The Divergent LibraryLibrary "TheDivergentLibrary"
The Divergent Library is only useful when combined with the Pro version of The Divergent - Advanced divergence indicator . This is because the Basic (free) version of The Divergent does not expose the "Divergence Signal" value.
Usage instructions:
1. Create a new chart
2. Add The Divergent (Pro) indicator to your chart
3. Create a new strategy, import this library, add a "source" input, link it to "The Divergent: Divergence Signal", and use the library to decode the divergence signals from The Divergent (You can find example strategy code published in our profile)
4. Act on the divergences signalled by The Divergent
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isRegularBullishEnabled(context) Returns a boolean value indicating whether Regular Bullish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Regular Bullish divergence detection is enabled in The Divergent.
isHiddenBullishEnabled(context) Returns a boolean value indicating whether Hidden Bullish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Hidden Bullish divergence detection is enabled in The Divergent.
isRegularBearishEnabled(context) Returns a boolean value indicating whether Regular Bearish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Regular Bearish divergence detection is enabled in The Divergent.
isHiddenBearishEnabled(context) Returns a boolean value indicating whether Hidden Bearish divergence detection is enabled in The Divergent.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating whether Hidden Bearish divergence detection is enabled in The Divergent.
getPivotDetectionSource(context) Returns the 'Pivot Detection Source' setting of The Divergent. The returned value can be either "Oscillator" or "Price".
Parameters:
context : The context of The Divergent Library.
Returns: One of the following string values: "Oscillator" or "Price".
getPivotDetectionMode(context) Returns the 'Pivot Detection Mode' setting of The Divergent. The returned value can be either "Bodies" or "Wicks".
Parameters:
context : The context of The Divergent Library.
Returns: One of the following string values: "Bodies" or "Wicks".
isLinked(context) Returns a boolean value indicating the link status to The Divergent indicator.
Parameters:
context : The context of The Divergent Library.
Returns: A boolean value indicating the link status to The Divergent indicator.
init(firstBarSignal, displayLinkStatus, debug) Initialises The Divergent Library's context with the signal produced by The Divergent on the first bar. The value returned from this function is called the "context of The Divergent Library". Some of the other functions of this library requires you to pass in this context.
Parameters:
firstBarSignal : The signal from The Divergent indicator on the first bar.
displayLinkStatus : A boolean value indicating whether the Link Status window should be displayed in the bottom left corner of the chart. Defaults to true.
debug : A boolean value indicating whether the Link Status window should display debug information. Defaults to false.
Returns: A bool array containing the context of The Divergent Library.
processSignal(signal) Processes a signal from The Divergent and returns a 5-tuple with the decoded signal: [ int divergenceType, int priceBarIndexStart, int priceBarIndexEnd, int oscillatorBarIndexStart, int oscillatorBarIndexEnd]. `divergenceType` can be one of the following values: na → No divergence was detected, 1 → Regular Bullish, 2 → Regular Bullish early, 3 → Hidden Bullish, 4 → Hidden Bullish early, 5 → Regular Bearish, 6 → Regular Bearish early, 7 → Hidden Bearish, 8 → Hidden Bearish early.
Parameters:
signal : The signal from The Divergent indicator.
Returns: A 5-tuple with the following values: [ int divergenceType, int priceBarIndexStart, int priceBarIndexEnd, int oscillatorBarIndexStart, int oscillatorBarIndexEnd].
Strategies
TradingPortfolioLibrary "TradingPortfolio"
Simple functions for portfolio management. A portfolio is essentially
a float array with 3 positions that gets passed around
into these functions that ensure it gets properly updated as trading ensues.
An example usage:
import hugodanielcom/TradingPortfolio/XXXX as portfolio
var float my_portfolio = portfolio.init(0.0, strategy.initial_capital) // Initialize the portfolio with the strategy capital
if close < 10.0
portfolio.buy(my_portfolio, 10.0, close) // Buy when the close is below 10.0
plot(portfolio.total(my_portfolio), title = "Total portfolio value")
get_balance(portfolio) Gets the number of tokens and fiat available in the supplied portfolio.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The tokens and fiat in a tuple
set_balance(portfolio, new_crypto, new_fiat) Sets the portfolio number of tokens and fiat amounts. This function overrides the current values in the portfolio and sets the provided ones as the new portfolio.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
new_crypto : The new amount of tokens in the portfolio.
new_fiat : The new amount of fiat in the portfolio
Returns: The tokens and fiat in a tuple
init(crypto, fiat) This function returns a clean portfolio. Start by calling this function and pass its return value as an argument to the other functions in this library.
Parameters:
crypto : The initial amount of tokens in the portfolio (defaults to 0.0).
fiat : The initial amount of fiat in the portfolio (defaults to 0.0).
Returns: The portfolio (a float )
crypto(portfolio) Gets the number of tokens in the portfolio
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The amount of tokens in the portfolio
fiat(portfolio) Gets the fiat in the portfolio
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The amount of fiat in the portfolio
retained(portfolio) Gets the amount of reatined fiat in the portfolio. Retained fiat is not considered as part of the balance when buying/selling, but it is considered as part of the total of the portfolio.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
Returns: The amount of retained fiat in the portfolio
retain(portfolio, fiat_to_retain) Sets the amount of fiat to retain. It removes the amount from the current fiat in the portfolio and marks it as retained.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
fiat_to_retain : The amount of fiat to remove and mark as retained.
