BACKTEST SCRIPT 0.999 ALPHATRADINGVIEW BACKTEST SCRIPT by Lionshare (c) 2015
THS IS A REAL ALTERNATIVE FOR LONG AWAITED TV NATIVE BACKTEST ENGINE.
READY FOR USE JUST RIGHT NOW.
For user provided trading strategy, executes the trades on pricedata history and continues to make it over live datafeed.
Calculates and (plots on premise) the next performance statistics:
profit - i.e. gross profit/loss.
profit_max - maximum value of gross profit/loss.
profit_per_trade - each trade's profit/loss.
profit_per_stop_trade - profit/loss per "stop order" trade.
profit_stop - gross profit/loss caused by stop orders.
profit_stop_p - percentage of "stop orders" profit/loss in gross profit/loss.
security_if_bought_back - size of security portfolio if bought back.
trades_count_conseq_profit - consecutive gain from profitable series.
trades_count_conseq_profit_max - maxmimum gain from consecutive profitable series achieved.
trades_count_conseq_loss - same as for profit, but for loss.
trades_count_conseq_loss_max - same as for profit, but for loss.
trades_count_conseq_won - number of trades, that were won consecutively.
trades_count_conseq_won_max - maximum number of trades, won consecutively.
trades_count_conseq_lost - same as for won trades, but for lost.
trades_count_conseq_lost_max - same as for won trades, but for lost.
drawdown - difference between local equity highs and lows.
profit_factor - profit-t-loss ratio.
profit_factor_r - profit(without biggest winning trade)-to-loss ratio.
recovery_factor - equity-to-drawdown ratio.
expected_value - median gain value of all wins and loss.
zscore - shows how much your seriality of consecutive wins/loss diverges from the one of normal distributed process. valued in sigmas. zscore of +3 or -3 sigmas means nonrandom realitonship of wins series-to-loss series.
confidence_limit - the limit of confidence in zscore result. values under 0.95 are considered inconclusive.
sharpe - sharpe ratio - shows the level of strategy stability. basically it is how the profit/loss is deviated around the expected value.
sortino - the same as sharpe, but is calculated over the negative gains.
k - Kelly criterion value, means the percentage of your portfolio, you can trade the scripted strategy for optimal risk management.
k_margin - Kelly criterion recalculated to be meant as optimal margin value.
DISCLAIMER :
The SCRIPT is in ALPHA stage. So there could be some hidden bugs.
Though the basic functionality seems to work fine.
Initial documentation is not detailed. There could be english grammar mistakes also.
NOW Working hard on optimizing the script. Seems, some heavier strategies (especially those using the multiple SECURITY functions) call TV processing power limitation errors.
Docs are here:
docs.google.com
Pesquisar nos scripts por "backtest"
Backtest Larry Williams 9.1 ( exemplo) - JBBacktest simples para compra no rompimento do topo do primeiro candle que abre abaixo e fecha acima da ema de 9 periodos e venda no rompimento do fundo do primeiro candle que fecha abaixo da ema de 9
BackTestLibLibrary "BackTestLib"
Allows backtesting indicator performance. Tracks typical metrics such as won/loss, profit factor, draw down, etc. Trading View strategy library provides similar (and more comprehensive)
functionality but only works with strategies. This libary was created to address performance tracking within indicators.
Two primary outputs are generated:
1. Summary Table: Displays overall performance metrics for the indicator over the chart's loaded timeframe and history
2. Details Table: Displays a table of individual trade entries and exits. This table can grow larger than the available chart space. It does have a max number of rows supported. I haven't
found a way to add scroll bars or scroll bar equivalents yet.
f_init(data, _defaultStopLoss, _defaultTakeProfit, _useTrailingStop, _useTraingStopToBreakEven, _trailingStopActivation, _trailingStopOffset)
f_init Initialize the backtest data type. Called prior to using the backtester functions
Parameters:
data (backtesterData) : backtesterData to initialize
_defaultStopLoss (float) : Default trade stop loss to apply
_defaultTakeProfit (float) : Default trade take profit to apply
_useTrailingStop (bool) : Trailing stop enabled
_useTraingStopToBreakEven (bool) : When trailing stop active, trailing stop will increase no further than the entry price
_trailingStopActivation (int) : When trailing stop active, trailing will begin once price exceeds base stop loss by this number of points
_trailingStopOffset (int) : When trailing stop active, it will trail the max price achieved by this number of points
Returns: Initialized data set
f_buildResultStr(_resultType, _price, _resultPoints, _numWins, _pointsWon, _numLoss, _pointsLost)
f_buildResultStr Helper function to construct a string of resutling data for exit tooltip labels
Parameters:
_resultType (string)
_price (float)
_resultPoints (float)
_numWins (int)
_pointsWon (float)
_numLoss (int)
_pointsLost (float)
f_buildResultLabel(data, labelVertical, labelOffset, long)
f_buildResultLabel Helper function to construct an Exit label for display on the chart
Parameters:
data (backtesterData)
labelVertical (bool)
labelOffset (int)
long (bool)
f_updateTrailingStop(_entryPrice, _curPrice, _sl, _tp, trailingStopActivationInput, trailingStopOffsetInput, useTrailingStopToBreakEven)
f_updateTrailingStop Helper function to advance the trailing stop as price action dictates
Parameters:
_entryPrice (float)
_curPrice (float)
_sl (float)
_tp (float)
trailingStopActivationInput (float)
trailingStopOffsetInput (float)
useTrailingStopToBreakEven (bool)
Returns: Updated stop loss for current price action
f_enterShort(data, entryPrice, fixedStopLoss)
f_enterShort Helper function to enter a short and collect data necessary for tracking the trade entry
Parameters:
data (backtesterData)
entryPrice (float)
fixedStopLoss (float)
Returns: Updated backtest data
f_enterLong(data, entryPrice, fixedStopLoss)
f_enterLong Helper function to enter a long and collect data necessary for tracking the trade entry
Parameters:
data (backtesterData)
entryPrice (float)
fixedStopLoss (float)
Returns: Updated backtest data
f_exitTrade(data)
f_enterLong Helper function to exit a trade and update/reset tracking data
Parameters:
data (backtesterData)
Returns: Updated backtest data
f_checkTradeConditionForExit(data, condition, curPrice, enableRealTime)
f_checkTradeConditionForExit Helper function to determine if provided condition indicates an exit
Parameters:
data (backtesterData)
condition (bool) : When true trade will exit
curPrice (float)
enableRealTime (bool) : When true trade will evaluate if barstate is relatime or barstate is confirmed; otherwise just checks on is confirmed
Returns: Updated backtest data
f_checkTrade(data, curPrice, curLow, curHigh, enableRealTime)
f_checkTrade Helper function to determine if current price action dictates stop loss or take profit exit
Parameters:
data (backtesterData)
curPrice (float)
curLow (float)
curHigh (float)
enableRealTime (bool) : When true trade will evaluate if barstate is relatime or barstate is confirmed; otherwise just checks on is confirmed
Returns: Updated backtest data
f_fillCell(_table, _column, _row, _title, _value, _bgcolor, _txtcolor, _text_size)
f_fillCell Helper function to construct result table cells
Parameters:
_table (table)
_column (int)
_row (int)
_title (string)
_value (string)
_bgcolor (color)
_txtcolor (color)
_text_size (string)
Returns: Table cell
f_prepareStatsTable(data, drawTesterSummary, drawTesterDetails, summaryTableTextSize, detailsTableTextSize, displayRowZero, summaryTableLocation, detailsTableLocation)
f_fillCell Helper function to populate result table
Parameters:
data (backtesterData)
drawTesterSummary (bool)
drawTesterDetails (bool)
summaryTableTextSize (string)
detailsTableTextSize (string)
displayRowZero (bool)
summaryTableLocation (string)
detailsTableLocation (string)
Returns: Updated backtest data
backtesterData
backtesterData - container for backtest performance metrics
Fields:
tradesArray (array) : Array of strings with entries for each individual trade and its results
pointsBalance (series float) : Running sum of backtest points won/loss results
drawDown (series float) : Running sum of backtest total draw down points
maxDrawDown (series float) : Running sum of backtest total draw down points
maxRunup (series float) : Running sum of max points won over the backtest
numWins (series int) : Number of wins of current backtes set
numLoss (series int) : Number of losses of current backtes set
pointsWon (series float) : Running sum of points won to date
pointsLost (series float) : Running sum of points lost to date
entrySide (series string) : Current entry long/short
tradeActive (series bool) : Indicates if a trade is currently active
tradeComplete (series bool) : Indicates if a trade just exited (due to stop loss or take profit)
entryPrice (series float) : Current trade entry price
entryTime (series int) : Current trade entry time
sl (series float) : Current trade stop loss
tp (series float) : Current trade take profit
defaultStopLoss (series float) : Default trade stop loss to apply
defaultTakeProfit (series float) : Default trade take profit to apply
useTrailingStop (series bool) : Trailing stop enabled
useTrailingStopToBreakEven (series bool) : When trailing stop active, trailing stop will increase no further than the entry price
trailingStopActivation (series int) : When trailing stop active, trailing will begin once price exceeds base stop loss by this number of points
trailingStopOffset (series int) : When trailing stop active, it will trail the max price achieved by this number of points
resultType (series string) : Current trade won/lost
exitPrice (series float) : Current trade exit price
resultPoints (series float) : Current trade points won/lost
summaryTable (series table) : Table to deisplay summary info
tradesTable (series table) : Table to display per trade info
TV signal for DCA backtestThis script allows you to backtest Trading View's "Technical Ratings" (Buy, Strong buy, Sell, Strong Sell) using Gavin's backtest script.
