideration.
* Slippage is intentionally left at 0. Due to shorter than 1 second delivery time of TradingConnector, slippage is practically non-existing.
* This strategy is NON-REPAINTING and uses NO TRAILING-STOP or any other feature known to be causing problems.
* The strategy was backtested on EURUSD 6h timeframe, will perform differently on other markets and timeframes.
Despite the fact this strategy seems to be still profitable, it is not guaranteed it will continue to perform well in the future. Remember the no.1 rule of backtesting - no matter how profitable and good looking a script is, it only tells about the past. There is zero guarantee the same strategy will get similar results in the future.
Full specs of TradingView alerts and how to set them up can be found here: www.tradingview.com
MrBS:Directional Movement Index [Trend Friend Strategy]This goes with my MrBS:DMI+ indicator. I originally combined them into one, but then you cannot set alerts based on what the ADX and DMI is doing, only strategy alerts, so separate ones have more flexibility and uses.
Indicator Version is found under "MrBS:Directional Movement Index " ()
//// THE IDEA
The majority of profits made in the market come from trending markets. Of course there are strategies that would say otherwise but for the majority of people, THE TREND IS YOUR FRIEND (until the end). The idea is to follow the trend, entering once it has established its self and exiting positions when the trend weakens. This strategy gives a rough idea of the returns produced from following purely the ADX signals. At first Heikin Ashi values were used for the calculation but the results show it's not that effective. The functionality to switch between calculation types has been left in, so we can uses HA candle data to generate signals from while looking at an OHLC chart, if we want to experiment. Due to the way strategies work, we are unable to get reliable results when running the strategy on the HA chart even if we are calculating the signals from the real OHLC values. It is best to always run strategies on standard charts.
When using this strategy, I look for confirmation of the signal based on stochastic (14:3:6) direction, reversal level of stochastic, and divergance, to add confidence and adjust position size accordingly. I am going to try and code some version of that in future updates, if anyone can help or has suggestions please drop me a message.
//// INDICATOR DETAILS
- The default settings are for optimized Daily charts, for 4 hour I would suggest a smoothing of 2.
- The default values used for calculation are the Real OHLC, we can change this to Heikin Ashi in the menu.
- The strategy enters a position when ADX crosses the threshold level, and closes the position when ADX starts to fall.
- There is a signal filter in the form of a 377 period Hull Moving Average, which the price must be above or bellow for long and short positions respectively.
- The strategy closes the position when a cross-under of the ADX and its 4 period EMA. This is an attempt to stay into positions longer as sometimes the ADX will fall for 1 bar and then keep rising, while the overall trend is strong. The downside to this is that we exit trades later and this affects our max drawdown.
[blackcat] L2 Ehlers Empirical Mode TraderCircumstance Remarks: Because of my carelessness, the script of the same name that I posted before was banned and hidden because the description contained content that violated the TradingView House Rule. After communicating with the MOD, I corrected the description and obtained permission to publish it again. I hereby declare. Sorry for the inconvenience!
Level: 2
Background
John F. Ehlers introuced Empirical Mode Trader Indicator in Mar, 2010.
Function
In his article “Empirical Mode Decomposition,” John Ehlers and Ric Way suggest using methods based on bandpass filtering to distinguish trending from cycling markets. The article’s trading suggestions were used to create the Empirical Mode strategy given here for pine v4 script. If the strategy determines that the marke is in trending mode, then the strategy is allowed to trade with the trend — either long, in uptrends, or short, in downtrends. If the indicator determines that the market is in cycling mode, then the strategy allows trading cycle extremes, using Bollinger bands to trigger entries. You can do this by Choosing either cycle or trend mode at inputs.
Key Signal
Trend ---> Trend signal
FracAvgPeak ---> Upper band signal
FracAvgValley ---> Lower band signal
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 75th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
[blackcat] L2 Ehlers DFT-Adapted RSILevel: 2
Background
John F. Ehlers introuced his DFT-ADAPTED RELATIVE STRENGTH INDEX (RSI) in Jan, 2007.
Function
In "Fourier Transform For Traders" in Jan, 2007, John Ehlers presented an interesting technique of improving the resolution of spectral analysis that could be used to effectively measure market cycles. Better resolution is obtained by a surprisingly simple modification of the discrete Fourier transform. John Ehlers suggests using the discrete Fourier transform (DFT) to tune indicators. Here, I demonstrate this by building a DFT-adapted relative strength index (RSI) strategy.