Returns: void
total(portfolio, token_value) Calculates the total fiat value of the portfolio. It multiplies the amount of tokens by the supplied value and adds to the result the current fiat and retained amount.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
token_value : The fiat value of a unit (1) of token
Returns: A float that corresponds to the total fiat value of the portfolio (retained amount included)
ratio(portfolio, token_value) Calculates the ratio of tokens / fiat. The retained amount of fiat is not considered, only the active fiat being considered for trading.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
token_value : The fiat value of a unit (1) of token
Returns: A float between 1.0 and 0.0 that corresponds to the portfolio ratio of token / fiat (i.e. 0.6 corresponds to a portfolio whose value is made by 60% tokens and 40% fiat)
can_buy(portfolio, amount, token_value) Asserts that there is enough balance to buy the requested amount of tokens.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to assert that can be bought
token_value : The fiat value of a unit (1) of token
Returns: A boolean value, true if there is capacity to buy the amount of tokens provided.
can_sell(portfolio, amount) Asserts that there is enough token balance to sell the requested amount of tokens.
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to assert that can be sold
Returns: A boolean value, true if there is capacity to sold the amount of tokens provided.
buy(portfolio, amount, token_value) Adjusts the portfolio state to perform the equivalent of a buy operation (as in, buy the requested amount of tokens at the provided value and set the portfolio accordingly).
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to buy
token_value : The fiat value of a unit (1) of token
Returns: A boolean value, true the requested amount of tokens was "bought" and the portfolio updated. False if nothing was changed.
sell(portfolio, amount, token_value) Adjusts the portfolio state to perform the equivalent of a sell operation (as in, sell the requested amount of tokens at the provided value and set the portfolio accordingly).
Parameters:
portfolio : A portfolio float array as created by the `init()` function.
amount : The amount of tokens to sell
token_value : The fiat value of a unit (1) of token
Returns: A boolean value, true the requested amount of tokens was "sold" and the portfolio updated. False if nothing was changed.
StringtoNumberThis library is used to convert Text type numbers are numbers.
Library "StringtoNumber"
str1 = '12340' , vv = numstrToNum(str1)
numstrToNum()
Example
// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © hapharmonic
//@version=5
indicator("My Script")
import hapharmonic/StringtoNumber/1 as CV
TF = '240'
GETTF = CV.numstrToNum(TF)
L = label.new(bar_index, high, '|| numstrToNum :>> || ' + str.tostring(GETTF), style=label.style_label_down,size=size.large)
label.delete(L )
PureRebalanceLibrary "PureRebalance"
A rebalance function that is pure.
Depends only on its arguments to perform the necessary calculations.
rebalance(token_price, portfolio_token_amount, portfolio_fiat_amount, rebalance_ratio) Rebalances a portfolio made of tokens and fiat to a given ratio of tokens per fiat
Parameters:
token_price : The value of a single unit (1) token
portfolio_token_amount : The number of tokens in the portfolio
portfolio_fiat_amount : Fiat available in the portfolio
rebalance_ratio : The ratio of token value / fiat that the portfolio should have after the rebalance (0.5 is used if no argument is supplied).
Returns: The number of tokens to buy or sell in order to achieve the desired portfolio ratio passed as argument (a positive value is returned if the tokens are to be bought, and negative value if the tokens are to be sold).
MHCustomSpotTradingLibraryLibrary "MHCustomSpotTradingLibrary"
HMA(float, float)
Parameters:
float : _src price data
float : _length Period
Returns: Hull Moving Average
EHMA(float, float)
Parameters:
float : _src price data
float : _length Period
Returns: EHMA Moving Average
THMA(float, float)
Parameters:
float : _src price data
float : _length Period
Returns: THMA Moving Average
HullMode(string, float, float)
Parameters:
string : modeSwitch Hull type
float : _src price data
float : _length Period
Returns: A Moving Average
start_Dates(int, int, int)
Parameters:
int : year
int : month
int : day
Returns: Start Date
stop_Dates(int, int, int)
Parameters:
int : year
int : month
int : day
Returns: Stop Date
f_print(string, int, int)
Parameters:
string : _text
int : col
int : row
Returns: Nothing
showStats()
greenCandle()
redCandle()
getDecimals()
truncate()
toWhole()
toPips()
getMA()
getEAP()
barsAboveMA()
barsBelowMA()
barsCrossedMA()
getPullbackBarCount()
getBodySize()
getTopWickSize()
getBottomWickSize()
getBodyPercent()
isHammer()
isStar()
isDoji()
isBullishEC()
isBearishEC()
timeFilter()
dateFilter()
dayFilter()
atrFilter()
fillCell()
GenericTradingLibrary "GenericTrading"
This library aims to collect rare but useful operations for
get_most_recent_long_or_short_position_closed_index() : returns most recent long/short closed bar index.
get_most_recent_long_or_short_position_open_index() : returns most recent long/short closed bar index.
These two functions designed to help to speed up the coding for strategies that contains "re-enter" logic.
These two functions also could applies in the situations where time-count is needed in your condition.