To use it add the backtest script to the chart together with this script and then from the back test settings connect the external indicator. You should select "TV Signal" as the source.
Backtesting works best on the 5m chart, but you can still change this script from its settings to higher timeframes.
Encoding
Buy is 1
Strong Buy is 2
Sell is -1
Strong Sell is -2
In the backtest script you can decide which rating you want to use for open deal and which one for close deal.
For example, if you were backtesting a long bot you could enter a long position when TV signal is Buy (1) and close the deal when TV signal is Sell (-1).
You have the full flexibility to decide which technical rating to use for your backtesting.
Enjoy!
Backtest Strategy Optimizer AdapterBacktest Strategy Optimizer Adapter
With this library, you will be able to run one or multiple backtests with different variables (combinations). For example, you can run 100 backtests of Supertrend at once with an increment factor of 0.1. This way, you can easily fetch the most profitable settings and apply them to your strategy.
To get a better understanding of the code, you can check the code below.
Single backtest results
= backtest.results(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Add backtest results to a table
backtest.table(initial_capital, profit_and_loss, open_balance, winrate, entries, exits, wins, losses, backtest_table_position, backtest_table_margin, backtest_table_transparency, backtest_table_cell_color, backtest_table_title_cell_color, backtest_table_text_color)
Backtest result without chart labels
= backtest.run(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result profit
profit = backtest.profit(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result winrate
winrate = backtest.winrate(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Start Date
You can set the start date either by using a timestamp or a number that refers to the number of bars back.
Stop Loss / Take Profit Issue
Unfortunately, I did not manage to achieve 100% accuracy for the take profit and stop loss. The original TradingView backtest can stop at the correct position within a bar using the strategy.exit stop and limit variables. However, it seems unachievable with a crossunder/crossover function in PineScript unless it is calculated on every tick (which would make the backtesting results invalid). So far, I have not found a workaround, and I would be grateful if someone could solve this issue, if it is even possible. If you have any solutions or fixes, please let me know!
Multiple Backtest Results / Optimizer
You can run multiple backtests in a single strategy or indicator, but there are certain requirements for placing the correct code in the right way. To view examples of running multiple backtests, you can refer to the links provided in the updates I posted below. In the samples I have also explained how you can auto-generate code for your backtest strategy.
Backtesting & Trading Engine [PineCoders]The PineCoders Backtesting and Trading Engine is a sophisticated framework with hybrid code that can run as a study to generate alerts for automated or discretionary trading while simultaneously providing backtest results. It can also easily be converted to a TradingView strategy in order to run TV backtesting. The Engine comes with many built-in strats for entries, filters, stops and exits, but you can also add you own.
If, like any self-respecting strategy modeler should, you spend a reasonable amount of time constantly researching new strategies and tinkering, our hope is that the Engine will become your inseparable go-to tool to test the validity of your creations, as once your tests are conclusive, you will be able to run this code as a study to generate the alerts required to put it in real-world use, whether for discretionary trading or to interface with an execution bot/app. You may also find the backtesting results the Engine produces in study mode enough for your needs and spend most of your time there, only occasionally converting to strategy mode in order to backtest using TV backtesting.
As you will quickly grasp when you bring up this script’s Settings, this is a complex tool. While you will be able to see results very quickly by just putting it on a chart and using its built-in strategies, in order to reap the full benefits of the PineCoders Engine, you will need to invest the time required to understand the subtleties involved in putting all its potential into play.
Disclaimer: use the Engine at your own risk.
Before we delve in more detail, here’s a bird’s eye view of the Engine’s features:
More than 40 built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
By combining your own strats to the built-in strats supplied with the Engine, and then tuning the numerous options and parameters in the Inputs dialog box, you will be able to play what-if scenarios from an infinite number of permutations.
USE CASES
You have written an indicator that provides an entry strat but it’s missing other components like a filter and a stop strategy. You add a plot in your indicator that respects the Engine’s External Signal Protocol, connect it to the Engine by simply selecting your indicator’s plot name in the Engine’s Settings/Inputs and then run tests on different combinations of entry stops, in-trade stops and profit taking strats to find out which one produces the best results with your entry strat.
You are building a complex strategy that you will want to run as an indicator generating alerts to be sent to a third-party execution bot. You insert your code in the Engine’s modules and leverage its trade management code to quickly move your strategy into production.
You have many different filters and want to explore results using them separately or in combination. Integrate the filter code in the Engine and run through different permutations or hook up your filtering through the external input and control your filter combos from your indicator.
You are tweaking the parameters of your entry, filter or stop strat. You integrate it in the Engine and evaluate its performance using the Engine’s statistics.
You always wondered what results a random entry strat would yield on your markets. You use the Engine’s built-in random entry strat and test it using different combinations of filters, stop and exit strats.
You want to evaluate the impact of fees and slippage on your strategy. You use the Engine’s inputs to play with different values and get immediate feedback in the detailed numbers provided in the Data Window.
You just want to inspect the individual trades your strategy generates. You include it in the Engine and then inspect trades visually on your charts, looking at the numbers in the Data Window as you move your cursor around.
You have never written a production-grade strategy and you want to learn how. Inspect the code in the Engine; you will find essential components typical of what is being used in actual trading systems.
You have run your system for a while and have compiled actual slippage information and your broker/exchange has updated his fees schedule. You enter the information in the Engine and run it on your markets to see the impact this has on your results.
FEATURES
Before going into the detail of the Inputs and the Data Window numbers, here’s a more detailed overview of the Engine’s features.
Built-in strats
The engine comes with more than 40 pre-coded strategies for the following standard system components:
Entries,
Filters,
Entry stops,
2 stage in-trade stops with kick-in rules,
Pyramiding rules,
Hard exits.
While some of the filter and stop strats provided may be useful in production-quality systems, you will not devise crazy profit-generating systems using only the entry strats supplied; that part is still up to you, as will be finding the elusive combination of components that makes winning systems. The Engine will, however, provide you with a solid foundation where all the trade management nitty-gritty is handled for you. By binding your custom strats to the Engine, you will be able to build reliable systems of the best quality currently allowed on the TV platform.
On-chart trade information
As you move over the bars in a trade, you will see trade numbers in the Data Window change at each bar. The engine calculates the P&L at every bar, including slippage and fees that would be incurred were the trade exited at that bar’s close. If the trade includes pyramided entries, those will be taken into account as well, although for those, final fees and slippage are only calculated at the trade’s exit.
You can also see on-chart markers for the entry level, stop positions, in-trade special events and entries/exits (you will want to disable these when using the Engine in strategy mode to see TV backtesting results).
Customization
You can couple your own strats to the Engine in two ways:
1. By inserting your own code in the Engine’s different modules. The modular design should enable you to do so with minimal effort by following the instructions in the code.
2. By linking an external indicator to the engine. After making the proper selections in the engine’s Settings and providing values respecting the engine’s protocol, your external indicator can, when the Engine is used in Indicator mode only:
Tell the engine when to enter long or short trades, but let the engine’s in-trade stop and exit strats manage the exits,
Signal both entries and exits,
Provide an entry stop along with your entry signal,
Filter other entry signals generated by any of the engine’s entry strats.
Conversion from strategy to study
TradingView strategies are required to backtest using the TradingView backtesting feature, but if you want to generate alerts with your script, whether for automated trading or just to trigger alerts that you will use in discretionary trading, your code has to run as a study since, for the time being, strategies can’t generate alerts. From hereon we will use indicator as a synonym for study.
Unless you want to maintain two code bases, you will need hybrid code that easily flips between strategy and indicator modes, and your code will need to restrict its use of strategy() calls and their arguments if it’s going to be able to run both as an indicator and a strategy using the same trade logic. That’s one of the benefits of using this Engine. Once you will have entered your own strats in the Engine, it will be a matter of commenting/uncommenting only four lines of code to flip between indicator and strategy modes in a matter of seconds.