Rather than display the RSI for a single cycle length across the entire chart, Ehlers DFT adaptive RSI value reflects the DFT-calculated dominant cycle length RSI. If the dominant cycle changes from 14 to 18 bars, the RSI length parameter changes accordingly. Computationally, this requires the strategy to continuously update values for all possible RSI cycle lengths via a "for" loop and array.
In details, a full-featured formula that implements a high-pass filter (HP) and a six-tap low-pass finite impulse response (FIR) filter on input, then does discrete Fourier transform calculations. I has taken liberty of adding extra parameters so the user can modify the analysis window length and the high-pass filter cutoff frequency in real time using the parameters window. Once the suite of possible RSI values is calculated, we use the DFT to select the relevant RSI for the current bar. The strategy then trades according to J. Welles Wilder's original rules for the RSI.
Key Signal
fastline--> DFT-ADAPTED RELATIVE STRENGTH INDEX (RSI) fast line
slowline--> DFT-ADAPTED RELATIVE STRENGTH INDEX (RSI) slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 71th script for Blackcat1402 John F. Ehlers Week publication.
Based on original work of Ehlers, I added ALMA smoothing on DFT-adapted relative strength index (RSI) so that clearer trend can be observed.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
[blackcat] L2 Ehlers Instantaneous Trendline Trading StrategyLevel: 2
Background
John F. Ehlers introuced Instantaneous Trendline Trading Strategy in his "Cybernetic Analysis for Stocks and Futures" chapter 3 on 2004.
Function
With the crossover strategy that Dr. Ehlers has developed it is possible to be on the wrong side of the trade for a substantial period from time to time. For this reason, Dr. Ehlers has added a rule that if the price goes against your position by more than some percentage, the strategy will correct itself and automatically reverse to the opposite position. The percentage is supplied as the input variable RevPct. RevPct is an optimizable parameter, but I find that the default
value of 1.5 percent (RevPct = 1.015) is a relatively robust number.
Key Signal
Trigger ---> Instantaneous Trendline fast line
ITrend ---> Instantaneous Trendline slow line
LimitBuy ---> limit buy price level which is triggered by gold cross
LimitSell ---> limit buy price level wich is triggered by dead cross
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 23th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
[blackcat] L2 Ehlers ITrendline Trigger Trend-Following SystemLevel: 2
Background
John F. Ehlers introuced the Instantaneous Trendline and the Trigger of the trend-following system in his "Cybernetic Analysis for Stocks and Futures" chapter 3 on 2004.
Function
The process for creating a trendfollowing trading system from the indicators is simple. One unique aspect of the code is that the ITrend is forced to be a finite impulse response (FIR)-smoothed version of price for the first seven bars of the calculation.
This initialization is included to cause the ITrend to converge more rapidly to its correct value from the beginning transient. The strategy enters a long position when the trigger crosses over the Instantaneous Trendline and enters a short position when the trigger crosses under the Instantaneous Trendline. However, an effective trading system is more than following a simple set of indicators.
First, experience has shown that greater profits result from using limit orders rather than market orders or stop orders. Market orders are selfexplanatory. Stop orders mean the market must be going in the direction of the trade before the order is filled. For example, for long-position trades, the stop order must be placed above the current price. Thus, the price must increase from its current level before you get stopped into the long-position trade. This means you necessarily give up some of the profits you would otherwise have gotten if you had entered on a market order at the instant of your signal. You can lose additional profits from stop orders due to slippage.
Key Signal
Trigger ---> trend-following system fast line
ITrend ---> trend-following system slow line
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 22th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Combo Backtest 123 Reversal & EMA & MA Crossover This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The Moving Average Crossover trading strategy is possibly the most popular
trading strategy in the world of trading. First of them were written in the
middle of XX century, when commodities trading strategies became popular.
This strategy is a good example of so-called traditional strategies.
Traditional strategies are always long or short. That means they are never
out of the market. The concept of having a strategy that is always long or
short may be scary, particularly in today’s market where you don’t know what
is going to happen as far as risk on any one market. But a lot of traders
believe that the concept is still valid, especially for those of traders who
do their own research or their own discretionary trading.
This version uses crossover of moving average and its exponential moving average.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Strategy 123 Reversal & EMA & MA Crossover This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The Moving Average Crossover trading strategy is possibly the most popular
trading strategy in the world of trading. First of them were written in the
middle of XX century, when commodities trading strategies became popular.