Bjorgum MTF MAScope:
Up to 3 MA's can be applied at the users discretion
Choose between 10 different average types including favorites from the Bjorgum series from HEMA to Reversal T3's
Each MA can be independently set
Go Multi-timeframe! Any MA can be set to any timeframe of reference you choose (ex. using 3 different timeframes of higher resolution to your chart reference to establish a multi-time frame trend)
RSI HEATMAP. Use the bar color or the MA color selection toggle to set your color to reference RSI on a gradient. This helps to establish clear visual reference to momentum on top of trend analysis
Assigning RSI bar color to an MA of a higher time frame can allow you to see visual reference of momentum of the greater trend that may be at play.
Example: trying to get short on a 15min while your 1hr and 4hr RSI Heatmap burns deep in oversold.
RSI color can be assigned to the bar color, the MA (1,2 or 3), or both. Alternatively, bar color can be assigned to a more simple rising/ falling MA color and price above/ below the MA for bar color.
ALL MTF FUNCTIONS ARE NON_REPAINTING.
The Idea
The original inspiration behind the script came from an observation of a constant struggle of Pinecoders of both youth or experience to find accurate multi-timeframe indicators that do not repaint, and appear as should on historical bars, while performing reliably in real time. I encourage you to scroll the Pinecoders FAQ on a recent piece explaining the difficulties and caveats of different approaches, but I would like to reference the elimination of the historical bar offset for THIS purpose. MA's are based on closing prices, that is to say they are confirmed and will not change once the bar has closed. There is no need to offset these for historical reference. The purpose of the historical offset is best exemplified, for example, with an intraday strategy involving daily breakouts. Let's entertain going long on a break to a new daily high. We would not know that in real time so we need to reference the previous bars close for BACKTESTING purposes, otherwise we get "lookahead bias". There is no shortage of strategies unintentionally employing this bias out there (I'm sure you have come across them with their unrealistic results).
There is no worry of repainting with the MTF security function included within this script, nor will it offset for something involving lookahead bias. I encourage those that are playing with the code or perhaps writing strategies of their own to borrow the functions within. There is also a function that will return the 10 MA variable selection - yours to keep.
With all that in mind, I wanted a practical script that is easily deployable in everyday trading for the average trader that gives the user a firm grip on their trading steering wheel equipped with the feel of the road. Incorporating several MA's of differing times with the RSI heatmap gives a good visual reference and feel to your trading environment, while offering a level of customization that will fit an individuals personal trading style. The RSI heatmap gradient is per percentage between 30 and 70 and your 2 colors - anything outside of those levels gives you the vibrant bias color
Below I walk through 2 examples of live trades scenarios I made using the mindset the script offers.
The Trade
LONG IZEA
Timeframe: 4hr chart
- 3 Tilson MA's of varying length are deployed at varying intraday time frames
- A bullish pattern in an uptrend offers a possible trade allowing entry from a low risk point from the pattern low
- See the chart for notes and observations using the script
- Notice how the heatmap brightens up as price extends far away from the averages - your risk has elevated for a new position
- Notice the heatmap cool off while price action consolidates sideways
The Trade
SHORT BTCUSDT
Timeframe: 4hr chart
- 3 EMAs are employed - same length (50), with 3 successive timeframe resolutions
- A downtrend is formed with a sharp move to stack the EMAs into layers
- A pull back opportunity presents itself in bearish consolidation
- Notice a Doji star at resistance establishing a swing high as RSI cools off into the EMA dynamic resistance for entry
- An '"equal legs" measured move can be used for a trade target with a stop out above the swing high.
- Again, take note of distance from EMA's, the heatmap in combination with trend development surrounding the MA's
These were just two ideas to show you an example of how to implement some strategy into your trading and to get some interesting use from the indicator. Hope you enjoyed the read and happy trading.
Three moving average strategies
If the short term (Default 7) moving average cuts the medium term (default 25) moving average, BUY. Conversely, it generates the SELL signal. If the long term (Default 99) moving average cuts its short term moving average, Quick SELL. Conversely, it can be interpreted as Fast SELL. You can change the moving average and the number of days as you wish, and you can trade in the short or long term. I hope it was useful.
Strategy Quadratic Semaphore I add a basic strategy for the "Quadratic traffic light" indicator, it can help to find a good configuration.
Regards.
BEST Trailing Stop StrategyHello traders
Here we go again.... with the second strategy snippet.
Reminder: the first snipper was a Trailing Profit strategy script
What's on the menu?
A trailing stop is designed to protect gains by enabling a trade to remain open and continue to profit as long as the price is moving in the investor's favor.
The order closes the trade if the price changes direction by a specified percentage or dollar amount.
Trigger me I'm famous
I developed many trading strategies in my career and often I've been asked to trigger a trailing-stop once a certain % move has been made.
On the screenshot below, the SL trigger is plotted in maroon.
Once the price goes past that level for the first time, I'll start trailing the trailing stop level.
In other words, when we see a price makes an interesting move in percentage value - we decide to trail the stop for at least not losing any more
All the BEST
Dave
BEST Trailing Take Profit StrategyHello traders
Hope you enjoyed your weekend on my behalf. Was staying home working ... ^^
This is my first strategy educational post I'm doing ever
While I'm generally against posting strategies because it's very easy to fake performance numbers... I cannot prevent myself from sharing a few cool strategy snippets anyway.
So from now on, I'll be sharing a few strategies also - generally not to showcase performance but only to show what pinescript can do.
As once again strategy performance can be faked is so many ways... :)
What's on the menu?