Additionally, even when running in Indicator mode, the Engine will still provide you with precious numbers on your individual trades and global results, some of which are not available with normal TradingView backtesting.
Post-Exit Analysis for alternate outcomes (PEA)
While typical backtesting shows results of trade outcomes, PEA focuses on what could have happened after the exit. The intention is to help traders get an idea of the opportunity/risk in the bars following the trade in order to evaluate if their exit strategies are too aggressive or conservative.
After a trade is exited, the Engine’s PEA module continues analyzing outcomes for a user-defined quantity of bars. It identifies the maximum opportunity and risk available in that space, and calculates the drawdown required to reach the highest opportunity level post-exit, while recording the number of bars to that point.
Typically, if you can’t find opportunity greater than 1X past your trade using a few different reasonable lengths of PEA, your strategy is doing pretty good at capturing opportunity. Remember that 100% of opportunity is never capturable. If, however, PEA was finding post-trade maximum opportunity of 3 or 4X with average drawdowns of 0.3 to those areas, this could be a clue revealing your system is exiting trades prematurely. To analyze PEA numbers, you can uncomment complete sets of plots in the Plot module to reveal detailed global and individual PEA numbers.
Statistics
The Engine provides stats on your trades that TV backtesting does not provide, such as:
Average Profitability Per Trade (APPT), aka statistical expectancy, a crucial value.
APPT per bar,
Average stop size,
Traded volume .
It also shows you on a trade-by-trade basis, on-going individual trade results and data.
In-trade events
In-trade events can plot reminders and trigger alerts when they occur. The built-in events are:
Price approaching stop,
Possible tops/bottoms,
Large stop movement (for discretionary trading where stop is moved manually),
Large price movements.
Slippage and Fees
Even when running in indicator mode, the Engine allows for slippage and fees to be included in the logic and test results.
Alerts
The alert creation mechanism allows you to configure alerts on any combination of the normal or pyramided entries, exits and in-trade events.
Backtesting results
A few words on the numbers calculated in the Engine. Priority is given to numbers not shown in TV backtesting, as you can readily convert the script to a strategy if you need them.
We have chosen to focus on numbers expressing results relative to X (the trade’s risk) rather than in absolute currency numbers or in other more conventional but less useful ways. For example, most of the individual trade results are not shown in percentages, as this unit of measure is often less meaningful than those expressed in units of risk (X). A trade that closes with a +25% result, for example, is a poor outcome if it was entered with a -50% stop. Expressed in X, this trade’s P&L becomes 0.5, which provides much better insight into the trade’s outcome. A trade that closes with a P&L of +2X has earned twice the risk incurred upon entry, which would represent a pre-trade risk:reward ratio of 2.
The way to go about it when you think in X’s and that you adopt the sound risk management policy to risk a fixed percentage of your account on each trade is to equate a currency value to a unit of X. E.g. your account is 10K USD and you decide you will risk a maximum of 1% of it on each trade. That means your unit of X for each trade is worth 100 USD. If your APPT is 2X, this means every time you risk 100 USD in a trade, you can expect to make, on average, 200 USD.
By presenting results this way, we hope that the Engine’s statistics will appeal to those cognisant of sound risk management strategies, while gently leading traders who aren’t, towards them.
We trade to turn in tangible profits of course, so at some point currency must come into play. Accordingly, some values such as equity, P&L, slippage and fees are expressed in currency.
Many of the usual numbers shown in TV backtests are nonetheless available, but they have been commented out in the Engine’s Plot module.
Position sizing and risk management
All good system designers understand that optimal risk management is at the very heart of all winning strategies. The risk in a trade is defined by the fraction of current equity represented by the amplitude of the stop, so in order to manage risk optimally on each trade, position size should adjust to the stop’s amplitude. Systems that enter trades with a fixed stop amplitude can get away with calculating position size as a fixed percentage of current equity. In the context of a test run where equity varies, what represents a fixed amount of risk translates into different currency values.
Dynamically adjusting position size throughout a system’s life is optimal in many ways. First, as position sizing will vary with current equity, it reproduces a behavioral pattern common to experienced traders, who will dial down risk when confronted to poor performance and increase it when performance improves. Second, limiting risk confers more predictability to statistical test results. Third, position sizing isn’t just about managing risk, it’s also about maximizing opportunity. By using the maximum leverage (no reference to trading on margin here) into the trade that your risk management strategy allows, a dynamic position size allows you to capture maximal opportunity.
To calculate position sizes using the fixed risk method, we use the following formula: Position = Account * MaxRisk% / Stop% [, which calculates a position size taking into account the trade’s entry stop so that if the trade is stopped out, 100 USD will be lost. For someone who manages risk this way, common instructions to invest a certain percentage of your account in a position are simply worthless, as they do not take into account the risk incurred in the trade.
The Engine lets you select either the fixed risk or fixed percentage of equity position sizing methods. The closest thing to dynamic position sizing that can currently be done with alerts is to use a bot that allows syntax to specify position size as a percentage of equity which, while being dynamic in the sense that it will adapt to current equity when the trade is entered, does not allow us to modulate position size using the stop’s amplitude. Changes to alerts are on the way which should solve this problem.
In order for you to simulate performance with the constraint of fixed position sizing, the Engine also offers a third, less preferable option, where position size is defined as a fixed percentage of initial capital so that it is constant throughout the test and will thus represent a varying proportion of current equity.
Let’s recap. The three position sizing methods the Engine offers are:
1. By specifying the maximum percentage of risk to incur on your remaining equity, so the Engine will dynamically adjust position size for each trade so that, combining the stop’s amplitude with position size will yield a fixed percentage of risk incurred on current equity,
2. By specifying a fixed percentage of remaining equity. Note that unless your system has a fixed stop at entry, this method will not provide maximal risk control, as risk will vary with the amplitude of the stop for every trade. This method, as the first, does however have the advantage of automatically adjusting position size to equity. It is the Engine’s default method because it has an equivalent in TV backtesting, so when flipping between indicator and strategy mode, test results will more or less correspond.
3. By specifying a fixed percentage of the Initial Capital. While this is the least preferable method, it nonetheless reflects the reality confronted by most system designers on TradingView today. In this case, risk varies both because the fixed position size in initial capital currency represents a varying percentage of remaining equity, and because the trade’s stop amplitude may vary, adding another variability vector to risk.
Note that the Engine cannot display equity results for strategies entering trades for a fixed amount of shares/contracts at a variable price.
SETTINGS/INPUTS
Because the initial text first published with a script cannot be edited later and because there are just too many options, the Engine’s Inputs will not be covered in minute detail, as they will most certainly evolve. We will go over them with broad strokes; you should be able to figure the rest out. If you have questions, just ask them here or in the PineCoders Telegram group.
Display
The display header’s checkbox does nothing.
For the moment, only one exit strategy uses a take profit level, so only that one will show information when checking “Show Take Profit Level”.
Entries
You can activate two simultaneous entry strats, each selected from the same set of strats contained in the Engine. If you select two and they fire simultaneously, the main strat’s signal will be used.
The random strat in each list uses a different seed, so you will get different results from each.
The “Filter transitions” and “Filter states” strats delegate signal generation to the selected filter(s). “Filter transitions” signals will only fire when the filter transitions into bull/bear state, so after a trade is stopped out, the next entry may take some time to trigger if the filter’s state does not change quickly. When you choose “Filter states”, then a new trade will be entered immediately after an exit in the direction the filter allows.
If you select “External Indicator”, your indicator will need to generate a +2/-2 (or a positive/negative stop value) to enter a long/short position, providing the selected filters allow for it. If you wish to use the Engine’s capacity to also derive the entry stop level from your indicator’s signal, then you must explicitly choose this option in the Entry Stops section.
Filters
You can activate as many filters as you wish; they are additive. The “Maximum stop allowed on entry” is an important component of proper risk management. If your system has an average 3% stop size and you need to trade using fixed position sizes because of alert/execution bot limitations, you must use this filter because if your system was to enter a trade with a 15% stop, that trade would incur 5 times the normal risk, and its result would account for an abnormally high proportion in your system’s performance.
Remember that any filter can also be used as an entry signal, either when it changes states, or whenever no trade is active and the filter is in a bull or bear mode.
Entry Stops
An entry stop must be selected in the Engine, as it requires a stop level before the in-trade stop is calculated. Until the selected in-trade stop strat generates a stop that comes closer to price than the entry stop (or respects another one of the in-trade stops kick in strats), the entry stop level is used.
It is here that you must select “External Indicator” if your indicator supplies a +price/-price value to be used as the entry stop. A +price is expected for a long entry and a -price value will enter a short with a stop at price. Note that the price is the absolute price, not an offset to the current price level.
In-Trade Stops
The Engine comes with many built-in in-trade stop strats. Note that some of them share the “Length” and “Multiple” field, so when you swap between them, be sure that the length and multiple in use correspond to what you want for that stop strat. Suggested defaults appear with the name of each strat in the dropdown.