This strategy is a good example of so-called traditional strategies.
Traditional strategies are always long or short. That means they are never
out of the market. The concept of having a strategy that is always long or
short may be scary, particularly in today’s market where you don’t know what
is going to happen as far as risk on any one market. But a lot of traders
believe that the concept is still valid, especially for those of traders who
do their own research or their own discretionary trading.
This version uses crossover of moving average and its exponential moving average.
WARNING:
- For purpose educate only
- This script to change bars colors.
Strategy VS Buy & HoldSUMMARY:
A strategy wrapper that makes a detailed and visual comparison between a given strategy and the buy & hold returns of the traded security.
DESCRIPTION:
TradingView has a "Buy & Hold Return" metric in the strategy tester that is often enough to assess how our strategy compares to a simple buy hold. However, one may want more information on how and when your strategy beats or is beaten by a simple buy & hold strategy. This script aims to show such detail by providing a more comprehensive metrics and charting the profit/loss of the given strategy against buy & hold.
As seen in the script, it plots/draws 4 elements:
1) Strategy P/L: strategy net profit + strategy open profit
2) Buy & Hold P/L: unrealized return
3) Difference: Strategy P/L - Buy & Hold P/L
4) Strategy vs Buy Hold Stats
> Percent of bars strategy P/L is above Buy & Hold
> Percent of bars strategy P/L is below Buy & Hold
> All Time Average Difference
ADJUSTABLE PARAMETERS:
All labels/panels can be disabled by unchecking these two options:
>bnh_info_panel = input(true, title='Enable Info Panel')
>bnh_indicator_panel = input(true, title='Enable Indicator Panel')
Comparison Date Range can be changed to better isolate specific areas:
>From Year, From Month, From Day
default: 1970 01 01
>To Year, To Month, To Day
default: 2050 12 31
Default settings basically covers all historical data.
HOW TO USE:
The default script contains a simple 50-200 SMA cross strategy, just delete and replace it. Those are everything between these lines:
/////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////STRATEGY SCRIPT START//////////////////////////////////
(STRATEGY SCRIPT GOES HERE)
//////////////////////////////STRATEGY SCRIPT END////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////
Removing all plots and drawings from your strategy is advisable.
If you are going to use the Comparison Date Range, apply "bnh_timeCond" to your strategy to align the dates. A sample on how it’s applied can be seen on the Placeholder MA cross strategy.
Note: bnh_timeCond returns a boolean series
Backtesting on Non-Standard Charts: Caution! - PineCoders FAQMuch confusion exists in the TradingView community about backtesting on non-standard charts. This script tries to shed some light on the subject in the hope that traders make better use of those chart types.
Non-standard charts are:
Heikin Ashi (HA)
Renko
Kagi
Point & Figure
Range
These chart types are called non-standard because they all transform market prices into synthetic views of price action. Some focus on price movement and disregard time. Others like HA use the same division of bars into fixed time intervals but calculate artificial open, high, low and close (OHLC) values.
Non-standard chart types can provide traders with alternative ways of interpreting price action, but they are not designed to test strategies or run automated traded systems where results depend on the ability to enter and exit trades at precise price levels at specific times, whether orders are issued manually or algorithmically. Ironically, the same characteristics that make non-standard chart types interesting from an analytical point of view also make them ill-suited to trade execution. Why? Because of the dislocation that a synthetic view of price action creates between its non-standard chart prices and real market prices at any given point in time. Switching from a non-standard chart price point into the market always entails a translation of time/price dimensions that results in uncertainty—and uncertainty concerning the level or the time at which orders are executed is detrimental to all strategies.
The delta between the chart’s price when an order is issued (which is assumed to be the expected price) and the price at which that order is filled is called slippage . When working from normal chart types, slippage can be caused by one or more of the following conditions:
• Time delay between order submission and execution. During this delay the market may move normally or be subject to large orders from other traders that will cause large moves of the bid/ask levels.
• Lack of bids for a market sell or lack of asks for a market buy at the current price level.
• Spread taken by middlemen in the order execution process.
• Any other event that changes the expected fill price.
When a market order is submitted, matching engines attempt to fill at the best possible price at the exchange. TradingView strategies usually fill market orders at the opening price of the next candle. A non-standard chart type can produce misleading results because the open of the next candle may or may not correspond to the real market price at that time. This creates artificial and often beneficial slippage that would not exist on standard charts.