We all know what a trailing-stop is. right? right? Ok... sharing the definition here :)
A trailing stop is designed to protect gains by enabling a trade to remain open and continue to profit as long as the price is moving in the investor's favor. The order closes the trade if the price changes direction by a specified percentage or dollar amount.
But...do you know what a trailing profit is?
Short definition : Well the same but with your profit limit order.
Long definition : A trailing profit is designed to increase your gains by enabling a trade to remain open and continue to profit as long as the price is moving in the investor's favor. The order closes the trade if the price hits the trailing profit level specified percentage or dollar amount.
Some trading strategies used both a trailing stop AND trailing profit. Not making any recommendation here - only sharing what's possible in the realm of trading and pinescripting
Trigger me I'm famous
I developed many trading strategies in my career and often I've been asked to trigger a trailing-stop or trailing-profit once a certain % move has been made.
I integrated here a Take Profit trigger - once hit, it will activate the trailing profit
On the screenshot below, the TP trigger is plotted in orange. Once the price goes past that level for the first time, I'll start trailing the profit level.
In other words, when we see a price makes an interesting move in percentage value - we decide to offset the profit as we concluded that once it reached such distance - then it leads often to more profit
Of course, using only a trailing profit without stop/trailing-stop/invalidation isn't smart and the surest way to kiss goodbye a trading capital and trading and your good mood
See you tomorrow for another strategy snippet
All the BEST
Dave
Great Expectations [LucF]Great Expectations helps traders answer the question: What is possible? It is a powerful question, yet exploration of the unknown always entails risk. A more complete set of questions better suited to traders could be:
What opportunity exists from any given point on a chart?
What portion of this opportunity can be realistically captured?
What risk will be incurred in trying to do so, and how long will it take?
Great Expectations is the result of an exploration of these questions. It is a trade simulator that generates visual and quantitative information to help strategy modelers visually identify and analyse areas of optimal expectation on charts, whether they are designing automated or discretionary strategies.
WARNING: Great Expectations is NOT an indicator that helps determine the current state of a market. It works by looking at points in the past from which the future is already known. It uses one definition of repainting extensively (i.e. it goes back in the past to print information that could not have been know at the time). Repainting understood that way is in fact almost all the indicator does! —albeit for what I hope is a noble cause. The indicator is of no use whatsoever in analyzing markets in real-time. If you do not understand what it does, please stay away!
This is an indicator—not a strategy that uses TradingView’s backtesting engine. It works by simulating trades, not unlike a backtest, but with the crucial difference that it assumes a trade (either long or short) is entered on all bars in the historic sample. It walks forward from each bar and determines possible outcomes, gathering individual trade statistics that in turn generate precious global statistics from all outcomes tested on the chart.
Great Expectations provides numbers summarizing trade results on all simulations run from the chart. Those numbers cannot be compared to backtest-produced numbers since all non-filtered bars are examined, even if an entry was taken on the bar immediately preceding the current one, which never happens in a backtest. This peculiarity does NOT invalidate Great Expectations calculations; it just entails that results be considered under a different light. Provided they are evaluated within the indicator’s context, they can be useful—sometimes even more than backtesting results, e.g. in evaluating the impact of parameter-fitting or variations in entry, exit or filtering strats.
Traders and strategy modelers are creatures of hope often suffering from blurred vision; my hope is that Great Expectations will help them appraise the validity of their setup and strat intuitions in a realistic fashion, preventing confirmation bias from obstructing perspective—and great expectations from turning into financial great deceptions.
USE CASES
You’ve identified what looks like a promising setup on other indicators. You load Great Expectations on the chart and evaluate if its high-expectation areas match locations where your setup’s conditions occur. Unless today is your lucky day, chances are the indicator will help you realize your setup is not as promising as you had hoped.
You want to get a rough estimate of the optimal trade duration for a chart and you don’t mind using the entry and exit strategies provided with the indicator. You use the trade length readouts of the indicator.
You’re experimenting with a new stop strategy and want to know how long it will keep you in trades, on average. You integrate your stop strategy in the indicator’s code and look at the average trade length it produces and the TST ratio to evaluate its performance.
You have put together your own entry and exit criteria and are looking for a filter that will help you improve backtesting results. You visually ascertain the suitability of your filter by looking at its results on the charts with great Expectations, to see if your filter is choosing its areas correctly.
You have a strategy that shows backtested trades on your chart. Great Expectations can help you evaluate how well your strategy is benefitting from high-opportunity areas while avoiding poor expectation spots.
You want more complete statistics on your set of strategies than what backtesting will provide. You use Great Expectations, knowing that it tests all bars in the sample that correspond to your criteria, as opposed to backtesting results which are limited to a subset of all possible entries.
You want to fool your friends into thinking you’ve designed the holy grail of indicators, something that identifies optimal opportunities on any chart; you show them the P&L cloud.
FEATURES
For one trade
At any given point on the chart, assuming a trade is entered there, Great Expectations shows you information specific to that trade simulation both on the chart and in the Data Window.
The chart can display:
the P & L Cloud which shows whether the trade ended profitably or not, and by how much,
the Opportunity & Risk Cloud which the maximum opportunity and risk the simulation encountered. When superimposed over the P & L cloud, you will see what I call the managed opportunity and risk, i.e the portion of maximum opportunity that was captured and the portion of the maximum risk that was incurred,
the target and if it was reached,
a background that uses a gradient to show different levels of trade length, P&L or how frequently the target was reached during simulation.