In addition to the strat you wish to use, you must also determine when it kicks in to replace the initial entry’s stop, which is determined using different strats. For strats where you can define a positive or negative multiple of X, percentage or fixed value for a kick-in strat, a positive value is above the trade’s entry fill and a negative one below. A value of zero represents breakeven.
Pyramiding
What you specify in this section are the rules that allow pyramiding to happen. By themselves, these rules will not generate pyramiding entries. For those to happen, entry signals must be issued by one of the active entry strats, and conform to the pyramiding rules which act as a filter for them. The “Filter must allow entry” selection must be chosen if you want the usual system’s filters to act as additional filtering criteria for your pyramided entries.
Hard Exits
You can choose from a variety of hard exit strats. Hard exits are exit strategies which signal trade exits on specific events, as opposed to price breaching a stop level in In-Trade Stops strategies. They are self-explanatory. The last one labelled When Take Profit Level (multiple of X) is reached is the only one that uses a level, but contrary to stops, it is above price and while it is relative because it is expressed as a multiple of X, it does not move during the trade. This is the level called Take Profit that is show when the “Show Take Profit Level” checkbox is checked in the Display section.
While stops focus on managing risk, hard exit strategies try to put the emphasis on capturing opportunity.
Slippage
You can define it as a percentage or a fixed value, with different settings for entries and exits. The entry and exit markers on the chart show the impact of slippage on the entry price (the fill).
Fees
Fees, whether expressed as a percentage of position size in and out of the trade or as a fixed value per in and out, are in the same units of currency as the capital defined in the Position Sizing section. Fees being deducted from your Capital, they do not have an impact on the chart marker positions.
In-Trade Events
These events will only trigger during trades. They can be helpful to act as reminders for traders using the Engine as assistance to discretionary trading.
Post-Exit Analysis
It is normally on. Some of its results will show in the Global Numbers section of the Data Window. Only a few of the statistics generated are shown; many more are available, but commented out in the Plot module.
Date Range Filtering
Note that you don’t have to change the dates to enable/diable filtering. When you are done with a specific date range, just uncheck “Date Range Filtering” to disable date filtering.
Alert Triggers
Each selection corresponds to one condition. Conditions can be combined into a single alert as you please. Just be sure you have selected the ones you want to trigger the alert before you create the alert. For example, if you trade in both directions and you want a single alert to trigger on both types of exits, you must select both “Long Exit” and “Short Exit” before creating your alert.
Once the alert is triggered, these settings no longer have relevance as they have been saved with the alert.
When viewing charts where an alert has just triggered, if your alert triggers on more than one condition, you will need the appropriate markers active on your chart to figure out which condition triggered the alert, since plotting of markers is independent of alert management.
Position sizing
You have 3 options to determine position size:
1. Proportional to Stop -> Variable, with a cap on size.
2. Percentage of equity -> Variable.
3. Percentage of Initial Capital -> Fixed.
External Indicator
This is where you connect your indicator’s plot that will generate the signals the Engine will act upon. Remember this only works in Indicator mode.
DATA WINDOW INFORMATION
The top part of the window contains global numbers while the individual trade information appears in the bottom part. The different types of units used to express values are:
curr: denotes the currency used in the Position Sizing section of Inputs for the Initial Capital value.
quote: denotes quote currency, i.e. the value the instrument is expressed in, or the right side of the market pair (USD in EURUSD ).
X: the stop’s amplitude, itself expressed in quote currency, which we use to express a trade’s P&L, so that a trade with P&L=2X has made twice the stop’s amplitude in profit. This is sometimes referred to as R, since it represents one unit of risk. It is also the unit of measure used in the APPT, which denotes expected reward per unit of risk.
X%: is also the stop’s amplitude, but expressed as a percentage of the Entry Fill.
The numbers appearing in the Data Window are all prefixed:
“ALL:” the number is the average for all first entries and pyramided entries.
”1ST:” the number is for first entries only.
”PYR:” the number is for pyramided entries only.
”PEA:” the number is for Post-Exit Analyses
Global Numbers
Numbers in this section represent the results of all trades up to the cursor on the chart.
Average Profitability Per Trade (X): This value is the most important gauge of your strat’s worthiness. It represents the returns that can be expected from your strat for each unit of risk incurred. E.g.: your APPT is 2.0, thus for every unit of currency you invest in a trade, you can on average expect to obtain 2 after the trade. APPT is also referred to as “statistical expectancy”. If it is negative, your strategy is losing, even if your win rate is very good (it means your winning trades aren’t winning enough, or your losing trades lose too much, or both). Its counterpart in currency is also shown, as is the APPT/bar, which can be a useful gauge in deciding between rivalling systems.
Profit Factor: Gross of winning trades/Gross of losing trades. Strategy is profitable when >1. Not as useful as the APPT because it doesn’t take into account the win rate and the average win/loss per trade. It is calculated from the total winning/losing results of this particular backtest and has less predictive value than the APPT. A good profit factor together with a poor APPT means you just found a chart where your system outperformed. Relying too much on the profit factor is a bit like a poker player who would think going all in with two’s against aces is optimal because he just won a hand that way.
Win Rate: Percentage of winning trades out of all trades. Taken alone, it doesn’t have much to do with strategy profitability. You can have a win rate of 99% but if that one trade in 100 ruins you because of poor risk management, 99% doesn’t look so good anymore. This number speaks more of the system’s profile than its worthiness. Still, it can be useful to gauge if the system fits your personality. It can also be useful to traders intending to sell their systems, as low win rate systems are more difficult to sell and require more handholding of worried customers.
Equity (curr): This the sum of initial capital and the P&L of your system’s trades, including fees and slippage.
Return on Capital is the equivalent of TV’s Net Profit figure, i.e. the variation on your initial capital.
Maximum drawdown is the maximal drawdown from the highest equity point until the drop . There is also a close to close (meaning it doesn’t take into account in-trade variations) maximum drawdown value commented out in the code.
The next values are self-explanatory, until:
PYR: Avg Profitability Per Entry (X): this is the APPT for all pyramided entries.
PEA: Avg Max Opp . Available (X): the average maximal opportunity found in the Post-Exit Analyses.
PEA: Avg Drawdown to Max Opp . (X): this represents the maximum drawdown (incurred from the close at the beginning of the PEA analysis) required to reach the maximal opportunity point.
Trade Information
Numbers in this section concern only the current trade under the cursor. Most of them are self-explanatory. Use the description’s prefix to determine what the values applies to.
PYR: Avg Profitability Per Entry (X): While this value includes the impact of all current pyramided entries (and only those) and updates when you move your cursor around, P&L only reflects fees at the trade’s last bar.
PEA: Max Opp . Available (X): It’s the most profitable close reached post-trade, measured from the trade’s Exit Fill, expressed in the X value of the trade the PEA follows.
PEA: Drawdown to Max Opp . (X): This is the maximum drawdown from the trade’s Exit Fill that needs to be sustained in order to reach the maximum opportunity point, also expressed in X. Note that PEA numbers do not include slippage and fees.
EXTERNAL SIGNAL PROTOCOL
Only one external indicator can be connected to a script; in order to leverage its use to the fullest, the engine provides options to use it as either an entry signal, an entry/exit signal or a filter. When used as an entry signal, you can also use the signal to provide the entry’s stop. Here’s how this works:
For filter state: supply +1 for bull (long entries allowed), -1 for bear (short entries allowed).
For entry signals: supply +2 for long, -2 for short.
For exit signals: supply +3 for exit from long, -3 for exit from short.
To send an entry stop level with an entry signal: Send positive stop level for long entry (e.g. 103.33 to enter a long with a stop at 103.33), negative stop level for short entry (e.g. -103.33 to enter a short with a stop at 103.33). If you use this feature, your indicator will have to check for exact stop levels of 1.0, 2.0 or 3.0 and their negative counterparts, and fudge them with a tick in order to avoid confusion with other signals in the protocol.
Remember that mere generation of the values by your indicator will have no effect until you explicitly allow their use in the appropriate sections of the Engine’s Settings/Inputs.
An example of a script issuing a signal for the Engine is published by PineCoders.
RECOMMENDATIONS TO ASPIRING SYSTEM DESIGNERS
Stick to higher timeframes. On progressively lower timeframes, margins decrease and fees and slippage take a proportionally larger portion of profits, to the point where they can very easily turn a profitable strategy into a losing one. Additionally, your margin for error shrinks as the equilibrium of your system’s profitability becomes more fragile with the tight numbers involved in the shorter time frames. Avoid <1H time frames.