Consider an HA chart. The open for each candle is the average of the previous HA bar’s open and close prices. The open of the HA candle is a synthetic value, but the real market open at the time the new HA candle begins on the chart is the unrelated, regular open at the chart interval. The HA open will often be lower on long entries and higher on short entries, resulting in unrealistically advantageous fills.
Another example is a Renko chart. A Renko chart is a type of chart that only measures price movement. The purpose of a Renko chart is to cluster price action into regular intervals, which consequently removes the time element. Because Trading View does not provide tick data as a price source, it relies on chart interval close values to construct Renko bricks. As a consequence, a new brick is constructed only when the interval close penetrates one or more brick thresholds. When a new brick starts on the chart, it is because the previous interval’s close was above or below the next brick threshold. The open price of the next brick will likely not represent the current price at the time this new brick begins, so correctly simulating an order is impossible.
Some traders have argued with us that backtesting and trading off HA charts and other non-standard charts is useful, and so we have written this script to show traders what happens when order fills from backtesting on non-standard charts are compared to real-world fills at market prices.
Let’s review how TV backtesting works. TV backtesting uses a broker emulator to execute orders. When an order is executed by the broker emulator on historical bars, the price used for the fill is either the close of the order’s submission bar or, more often, the open of the next. The broker emulator only has access to the chart’s prices, and so it uses those prices to fill orders. When backtesting is run on a non-standard chart type, orders are filled at non-standard prices, and so backtesting results are non-standard—i.e., as unrealistic as the prices appearing on non-standard charts. This is not a bug; where else is the broker emulator going to fetch prices than from the chart?
This script is a strategy that you can run on either standard or non-standard chart types. It is meant to help traders understand the differences between backtests run on both types of charts. For every backtest, a label at the end of the chart shows two global net profit results for the strategy:
• The net profits (in currency) calculated by TV backtesting with orders filled at the chart’s prices.
• The net profits (in currency) calculated from the same orders, but filled at market prices (fetched through security() calls from the underlying real market prices) instead of the chart’s prices.
If you run the script on a non-standard chart, the top result in the label will be the result you would normally get from the TV backtesting results window. The bottom result will show you a more realistic result because it is calculated from real market fills.
If you run the script on a normal chart type (bars, candles, hollow candles, line, area or baseline) you will see the same result for both net profit numbers since both are run on the same real market prices. You will sometimes see slight discrepancies due to occasional differences between chart prices and the corresponding information fetched through security() calls.
Features
• Results shown in the Data Window (third icon from the top right of your chart) are:
— Cumulative results
— For each order execution bar on the chart, the chart and market previous and current fills, and the trade results calculated from both chart and market fills.
• You can choose between 2 different strategies, both elementary.
• You can use HA prices for the calculations determining entry/exit conditions. You can use this to see how a strategy calculated from HA values can run on a normal chart. You will notice that such strategies will not produce the same results as the real market results generated from HA charts. This is due to the different environment backtesting is running on where for example, position sizes for entries on the same bar will be calculated differently because HA and standard chart close prices differ.
• You can choose repainting/non-repainting signals.
• You can show MAs, entry/exit markers and market fill levels.
• You can show candles built from the underlying market prices.
• You can color the background for occurrences where an order is filled at a different real market price than the chart’s price.
Notes
• On some non-standard chart types you will not obtain any results. This is sometimes due to how certain types of non-standard types work, and sometimes because the script will not emit orders if no underlying market information is detected.
• The script illustrates how those who want to use HA values to calculate conditions can do so from a standard chart. They will then be getting orders emitted on HA conditions but filled at more realistic prices because their strategy can run on a standard chart.
• On some non-standard chart types you will see market results surpass chart results. While this may seem interesting, our way of looking at it is that it points to how unreliable non-standard chart backtesting is, and why it should be avoided.
• In order not to extend an already long description, we do not discuss the particulars of executing orders on the realtime bar when using non-standard charts. Unless you understand the minute details of what’s going on in the realtime bar on a particular non-standard chart type, we recommend staying away from this.
• Some traders ask us: Why does TradingView allow backtesting on non-standard chart types if it produces unrealistic results? That’s somewhat like asking a hammer manufacturer why it makes hammers if hammers can hurt you. We believe it’s a trader’s responsibility to understand the tools he is using.