The Data Window displays more than 40 values on individual trades and global results. For any given trade you will know:
Entry/Exit levels, including slippage impact,
It’s outcome and duration,
P/L achieved,
The fraction of the maximum opportunity/risk managed by the trade.
For all trades
After going through all the possible trades on the chart, the indicator will provide you with a rare view of all outcomes expressed with the P&L cloud, which allows us to instantly see the most/least profitable areas of a chart using trade data as support, while also showing its relationship with the opportunity/risk encountered during the simulation. The difference between the two clouds is the managed opportunity and risk.
The Data Window will present you with numbers which we will go through later. Some of them are: average stop size, P/L, win rate, % opportunity managed, trade lengths for different types of trade outcomes and the TST (Target:Stop Travel) ratio.
Let’s see Great Expectations in action… and remember to open your Data Window!
INPUTS
Trade direction : You must first choose if you wish to look at long or short trades. Because of the way the indicator works and the amount of visual information on the chart, it is only practical to look at one type of trades at a time. The default is Longs.
Maximum trade Length (MaxL) : This is the maximum walk forward distance the simulator will go in analyzing outcomes from any given point in the past. It also determines the size of the dead zone among the chart’s last bars. A red background line identifies the beginning of the dead zone for which not enough bars have elapsed to analyze outcomes for the maximum trade length defined. If an ATR-based entry stop is used, that length is added to the wait time before beginning simulations, so that the first entry starts with a clean ATR value. On a sample of around 16000 bars, my tests show that the indicator runs into server errors at lengths of around 290, i.e. having completed ~4,6M simulation loop iterations. That is way too high a length anyways; 100 will usually be amply enough to ring out all the possibilities out of a simulation, and on shorter time frames, 30 can be enough. While making it unduly small will prevent simulations of expressing the market’s potential, the less you use, the faster the indicator will run. The default is 40.
Unrealized P&L base at End of Trade (EOT) : When a simulation ends and the trade is still open, we calculate unrealized P&L from an exit order executed from either the last in-trade stop on the previous bar, or the close of the last bar. You can readily see the impact of this selection on the chart, with the P&L cloud. The default is on the close.
Display : The check box besides the title does nothing.
Show target : Shows a green line displaying the trade’s target expressed as a multiple of X, i.e. the amplitude of the entry stop. I call this value “X” and use it as a unit to express profit and loss on a trade (some call it “R”). The line is highlighted for trades where the close reached the target during the trade, whether the trade ended in profit or loss. This is also where you specify the multiple of X you wish to use in calculating targets. The multiple is used even if targets are not displayed.
Show P&L Cloud : The cloud allows traders to see right away the profitable areas of the chart. The only line printed with the cloud is the “end of trade line” (EOT). The EOT line is the only way one can see the level where a trade ended on the chart (in the Data Window you can see it as the “Exit Fill” value). The EOT level for the trade determines if the trade ended in a profit or a loss. Its value represents one of the following:
- fill from order executed at close of bar where stop is breached during trade (which produces “Realized P/L”),
- simulation of a fill pseudo-fill at the user-defined EOT level (last close or stop level) if the trade runs its course through MaxL bars without getting stopped (producing Unrealized P/L).
The EOT line and the cloud fill print in green when the trade’s outcome is profitable and in red when it is not. If the trade was closed after breaching the stop, the line appears brighter.
Show Opportunity&Risk Cloud : Displays the maximum opportunity/risk that was present during the trade, i.e. the maximum and minimum prices reached.
Background Color Scheme : Allows you to choose between 3 different color schemes for the background gradients, to accommodate different types of chart background/candles. Select “None” if you don’t want a background.
Background source : Determines what value will be used to generate the different intensities of the gradient. You can choose trade length (brighter is shorter), Trade P&L (brighter is higher) or the number of times the target was reached during simulation (brighter is higher). The default is Trade Length.
Entry strat : The check box besides the title does nothing. The default strat is All bars, meaning a trade will be simulated from all bars not excluded by the filters where a MaxL bars future exists. For fun, I’ve included a pseudo-random entry strat (an indirect way of changing the seed is to vary the starting date of the simulation).
Show Filter State : Displays areas where the combination of filters you have selected are allowing entries. Filtering occurs as per your selection(s), whether the state is displayed or not. The effect of multiple selections is additive. The filters are:
1. Bar direction: Longs will only be entered if close>open and vice versa.
2. Rising Volume: Applies to both long and shorts.
3. Rising/falling MA of the length you choose over the number of bars you choose.
4. Custom indicator: You can feed your own filtering signal through this from another indicator. It must produce a signal of 1 to allow long entries and 0 to allow shorts.
Show Entry Stops :
1. Multiple of user-defined length ATR.
2. Fixed percentage.
3. Fixed value.
All entry stops are calculated using the entry fill price as a reference. The fill price is calculated from the current bar’s open, to which slippage is added if configured. This simulates the case where the strategy issued the entry signal on the previous bar for it to be executed at the next bar’s open.
The entry stop remains active until the in-trade stop becomes the more aggressive of the two stops. From then on, the entry stop will be ignored, unless a bar close breaches the in-trade stop, in which case the stop will be reset with a new entry stop and the process repeats.
Show In-trade stops : Displays in bright red the selected in-trade stop (be sure to read the note in this section about them).