Know and calculate fees and slippage. To avoid market shock, backtest using conservative fees and slippage parameters. Systems rarely show unexpectedly good returns when they are confronted to the markets, so put all chances on your side by being outrageously conservative—or a the very least, realistic. Test results that do not include fees and slippage are worthless. Slippage is there for a reason, and that’s because our interventions in the market change the market. It is easier to find alpha in illiquid markets such as cryptos because not many large players participate in them. If your backtesting results are based on moving large positions and you don’t also add the inevitable slippage that will occur when you enter/exit thin markets, your backtesting will produce unrealistic results. Even if you do include large slippage in your settings, the Engine can only do so much as it will not let slippage push fills past the high or low of the entry bar, but the gap may be much larger in illiquid markets.
Never test and optimize your system on the same dataset , as that is the perfect recipe for overfitting or data dredging, which is trying to find one precise set of rules/parameters that works only on one dataset. These setups are the most fragile and often get destroyed when they meet the real world.
Try to find datasets yielding more than 100 trades. Less than that and results are not as reliable.
Consider all backtesting results with suspicion. If you never entertained sceptic tendencies, now is the time to begin. If your backtest results look really good, assume they are flawed, either because of your methodology, the data you’re using or the software doing the testing. Always assume the worse and learn proper backtesting techniques such as monte carlo simulations and walk forward analysis to avoid the traps and biases that unchecked greed will set for you. If you are not familiar with concepts such as survivor bias, lookahead bias and confirmation bias, learn about them.
Stick to simple bars or candles when designing systems. Other types of bars often do not yield reliable results, whether by design (Heikin Ashi) or because of the way they are implemented on TV (Renko bars).
Know that you don’t know and use that knowledge to learn more about systems and how to properly test them, about your biases, and about yourself.
Manage risk first , then capture opportunity.
Respect the inherent uncertainty of the future. Cleanse yourself of the sad arrogance and unchecked greed common to newcomers to trading. Strive for rationality. Respect the fact that while backtest results may look promising, there is no guarantee they will repeat in the future (there is actually a high probability they won’t!), because the future is fundamentally unknowable. If you develop a system that looks promising, don’t oversell it to others whose greed may lead them to entertain unreasonable expectations.
Have a plan. Understand what king of trading system you are trying to build. Have a clear picture or where entries, exits and other important levels will be in the sort of trade you are trying to create with your system. This stated direction will help you discard more efficiently many of the inevitably useless ideas that will pop up during system design.
Be wary of complexity. Experienced systems engineers understand how rapidly complexity builds when you assemble components together—however simple each one may be. The more complex your system, the more difficult it will be to manage.
Play! . Allow yourself time to play around when you design your systems. While much comes about from working with a purpose, great ideas sometimes come out of just trying things with no set goal, when you are stuck and don’t know how to move ahead. Have fun!
@LucF
NOTES
While the engine’s code can supply multiple consecutive entries of longs or shorts in order to scale positions (pyramid), all exits currently assume the execution bot will exit the totality of the position. No partial exits are currently possible with the Engine.
Because the Engine is literally crippled by the limitations on the number of plots a script can output on TV; it can only show a fraction of all the information it calculates in the Data Window. You will find in the Plot Module vast amounts of commented out lines that you can activate if you also disable an equivalent number of other plots. This may be useful to explore certain characteristics of your system in more detail.
When backtesting using the TV backtesting feature, you will need to provide the strategy parameters you wish to use through either Settings/Properties or by changing the default values in the code’s header. These values are defined in variables and used not only in the strategy() statement, but also as defaults in the Engine’s relevant Inputs.
If you want to test using pyramiding, then both the strategy’s Setting/Properties and the Engine’s Settings/Inputs need to allow pyramiding.
If you find any bugs in the Engine, please let us know.
THANKS
To @glaz for allowing the use of his unpublished MA Squize in the filters.
To @everget for his Chandelier stop code, which is also used as a filter in the Engine.
To @RicardoSantos for his pseudo-random generator, and because it’s from him that I first read in the Pine chat about the idea of using an external indicator as input into another. In the PineCoders group, @theheirophant then mentioned the idea of using it as a buy/sell signal and @simpelyfe showed a piece of code implementing the idea. That’s the tortuous story behind the use of the external indicator in the Engine.
To @admin for the Volatility stop’s original code and for the donchian function lifted from Ichimoku .
To @BobHoward21 for the v3 version of Volatility Stop .
To @scarf and @midtownsk8rguy for the color tuning.
To many other scripters who provided encouragement and suggestions for improvement during the long process of writing and testing this piece of code.
To J. Welles Wilder Jr. for ATR, used extensively throughout the Engine.
To TradingView for graciously making an account available to PineCoders.
And finally, to all fellow PineCoders for the constant intellectual stimulation; it is a privilege to share ideas with you all. The Engine is for all TradingView PineCoders, of course—but especially for you.
Look first. Then leap.
Backtesting- IndicatorFor anyone interested, Here is an example of how to put backtesting results into an Indicator. This calculates the same values as you find in the Summary Screen of the built in Strategy backtester. This will use the same result size as the standard backtester i.e. 5 minute chart grabs roughly 1 month of data, 1 minute chart grabs 1 week of data, etc... I tried to keep this as self-contained as possible so I put most of the code for the results in the bottom of the Indicator. The results stop at the last completed trade signal i.e. a Buy has a Sell to it. This is the same indicator I posted earlier with the PCT Trailing StopLoss so you will see that code in here as well. As said in my previous posting, the indicator is just a simple EMA crossover to give it something to do and I would not recommend using this indicator on its own, but instead copy the code to your own indicator if you find it useful. I also left the code in so that you can switch back to a Strategy if you want to verify the results.
Additional Notes:
- The results are within an acceptable margin of error due to the fact that the Indicator is having to calculate based on when the Buy and Sell Signal occur as opposed to when actual trades occur like in the Strategy Backtester
- I was trying to find a way to set the number of Buy Signals to use i.e. show me the results from the past 100 trades but couldn't sort out the logic. I am open to suggestions. Also keep in mind I am not a coder by profession so if you have any ideas on that front, please explain it to me as though I am a 5 year old child and provide code examples if possible :)
- I included the Strategy results in the Screen Shots so that you can see where the results line up.
Additional Additional Note:
This is not financial advice. Use at your own risk.
Backtest AdapterThis is a proof-of-concept Backtest Adapter that can be used with my recent publication "Machine Learning: Lorentzian Classification" located here:
This adapter is helpful because it enables interactive backtesting with TradingView's built-in "Strategy Tester" framework without the need to translate the logic from an "indicator" script to a "strategy" script.
To use this, one must have the "Machine Learning: Lorentzian Classification" script and this Backtest Adapter open simultaneously on the same chart. From there, simply change the "Source" setting of the Backtest Adapter to "Lorentzian Classification: Backtest Stream" to transfer the entry/exit signals stream to the Backtest Adapter.
For an example of how to implement your own backtest stream in your indicators, please refer to the "Backtesting" section in the source code of the "Machine Learning: Lorentzian Classification" script, which is shown below for convenience:
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.
MACD, backtest 2015+ only, cut in half and doubledThis is only a slight modification to the existing "MACD Strategy" strategy plugin!
found the default MACD strategy to be lacking, although impressive for its simplicity. I added "year>2014" to the IF buy/sell conditions so it will only backtest from 2015 and beyond ** .
I also had a problem with the standard MACD trading late, per se. To that end I modified the inputs for fast/slow/signal to double. Example: my defaults are 10, 21, 10 so I put 20, 42, 20 in. This has the effect of making a 30min interval the same as 1 hour at 10,21,10. So if you want to backtest at 4hr, you would set your time interval to 2hr on the main chart. This is a handy way to make shorter time periods more useful even regardless of strategy/testing, since you can view 15min with alot less noise but a better response.
Used on BTCCNY OKcoin, with the chart set at 45 min (so really 90min in the strategy) this gave me a percent profitable of 42% and a profit factor of 1.998 on 189 trades.
Personally, I like to set the length/signals to 30,63,30. Meaning you need to triple the time, it allows for much better use of shorter time periods and the backtests are remarkably profitable. (i.e. 15min chart view = 45min on script, 30min= 1.5hr on script)
** If you want more specific time periods you need to try plugging in different bar values: replace "year" with "n" and "2014" with "5500". The bars are based on unix time I believe so you will need to play around with the number for n, with n being the numbers of bars.
Dual Timeframe SMA Ribbon Crossover Backtest// Backtesting Dual SMA Ribbon Crossover Strategy
// see f.bpcdn.co
// including time limiting
Turned this study into a backtest.
Backtesting ModuleDo you often find yourself creating new 'strategy()' scripts for each trading system? Are you unable to focus on generating new systems due to fatigue and time loss incurred in the process? Here's a potential solution: the 'Backtesting Module' :)
INTRODUCTION
Every trading system is based on four basic conditions: long entry, long exit, short entry and short exit (which are typically defined as boolean series in Pine Script).