Takeaways
• Non-standard charts are not bad per se, but they can be badly used.
• TV backtesting on non-standard charts is not broken and doesn’t require fixing. Traders asking for a fix are in dire need of learning more about trading. We recommend they stop trading until they understand why.
• Stay away from—even better, report—any vendor presenting you with strategies running on non-standard charts and implying they are showing reliable results.
• If you don’t understand everything we discussed, don’t use non-standard charts at all.
• Study carefully how non-standard charts are built and the inevitable compromises used in calculating them so you can understand their limitations.
Thanks to @allanster and @mortdiggiddy for their help in editing this description.
Look first. Then leap.
Understanding order sizestype: properties manipulation, no programming needed
time required: 15minutes, at least
level: medium (need to know contracts, trading pairs)
A strategy can "appear" to work or be broken depending on the pile of cash that is working on. This amount is defined in the strat properties, under "order size".
For noobs (like me) this is very confusing at first :)
A strat opens/closes positions using units, a generic measure for the chart being operated on. Thes "units" can be a fixed amount of cash, a fixed amount of contracts, or a floating amount based on the last profits made. I recommend checking my previous strat to figure the case of contracts .
So, any trading price is the amount of "things" you get for some "cash". The things are the first unit, the "cash" is the second. Some examples:
XAU/USD - 1 xau oz is worth x dollars
BTC/USD - 1 bitcoin is worth x dollars
GBP/EUR - 1 pound is worth x euros
To add to confusion, a lot of markets the "unit size" is different from what the strat thinks it is. An options contract is 100 shares(the unit), 1 xau contract is 10 oz(units), 1 eur/usd contract is 100k euros and so on... so, after figuring out how the sizes work in a strat, then the sizes must be adapted for the specific market in question.
The choice os using the ETHUSD pair is because:
1 - you can buy 1eth, unlike a gold contract for example, so 1 "unit" = 1 eth, easier to get
2 - ETH is around 12 bucks, wich gives round numbers on the math, easier to wrap the brains around :)
3- is an unusual pair, so the regular contract sizes don't apply, and the brain is not conditioned to work inside the box ;)
You will have to access the script properties, to change the values. As these values are changed you will see exactly the differences in the values of the strat.
Text is too long, check the comments for all the cases
Understanding contract sizes in a strategyThis simple strat fires up on green bars, down on red bars. cannot get any simpler. So, it's a good example to check how returns are calculated.
First, the internal firing mechanism for the strategy.entry function is something hardcore. As result, the entry points can be confusing, and seem to appear in a wrong bar (as the 2nd and 3rd signals are good examples), but i'll put that aside to keep it simple. And, because i don't yet get it myself ;)
The example is simple, so that numbers can be followed easy. Chart in BTC/USD, so USD is the "base" currency used by strat to calculate. A contract/unit is the value of 1 unit in base currency. 1 Apple share is 600$, 1 bitcoin is 600$, 1 oz gold is 1330 bucks. So, here in each bar, the value of 1 contract is the value of the BTC in USD. simple as that.
The strat properties, can be passed as input fields (line 2) or accessed/changed in the right click->properties pop-up. To make it easier, initial capital is 1000 bucks, and "order size" is 1 contract. This means that the strat will open a position of 1 BTC when it fires. Value "Initial capital" makes no difference at all, at least with these choices. It's just for show. Try to put 1$ and 1 contract, the strat will still trade anyway. It manages to trade 1 contract(or BTC) values at ~600$, with a single dollar. nice ;)
Check the chart. see the little blue "BarUp +1" ? that's it, strat goes long 1 BTC. there's a little blue triangle on the bar, points to the value of entry.
Then later, on second move, the "BarDn -2", the strat goes short 2BTC. 1BTC to close the long +1 more to open a short.
The profit here is the difference between the value of the long opening and the long closing. The extra BTC (shorted) is part of the next position. Since this dumb strat just reverses the direction, there are always +2, -2 , +2.... 1 to close previous position, 1 to open another. At the strategy tester tab, the option "list of trades" shows in details each of the moves
Checking each move and comparing what we see with the chart itself helps to achieve ilumination :)
Bonus feature: as soon as you get it, try to increase the option "pyramiding" and see how the strat adds more contracts, and how it reverses the positions. sometimes it even makes sense!!!! :)
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