1. ATR multiple: added/subtracted from the average of the two previous bars minimum/maximum of open/close.
2. A trailing stop with a deviation expressed as a multiple of entry stop (X).
3. A fixed percentage trailing stop.
Trailing stops deviations are measured from the highest/lowest high/low reached during the trade.
Note: There is a twist with the in-trade stops. It’s that for any given bar, its in-trade stop can hold multiple values, as each successive pass of the advancing simulation loops goes over it from a different entry points. What is printed is the stop from the loop that ended on that bar, which may have nothing to do with other instances of the trade’s in-trade stop for the same bar when visited from other starting points in previous simulations. There is just no practical way to print all stop values that were used for any given bar. While the printed entry stops are the actual ones used on each bar, the in-trade stops shown are merely the last instance used among many.
Include Slippage : if checked, slippage will be added/subtracted from order price to yield the fill price. Slippage is in percentage. If you choose to include slippage in the simulations, remember to adjust it by considering the liquidity of the markets and the time frame you’ll be analyzing.
Include Fees : if checked, fees will be subtracted/added to both realized an unrealized trade profits/losses. Fees are in percentage. The default fees work well for crypto markets but will need adjusting for others—especially in Forex. Remember to modify them accordingly as they can have a major impact on results. Both fees and slippage are included to remind us of their importance, even if the global numbers produced by the indicator are not representative of a real trading scenario composed of sequential trades.
Date Range filtering : the usual. Just note that the checkbox has to be selected for date filtering to activate.
DATA WINDOW
Most of the information produced by this indicator is made available in the Data Window, which you bring up by using the icon below the Watchlist and Alerts buttons at the right of the TV UI. Here’s what’s there.
Some of the information presented in the Data Window is standard trade data; other values are not so standard; e. g. the notions of managed opportunity and risk and Target:Stop Travel ratio. The interplay between all the values provided by Great Expectations is inherently complex, even for a static set of entry/filter/exit strats. During the constant updating which the habitual process of progressive refinement in building strategies that is the lot of strategy modelers entails, another level of complexity is no doubt added to the analysis of this indicator’s values. While I don’t want to sound like Wolfram presenting A New Kind of Science , I do believe that if you are a serious strategy modeler and spend the time required to get used to using all the information this indicator makes available, you may find it useful.
Trade Information
Entry Order : This is the open of the bar where simulation starts. We suppose that an entry signal was generated at the previous bar.
Entry Fill (including slip.) : The actual entry price, including slippage. This is the base price from which other values will be calculated.
Exit Order : When a stop is breached, an exit order is executed from the close of the bar that breached the stop. While there is no “In-trade stop” value included in the Data Window (other than the End of trade Stop previously discussed), this “Exit Order” value is how we can know the level where the trade was stopped during the simulation. The “Trade Length” value will then show the bar where the stop was breached.
Exit Fill (including slip.) : When the exit order is simulated, slippage is added to the order level to create the fill.
Chart: Target : This is the target calculated at the beginning of the simulation. This value also appear on the chart in teal. It is controlled by the multiple of X defined under the “Show Target” checkbox in the Inputs.
Chart: Entry Stop : This value also appears on the chart (the red dots under points where a trade was simulated). Its value is controlled by the Entry Strat chosen in the Inputs.
X (% Fill, including Fees) and X (currency) : This is the stop’s amplitude (Entry Fill – Entry Stop) + Fees. It represents the risk incurred upon entry and will be used to express P&L. We will show R expressed in both a percentage of the Entry Fill level (this value), and currency (the next value). This value represents the risk in the risk:reward ratio and is considered to be a unit of 1 so that RR can be expressed as a single value (i.e. “2” actually meaning “1:2”).
Trade Length : If trade was stopped, it’s the number of bars elapsed until then. The trade is then considered “Closed”. If the trade ends without being stopped (there is no profit-taking strat implemented, so the stop is the only exit strat), then the trade is “Open”, the length is MaxL and it will show in orange. Otherwise the value will print in green/red to reflect if the trade is winning/losing.
P&L (X) : The P&L of the trade, expressed as a multiple of X, which takes into account fees paid at entry and exit. Given our default target setting at 2 units of “X”, a trade that closes at its target will have produced a P&L of +2.0, i.e. twice the value of X (not counting fees paid at exit ). A trade that gets stopped late 50% further that the entry stop’s level will produce a P&L of -1.5X.
P&L (currency, including Fees) : same value as above, but expressed in currency.
Target first reached at bar : If price closed above the target during the trade (even if it occurs after the trade was stopped), this will show when. This value will be used in calculating our TST ratio.
Times Stop/Target reached in sim. : Includes all occurrences during the complete simulation loop.
Opportunity (X) : The highest/lowest price reached during a simulation, i.e. the maximum opportunity encountered, whether the trade was previously stopped or not, expressed as a multiple of X.
Risk (X) : The lowest/highest price reached during a simulation, i.e. the maximum risk encountered, whether the trade was previously stopped or not, expressed as a multiple of X.
Risk:Opportunity : The greater this ratio, the greater Opportunity is, compared to Risk.
Managed Opportunity (%) : The portion of Opportunity that was captured by the highest/low stop position, even if it occurred after a previous stop closed the trade.