If you can define the conditions generated by your trading system as a series of integers, it becomes possible to use these variables in different scripts in efficient ways. (Pine Script is a convenient language that allows you to use the integer output of one indicator as a source in another.)
The 'Backtesting Module' is a dynamic strategy script designed to adapt to your signals. It boasts two notable features:
⮞ It produces a backtest report using the entry and exit variables you define.
⮞ It not only serves for system testing but also to combine independent signals into a single system. (This functionality enables to create complex strategies and report on their success!)
The module tests Golden and Death cross signals by default, when you enter your own conditions the default signals will be neutralized. The methodology is described below.
PREPARATION
There are three simple steps to connect your own indicator to the Module.
STEP 1
Firstly, you must define entry and exit variables in your own script. Let's elucidate it with a straightforward example. Consider a system generating long and short signals based on the intersections of two moving averages. Consequently, our conditions would be as follows:
// Signals
long = ta.crossover(ta.sma(close, 14), ta.sma(close, 28))
short = ta.crossunder(ta.sma(close, 14), ta.sma(close, 28))
Now, the question is: How can we convert boolean variables into integer variables? The answer is conditional ternary block, defined as follows:
// Entry & Exit
long_entry = long ? 1 : 0
long_exit = short ? 1 : 0
short_entry = short ? 1 : 0
short_exit = long ? 1 : 0
The mechanics of the Entry & Exit variables are simple. The variable takes on a value of 1 when your trading system generates the signal and if your system does not produce any signal, variable returns 0. In this example, you see how exit signals can be generated in a trading system that only contains entry signals. If you have a system with original exit signals, you can also use them directly. (Please mind the NOTES section below).
STEP 2
To utilize the Entry & Exit variables as source in another script, they must be plotted on the chart. Therefore, the final detail to include in the script containing your trading system would be as follows:
// Plot The Output
plot(long_entry, "Long Entry", display=display.data_window, editable=false)
plot(long_exit, "Long Exit", display=display.data_window, editable=false)
plot(short_entry, "Short Entry", display=display.data_window, editable=false)
plot(short_exit, "Short Exit", display=display.data_window, editable=false)
STEP 3
Now, we are ready to test the system! Load the Backtesting Module indicator onto the chart along with your trading system/indicator. Then set the outputs of your system (Long Entry, Long Exit, Short Entry, Short Exit) as source in the module. That's it.
FEATURES & ORIGINALITY
⮞ Primarily, this script has been created to provide you with an easy and practical method when testing your trading system.
⮞ I thought it might be nice to visualize a few useful results. The Backtesting Module provides insights into the outcomes of both long and short trades by computing the number of trades and the success percentage.
⮞ Through the 'Trade' parameter, users can specify the market direction in which the indicator is permitted to initiate positions.
⮞ Users have the flexibility to define the date range for the test.
⮞ There are optional features allowing users to plot entry prices on the chart and customize bar colors.
⮞ The report and the test date range are presented in a table on the chart screen. The entry price can be monitored in the data window.
⮞ Note that results are based on realized returns, and the open trade is not included in the displayed results. (The only exception is the 'Unrealized PNL' result in the table.)
STRATEGY SETTINGS
The default parameters are as follows:
⮞ Initial Balance : 10000 (in units of currency)
⮞ Quantity : 10% of equity
⮞ Commission : 0.04%
⮞ Slippage : 0
⮞ Dataset : All bars in the chart
For a realistic backtest result, you should size trades to only risk sustainable amounts of equity. Do not risk more than 5-10% on a trade. And ALWAYS configure your commission and slippage parameters according to pessimistic scenarios!
NOTES
⮞ This script is intended solely for development purposes. And it'll will be available for all the indicators I publish.
⮞ In this version of the module, all order types are designed as market orders. The exit size is the sum of the entry size.
⮞ As your trading conditions grow more intricate, you might need to define the outputs of your system in alternative ways. The method outlined in this description is tailored for straightforward signal structures.
⮞ Additionally, depending on the structure of your trading system, the backtest module may require further development. This encompasses stop-loss, take-profit, specific exit orders, quantity, margin and risk management calculations. I am considering releasing improvements that consider these options in future versions.
⮞ An example of how complex trading signals can be generated is the OTT Collection. If you're interested in seeing how the signals are constructed, you can use the link below.
THANKS
Special thanks to PineCoders for their valuable moderation efforts.
I hope this will be a useful example for the TradingView community...
DISCLAIMER
This is just an indicator, nothing more. It is provided for informational and educational purposes exclusively. The utilization of this script does not constitute professional or financial advice. The user solely bears the responsibility for risks associated with script usage. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
*Backtesting System ⚉ ＯＶＥＲＶＩＥＷ ⚉
One of the best Systems for Backtesting your Strategies.
Incredibly flexible, simple, fast and feature-rich system — will solve most of your queries without much effort.
Many systems for setting StopLoss, TakeProfit, Risk Management and advanced Filters.
All you need to do is plug in your indicator and start Backtesting .
I intentionally left the option to use my System on Full Power before you load your indicator into it.
The system uses the built-in simple and popular moving average crossover signal for this purpose. (EMA 50 & 200).
Also Highly Recommend that you Fully use ALL of the features of this system so that you understand how they work before you ask questions.
Also tried to leave TIPS for each feature everywhere, read Tips, activate them and see how they work.
But before you use this system, I Recommend you to read the following description in Full.
—————— How to connect your indicator in 2 steps:
Adapt your indicator by adding only 2 lines of code and then connect it to this Backtesting System.
Step 1 — Create your connector, For doing so:
• 1 — Find or create in your indicator where are the conditions printing the Long-Buy and Short-Sell signals.
• 2 — Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator wether it's a MACD, RSI , Pivots, or whatever indicator with Clear Buy and Sell conditions.
//@version=5
indicator('Moving Average Cross', overlay = true)
MA200 = ta.𝚎𝚖𝚊(close, 200)
MA50 = ta.𝚎𝚖𝚊(close, 50)
// Generate Buy and Sell conditions
buy = ta.crossover (MA200, MA50)
sell = ta.crossunder (MA200, MA50)
plot(MA200, color=color.green)
plot(MA50 , color=color.red )
bgcolor(color = buy ? color.green : sell ? color.red : na, title='SIGNALS')
// ———————————————— SIGNAL FOR SYSTEM ————————————————
Signal = buy ? +1 : sell ? -1 : 0
plot(Signal, title='🔌Connector🔌', display = display.none)
// —————— 🔥 The Backtesting System expects the value to be exactly +1 for the 𝚋𝚞𝚕𝚕𝚒𝚜𝚑 signal, and -1 for the 𝚋𝚎𝚊𝚛𝚒𝚜𝚑 signal
Basically, I identified my Buy & Sell conditions in the code and added this at the bottom of my indicator code
Now you can connect your indicator to the Backtesting System using the Step 2
Step 2 — Connect the connector
• 1 — Add your updated indicator to a TradingView chart and Add the Backtesting System as well to the SAME chart
• 2 — Open the Backtesting System settings and in the External Source field select your 🔌Connector🔌 (which comes from your indicator)
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⚉ ＭＡＩＮ ＳＥＴＴＩＮＧＳ ⚉
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𝐄𝐱𝐭𝐞𝐫𝐧𝐚𝐥 𝐒𝐨𝐮𝐫𝐜𝐞 — Select your indicator. Add your indicator by following the 2 steps described above and select it in the menu. To familiarize yourself with the system until you select your indicator, you will have an in-built strategy of crossing the two moving EMA's of 50 and 200.
Long Deals — Enable/Disable Long Deals.
Short Deals — Enable/Disable Short Deals.
Wait End Deal — Enable/Disable waiting for a trade to close at Stop Loss/Take Profit. Until the trade closes on the Stop Loss or Take Profit, no new trade will open.
Reverse Deals — To force the opening of a trade in the opposite direction.
ReEntry Deal — Automatically open the same new deal after the deal is closed.
ReOpen Deal — Reopen the trade if the same signal is received. For example, if you are already in the long and a new signal is received in the long, the trade will reopen. * Does not work if Wait End Deal is enabled.
𝐓𝐚𝐤𝐞 𝐏𝐫𝐨𝐟𝐢𝐭:
None — Disables take profit. Useful if you only want to use dynamic stoplosses such as MA, Fast-Trailing, ATR Trail.
FIXED ％ — Fixed take profit in percent.
FIXED ＄ — Fixed Take in Money.
ATR — Fixed Take based on ATR.
R:R — Fixed Take based on the size of your stop loss. For example, if your stop is 10% and R:R=1, then the Take would be 10%. R:R=3 Take would be 30%, etc.
HH / LL — Fixed Take based on the previous maximum/minimum (extremum).