Managed Risk (%) : The portion of risk that was protected by the lowest/highest stop position, even if it occurred after a previous stop closed the trade. When this value is greater than 100%, it means the trade’s stop is protecting more than the maximum risk, which is frequent. You will, however, never see close to those values for the Managed Opportunity value, since the stop would have to be higher than the Maximum opportunity. It is much easier to alleviate the risk than it is to lock in profits.
Managed Risk:Opportunity : The ratio of the two preceding values.
Managed Opp. vs. Risk : The Managed Opportunity minus the Managed Risk. When it is negative, which is most often is, it means your strat is protecting a greater portion of the risk than it captures opportunity.
Global Numbers
Win Rate(%) : Percentage of winning trades over all entries. Open trades are considered winning if their last stop/close (as per user selection) locks in profits.
Avg X%, Avg X (currency) : Averages of previously described values:.
Avg Profitability/Trade (APPT) : This measures expectation using: Average Profitability Per Trade = (Probability of Win × Average Win) − (Probability of Loss × Average Loss) . It quantifies the average expectation/trade, which RR alone can’t do, as the probabilities of each outcome (win/lose) must also be used to calculate expectancy. The APPT combine the RR with the win rate to yield the true expectancy of a strategy. In my usual way of expressing risk with X, APPT is the equivalent of the average P&L per trade expressed in X. An APPT of -1.5 means that we lose on average 1.5X/trade.
Equity (X), Equity (currency) : The cumulative result of all trade outcomes, expressed as a multiple of X. Multiplied by the Average X in currency, this yields the Equity in currency.
Risk:Opportunity, Managed Risk:Opportunity, Managed Opp. vs. Risk : The global values of the ones previously described.
Avg Trade Length (TL) : One of the most important values derived by going through all the simulations. Again, it is composed of either the length of stopped trades, or MaxL when the trade isn’t stopped (open). This value can help systems modelers shape the characteristics of the components they use to build their strategies.
Avg Closed Win TL and Avg Closed Lose TL : The average lengths of winning/losing trades that were stopped.
Target reached? Avg bars to Stop and Target reached? Avg bars to Target : For the trades where the target was reached at some point in the simulation, the number of bars to the first point where the stop was breached and where the target was reached, respectively. These two values are used to calculate the next value.
TST (Target:Stop Travel Ratio) : This tracks the ratio between the two preceding values (Bars to first stop/Bars to first target), but only for trades where the target was reached somewhere in the loop. A ratio of 2 means targets are reached twice as fast as stops.
The next values of this section are counts or percentages and are self-explanatory.
Chart Plots
Contains chart plots of values already describes.
NOTES
Optimization/Overfitting: There is a fine line between optimizing and overfitting. Tools like this indicator can lead unsuspecting modelers down a path of overfitting that often turns strategies into over-specialized beasts that do not perform elegantly when confronted to the real-world. Proven testing strategies like walk forward analysis will go a long way in helping modelers alleviate this risk.
Input tuning: Because the results generated by the indicator will vary with the parameters used in the active entry, filtering and exit strats, it’s important to realize that although it may be fun at first, just slapping the default settings on a chart and time frame will not yield optimal nor reliable results. While using ATR as often as possible (as I do in this indicator) is a good way to make strat parametrization adaptable, it is not a foolproof solution.
There is no data for the last MaxL bars of the chart, since not enough trade future has elapsed to run a simulation from MaxL bars back.
Modifying the code: I have tried to structure the code modularly, even if that entails a larger code base, so that you can adapt it to your needs. I’ve included a few token components in each of the placeholders designed for entry strategies, filters, entry stops and in-trade stops. This will hopefully make it easier to add your own. In the same spirit, I have also commented liberally.
You will find in the code many instances of standard trade management tasks that can be lifted to code TV strategies where, as I do in mine, you manage everything yourself and don’t rely on built-in Pine strategy functions to act on your trades.
Enjoy!
THANKS
To @scarf who showed me how plotchar() could be used to plot values without ruining scale.
To @glaz for the suggestion to include a Chandelier stop strat; I will.
To @simpelyfe for the idea of using an indicator input for the filters (if some day TV lets us use more than one, it will be useful in other modules of the indicator).
To @RicardoSantos for the random generator used in the random entry strat.
To all scripters publishing open source on TradingView; their code is the best way to learn.
To my trading buddies Irving and Bruno; who showed me way back how pro traders get it done.
Combo Strategy 123 Reversal and ADXR This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
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.
Secon strategy
The Average Directional Movement Index Rating (ADXR) measures the strength
of the Average Directional Movement Index (ADX). It's calculated by taking
the average of the current ADX and the ADX from one time period before
(time periods can vary, but the most typical period used is 14 days).
Like the ADX, the ADXR ranges from values of 0 to 100 and reflects strengthening
and weakening trends. However, because it represents an average of ADX, values
don't fluctuate as dramatically and some analysts believe the indicator helps
better display trends in volatile markets.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Strategies 123 Reversal and Accelerator Oscillator (AC) This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
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 Accelerator Oscillator has been developed by Bill Williams
as the development of the Awesome Oscillator. It represents the
difference between the Awesome Oscillator and the 5-period moving
average, and as such it shows the speed of change of the Awesome
Oscillator, which can be useful to find trend reversals before the
Awesome Oscillator does.
WARNING:
- This script to change bars colors.
Combo Strategies 123 Reversal and Absolute Price Oscillator This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
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.
Secon strategy
The Absolute Price Oscillator displays the difference between two exponential
moving averages of a security's price and is expressed as an absolute value.