𝐒𝐭𝐨𝐩 𝐋𝐨𝐬𝐬:
None — Disables Stop Loss. Useful if you want to work without a stop loss. *Be careful if Wait End Deal is enabled, the trade may not close for a long time until it reaches the Take.
FIXED ％ — Fixed Stop in percent.
FIXED ＄ — Fixed Stop in Money.
TRAILING — Dynamic Trailing Stop like on the stock exchanges.
FAST TRAIL — Dynamic Fast Trailing Stop moves immediately in profit and stays in place if the price stands still or the price moves in loss.
ATR — Fixed Stop based on the ATR.
ATR TRAIL — Dynamic Trailing Stop based on the ATR.
LO / HI — A Fixed Stop based on the last Maximum/Minimum extemum. Allows you to place a stop just behind or above the low/high candle.
MA — Dynamic Stop based on selected Moving Average. * You will have 8 types of MA (EMA, SMA, HMA, etc.) to choose from, but you can easily add dozens of other MAs, which makes this type of stop incredibly flexible.
Add ％ — If true, then with the "𝗦𝘁𝗼𝗽 ％" parameter you can add percentages to any of the current SL. Can be especially useful when using Stop - 𝗔𝗧𝗥 or 𝗠𝗔 or 𝗟𝗢/𝗛𝗜. For example with 𝗟𝗢/𝗛𝗜 to put a stop for the last High/Low and add 0.5% additional Stoploss.
Fixed R:R — If the stop loss is Dynamic (Trailing or MA) then if R:R true can also be made Dynamic * Use it carefully, the function is experimental.
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⚉ ＴＡＫＥ ＰＲＯＦＩＴ ＬＥＶＥＬＳ ⚉
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A unique method of constructing intermediate Take Profit Levels will allow you to select up to 5 intermediate Take Profit Levels and one intermediate Stop Loss.
Intermediate Take Profit Levels are perfectly calculated into 5 equal parts in the form of levels from the entry point to the final Take Profit target.
All you need to do is to choose the necessary levels for fixing and how much you want to fix at each level as a percentage. For example, TP 3 will always be exactly between the entry point and the Take Profit target. And the value of TP 3 = 50 will close 50% of the amount of the remaining size of the position.
Note: all intermediate SL/TP are closed from the remaining position amount and not from the initial position size, as TV does by default.
SL 0 Position — works in the same way as TP 1-5 but it's Stop. With this parameter you can set the position where the intermediate stop will be set.
Breakeven on TP — When activated, it allows you to put the stop loss at Breakeven after the selected TP is reached. For this function to work as it should - you need to activate an intermediate Take. For example, if TP 3 is activated and Breakeven on TP = 3, then after the price reaches this level, the Stop loss will go to Breakeven.
* This function will not work with Dynamic Stoplosses, because it simply does not make sense.
CoolDown # Bars — When activated, allows you to add a delay before a new trade is opened. A new trade after CoolDown will not be opened until # bars pass and a new signal appears.
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⚉ ＴＩＭＥ ＦＩＬＴＥＲＳ ⚉
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Powerful time filter code that allows you to filter data based on specific time zones, dates, and session days. This code is ideal for those who need to analyze data from different time zones and weed out irrelevant data.
With Time Filter, you can easily set the starting and ending time zones by which you want to filter the data.
You can also set a start and end date for your data and choose which days of the week to include in the analysis. In addition, you can specify start and end times for a specific session, allowing you to focus your analysis on specific time periods.
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⚉ ＳＩＧＮＡＬ ＦＩＬＴＥＲＳ ⚉
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Signal Filters — allows you to easily customize and optimize your trading strategies based on 10 filters.
Each filter is designed to help you weed out inaccurate signals to minimize your risks.
Let's take a look at their features:
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⚉ ＲＩＳＫ ＭＡＮＡＧＥＭＥＮＴ ⚉
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Risk management tools that allow you to set the maximum number of losing trades in a row, a limit on the number of trades per day or week and other filters.
Loss Streak — Set Max number of consecutive loss trades.
Win Streak — Max Winning Streak Length.
Row Loss InDay — Max of consecutive days with a loss in a row.
DrawDown ％ — Max DrawDown (in % of strategy equity).
InDay Loss ％ — Set Max Intraday Loss.
Daily Trades — Limit the number of MAX trades per day.
Weekly Trades — Limit the number of MAX trades per week.
* 🡅 I would Not Recommend using these functions without understanding how they work.
Order Size — Position Size
• NONE — Use the default position size settings in Tab "Properties".
• EQUITY — The amount of the allowed position as a percentage of the initial capital.
• Use Net Profit — On/Off the use of profit in the following trades. *Only works if the type is EQUITY.
• SIZE — The size of the allowed position in monetary terms.
• Contracts — The size of the allowed position in the contracts. 1 Сontract = Сurrent price.
________________
⚉ ＮＯＴＥＳ ⚉
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It is important to note that I have never worked with Backtesting and the functions associated with them before.
It took me about a month of slow work to build this system.
I want to say Big Thanks:
• The PineScripters🌲 group, the guys suggested how to implement some features. Especially @allanster
• Thanks to all those people who share their developments for free on TV and not only.
• I also thank myself for not giving up and finishing the project, and not trying to monetize the system by selling it. * Although I really want the money :)
I tried hard to make it as fast and convenient as possible for everyone who will use my code.
That's why I didn't use any libraries and dozens of heavy functions, and I managed to fit in 8+-functions for the whole code.
Absolutely every block of code I tried to make full-fledged modular, that it was easy to import/edit for myself (you).
I have abused the Ternary Pine operator a little (a lot) so that the code was as compact as possible.
Nevertheless, I tried very hard to keep my code very understandable even for beginners.
At last I managed to write 500 lines of code, making it one of the fastest and most feature-rich systems out there.
I hope everyone enjoys my work.
Put comments and write likes.
Backtest any Indicator v5Happy Trade,
here you get the opportunity to backtest any of your indicators like a strategy without converting them into a strategy. You can choose to go long or go short and detailed time filters. Further more you can set the take profit and stop loss, initial capital, quantity per trade and set the exchange fees. You get an overall result table and even a detailed, scroll-able table with all trades. In the Image 1 you see the provided info tables about all Trades and the Result Summary. Further more every trade is marked by a background color, Labels and Levels. An opening Label with the trade direction and trade number. A closing Label again with the trade number, the trades profit in % and the total amount of $ after all past trades. A green line for the take profit level and a red line for the stop loss.
Image 1
Example
For this description we choose the Stochastic RSI indicator from TradingView as it is. In Image 2 is shown the performance of it with decent settings.
Timeframe=45, BTCUSD, 2023-08-01 - 2023-10-20
Stoch RSI: k=30, d=40, RSI-length=140, stoch-length=140
Backtest any Indicator: input signal=Stoch RSI, goLong, take profit=9.1%, stop loss=2.5%, start capital=1000$, qty=5%, fee=0.1%, no Session Filter
Image 2
Usage
1) You need to know the name of the boolean (or integer) variable of your indicator which hold the buy condition. Lets say that this boolean variable is called BUY. If this BUY variable is not plotted on the chart you simply add the following code line at the end of your pine script.
For boolean (true/false) BUY variables use this:
plot(BUY ? 1:0,'Your buy condition hold in that variable BUY',display = display.data_window)
And in case your script's BUY variable is an integer or float then use instate the following code line:
plot(BUY ,'Your buy condition hold in that variable BUY',display = display.data_window)
2) Probably the name of this BUY variable in your indicator is not BUY. Simply replace in the code line above the BUY with the name of your script's trade condition variable.
3) Save your changed Indicator script.
4) Then add this 'Backtest any Indicator' script to the chart ...
5) and go to the settings of it. Choose under "Settings -> Buy Signal" your Indicator. So in the example above choose .
The form is usually: ' : BUY'. Then you see something like Image 2
6) Decide which trade direction the BUY signal should trigger. A go Long or a go Short by set the hook or not.
Now you have a backtest of your Indicator without converting it into a strategy. You may change the setting of your Indicator to the best results and setup the following strategy settings like Time- and Session Filter, Stop Loss, Take Profit etc. More of it below in the section Settings Menu.
Appereance
In the Image 2 you see on the right side the List of Trades . To scroll down you go into the settings again and decrease the scroll value. So you can see all trades that have happened before. In case there is an open trade you will find it at the last position of the list.
Every Long trade is green back grounded while Short trades are red.
Every trade begins with a label that show goLong or goShort and its number. And ends with another label again with its number, Profit in % and the resulting total amount of cash.
If activated you further see the Take Profit as a green line and the Stop Loss as a orange line. In the settings you can set their percentage above or below the entry price.
You also see the Result Summary below. Here you find the usual stats of a strategy of all closed trades. The profit after total amount of fees , amount of trades, Profit Factor and the total amount of fees .