How this indicator works
APO crossing above zero is considered bullish, while crossing below zero is bearish.
A positive indicator value indicates an upward movement, while negative readings
signal a downward trend.
Divergences form when a new high or low in price is not confirmed by the Absolute Price
Oscillator (APO). A bullish divergence forms when price make a lower low, but the APO
forms a higher low. This indicates less downward momentum that could foreshadow a bullish
reversal. A bearish divergence forms when price makes a higher high, but the APO forms a
lower high. This shows less upward momentum that could foreshadow a bearish reversal.
WARNING:
- This script to change bars colors.
Combo Strategies 123 Reversal and 2/20 EMA This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
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.
Secon strategy
This indicator plots 2/20 exponential moving average. For the Mov
Avg X 2/20 Indicator, the EMA bar will be painted when the Alert criteria is met.
Please, use it only for learning or paper trading. Do not for real trading.
4Hours optimized CARHey!
I have been working on this script the lasts days, it's a collaboration with an uruguayan mate.
#####IMPORTANT#####
* It has been optimized for 4h charts, use it on other at your own risk.
* Always use Stop Loss, since it might give false signals after a long trend.
* I'm still working on it, I'm going to add candlestick pattern filtering.
Thanks for your support, greetings.
Cowabunga System from babypips.comPlease do read the information below as well, especially if you are new to Forex.
The Cowabunga System is a type of Mechanical Trading System that filters trades based on the trend of the 4 hour chart with EMAs and some other familiar indicators (RSI, Stochastics and MACD) while entering trades base on 15 minute chart.
I have coded (quite amateurishly) the basic system onto a 15 minute chart (the 4 hour settings are coded as well). The author says the system is to be traded off the 15 minute chart with the 4 hour chart only as a reference for trend direction.
4 Hour Chart Settings
5 EMA
10 EMA
Stochastics (10,3,3)
RSI (9)
Then we move onto the 15 minute chart, where he gives us the trade entry rules.
15 Minute Chart Settings
5 EMA
10 EMA
Stochastics (10,3,3)
RSI (9)
MACD (12,26,9)
Entry Rules - long entry rules used, obviously reverse these for shorting.
1. EMA must cross above the 10 EMA.
2. RSI must be greater than 50 and not overbought.
3. Stochastic must be headed up and not be in overbought territory.
4. MACD histogram must go from negative to positive OR be negative and start to increase in value.
What I did.
1. Set the RSI and Stochastic levels to avoid entries when they indicate overbought conditions for long and oversold conditions for short (80 and 20 levels).
2. Users can input specific times they want to backtest.
3. User's can configure profit targets, trailing stops and stops. Default is set it to was 100 pips profit target with a 40 pip trailing stop. (Note, when you are changing these values, please note that each pip is worth 10, so 100 pips is entered as 1000.)
The Cowabunga System from babypips.com is another popular and active system. The author, Pip Surfer, continues to post wins and losses with this system. It shows there is a lot of honesty and integrity with this system if the author keeps up to date even 10 years later and is not afraid of sharing the times the system causes losses.
As an example of this, here is post he shared just last week . It's almost like a journal, he gives specific times and reasons why he entered, lets the readers know when he was stopped out, etc. I think that what he does is equally important as his system.
To read more about this system, visit the thread on babypips.com, click here.
Simple Strategy Code StubThis is a very basic strategy implementation
Use as a code stub for your strategy code. I wrote it because I could not find one.
This particular strategy goes long on Tuesdays at 10 am and goes short at 3 pm on Thursdays.
Because US markets open at 9:30 you have to have your chart in 30 minute or less resolution for trades to fire.
You can gut that code and replace it with your own to start testing your own indicators and strategies.
If you build a strategy that doesn't use "hour" like I have this resolution requirement won't apply.
For giggles, compare its performance to other strategies. Weird, huh? About 53% effective on most equities and indexes.
This strategy does the minimum needed to get a strategy working
and uses default position sizes and buys at market with no stops.
Again, it is the minimal code stub needed to test an indicator/rule based strategy.
A great code reference for building more sophisticated strategies can be
found here =>
The code is written by @greatwolf and is very well structured for anyone looking to fully utilize
strategy features for position sizing, limit orders, stops and cancellations.
UCS_S_Stochastic Pop and Drop StrategyMy Contribution to Jake Bernstein Educational Series, Initiated by Chris Moody.
The Stochastic Pop was developed by Jake Bernstein and modified by David Steckler. Bernstein's original Stochastic Pop is a trading strategy that identifies price pops when the Stochastic Oscillator surges above 80. Steckler modified this strategy by adding conditional filters using the Average Directional Index (ADX) and the weekly Stochastic Oscillator.
Modifications
1. Weekly Stochastic Oscillator for Trading Bias = 5* Daily Stochastic
2. Optional Volume Confirmation, Custom Average Volume Length
Future Plans
1. Adding Triggers for Entry, Stops and Target. - This will be release when we have ability to code the complete Strategy. Although it can be done with the current pinescript options, it would be far more easier if we have strategy ability.
Link for Educational Purpose
stockcharts.com
-
Good Luck Trading
UCSgears
TSM Donchian Moving Average System_by_ucsgearsThis is just the raw code for the system / strategies.
Will Complete this when I get more time.
Green - Setup Long
Red - Setup Short