Settings Menu
In the settings menu you will find the following high-lighted sections. Most of the settings have a question mark on their right side. Move over it with the cursor to read specific explanation.
Input Signal of your Indicator: Under Buy you set the trade signal of your Indicator. And under Target you set the value when a trade should happen. In the Example with the Stochastic RSI above we used 20. Below you can set the trade direction, let it be go short when hooked or go long when unhooked.
Trade Settings & List of Trades: Take Profit set the target price of any trade. Stop Loss set the price to step out when a trade goes the wrong direction. Check mark the List of Trades to see any single trade with their stats. In case that there are more trades as fits in the list you can scroll down the list by decrease the value Scroll .
Time Filter: You can set a Start Time or deactivate it by leave it unhooked. The same with End Time .
Session Filter: here you can choose to activate it on weekly base. Which days of the week should be trading and those without. And also on daily base from which time on and until trade are possible. Outside of all times and sessions there will be no new trades if activated.
Invest Settings: here you can choose the amount of cash to start with. The Quantity percentage define for every trade how much of the cash should be invested and the Fee percentage which have to be payed every trade. Open position and closing position.
Other Announcements
This Backtest script don't use the strategy functions of TradingView. It is programmed as an indicator. All trades get executed at candle closing. This script use the functionality "Indicator-on-Indicator" from TradingView.
Conclusion
So now it is your turn, take your promising indicators and connect it to that Backtest script. With it you get a fast impression of how successful your indicator will trade. You don't have to relay on coders who maybe add cheating code lines. Further more you can check with the Time Filter under which market condition you indicator perform the best or not so well. Also with the Session Filter you can sort out repeating good market conditions for your indicator. Even you can check with the GoShort XOR GoLong check mark the trade signals of you indicator in opposite trade direction with one click. And compare your indicators under the same conditions and get the results just after 2 clicks. Thanks to the in-build fee setting you get an impression how much a 0.1% fee cost you in total.
Cheers
Backtest History Setup 1.0Script of strategy component to setup the backtext lookback. You setup the maximum days back in the history, which will be used for backtest.
Backtest Custom HiLo StrategyThis script implements the HiLo trend follower strategy for backtesting the HiLo indicator. It comes with some custom options. Among them is the Type option that allows to choose between the moving averages for the highs and lows (HiLo), or highest and lowest values for those moving averages, respectively (HiLo Activator).
Portfolio Backtester Engine█ OVERVIEW
Portfolio Backtester Engine (PBTE). This tool will allow you to backtest strategies across multiple securities at once. Allowing you to easier understand if your strategy is robust. If you are familiar with the PineCoders backtesting engine , then you will find this indicator pleasant to work with as it is an adaptation based on that work. Much of the functionality has been kept the same, or enhanced, with some minor adjustments I made on the account of creating a more subjectively intuitive tool.
█ HISTORY
The original purpose of the backtesting engine (`BTE`) was to bridge the gap between strategies and studies . Previously, strategies did not contain the ability to send alerts, but were necessary for backtesting. Studies on the other hand were necessary for sending alerts, but could not provide backtesting results . Often, traders would have to manage two separate Pine scripts to take advantage of each feature, this was less than ideal.
The `BTE` published by PineCoders offered a solution to this issue by generating backtesting results under the context of a study(). This allowed traders to backtest their strategy and simultaneously generate alerts for automated trading, thus eliminating the need for a separate strategy() script (though, even converting the engine to a strategy was made simple by the PineCoders!).
Fast forward a couple years and PineScript evolved beyond these issues and alerts were introduced into strategies. The BTE was not quite as necessary anymore, but is still extremely useful as it contains extra features and data not found under the strategy() context. Below is an excerpt of features contained by the BTE:
"""
More than `40` built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
"""
Before I go any further, I want to be clear that the BTE is STILL a good tool and it is STILL very useful. The Portfolio Backtesting Engine I am introducing is only a tangental advancement and not to be confused as a replacement, this tool would not have been possible without the `BTE`.
█ THE PROBLEM
Most strategies built in Pine are limited by one thing. Data. Backtesting should be a rigorous process and researchers should examine the performance of their strategy across all market regimes; that includes, bullish and bearish markets, ranging markets, low volatility and high volatility. Depending on your TV subscription The Pine Engine is limited to 5k-20k historical bars available for backtesting, which can often leave the strategy results wanting. As a general rule of thumb, strategies should be tested across a quantity of historical bars which will allow for at least 100 trades. In many cases, the lack of historical bars available for backtesting and frequency of the strategy signals produces less than 100 trades, rendering your strategy results inconclusive.
█ THE SOLUTION
In order to be confident that we have a robust strategy we must test it across all market regimes and we must have over 100 trades. To do this effectively, researchers can use the Portfolio Backtesting Engine (PBTE).
By testing a strategy across a carefully selected portfolio of securities, researchers can now gather 5k-20k historical bars per security! Currently, the PTBE allows up to 5 securities, which amounts to 25k-100k historical bars.
█ HOW TO USE
1 — Add the indicator to your chart.
• Confirm inputs. These will be the most important initial values which you can change later by clicking the gear icon ⚙ and opening up the settings of the indicator.
2 — Select a portfolio.
• You will want to spend some time carefully selecting a portfolio of securities.
• Each security should be uncorrelated.
• The entire portfolio should contain a mix of different market regimes.
You should understand that strategies generally take advantage of one particular type of market regime. (trending, ranging, low/high volatility)
For example, the default RSI strategy is typically advantageous during ranging markets, whereas a typical moving average crossover strategy is advantageous in trending markets.
If you were to use the standard RSI strategy during a trending market, you might be selling when you should be buying.
Similarily, if you use an SMA crossover during a ranging market, you will find that the MA's may produce many false signals.
Even if you build a strategy that is designed to be used only in a trending market, it is still best to select a portfolio of all market regimes
as you will be able to test how your strategy will perform when the market does something unexpected.
3 — Test a built-in strategy or add your own.
• Navigate to gear icon ⚙ (settings) of strategy.
• Choose your options.
• Select a Main Entry Strat and Alternate Entry Strat .
• If you want to add your own strategy, you will need to modify the source code and follow the built-in example.
• You will only need to generate (buy 1 / sell -1/ neutral 0) signals.
• Select a Filter , by default these are all off.
• Select an Entry Stop - This will be your stop loss placed at the trade entry.
• Select Pyamiding - This will allow you to stack positions. By default this is off.
• Select Hard Exits - You can also think of these as Take Profits.
• Let the strategy run and take note of the display tables results.
• Portfolio - Shows each security.
• The strategy runs on each asset in your portfolio.
• The initial capital is equally distributed across each security.
So if you have 5 securities and a starting capital of 100,000$ then each security will run the strategy starting with 20,000$
The total row will aggregate the results on a bar by bar basis showing the total results of your initial capital.
• Net Profit (NP) - Shows profitability.
• Number of Trades (#T) - Shows # of trades taken during backtesting period.
• Typically will want to see this number greater than 100 on the "Total" row.
• Average Trade Length (ATL) - Shows average # of days in a trade.
• Maximum Drawdown (MD ) - Max peak-to-valley equity drawdown during backtesting period.
• This number defines the minimum amount of capital required to trade the system.
• Typically, this shouldn’t be lower than 34% and we will want to allow for at least 50% beyond this number.
• Maximum Loss (ML) - Shows largest loss experienced on a per-trade basis.
• Normally, don’t want to exceed more than 1-2 % of equity.
• Maximum Drawdown Duration (MDD) - The longest duration of a drawdown in equity prior to a new equity peak.
• This number is important to help us psychologically understand how long we can expect to wait for a new peak in account equity.
• Maximum Consecutive Losses (MCL) - The max consecutive losses endured throughout the backtesting period.
• Another important metric for trader psychology, this will help you understand how many losses you should be prepared to handle.
• Profit to Maximum Drawdown (P:MD) - A ratio for the average profit to the maximum drawdown.
• The higher the ratio is, the better. Large profits and small losses contribute to a good PMD.
• This metric allows us to examine the profit with respect to risk.
• Profit Loss Ratio (P:L) - Average profit over the average loss.
• Typically this number should be higher in trend following systems.
• Mean reversion systems show lower values, but compensate with a better win %.
• Percent Winners (% W) - The percentage of winning trades.
• Trend systems will usually have lower win percentages, since statistically the market is only trending roughly 30% of the time.
• Mean reversion systems typically should have a high % W.
• Time Percentage (Time %) - The amount of time that the system has an open position.
• The more time you are in the market, the more you are exposed to market risk, not to mention you could be using that money for something else right?
• Return on Investment (ROI) - Your Net Profit over your initial investment, represented as a percentage.
• You want this number to be positive and high.
• Open Profit (OP) - If the strategy has any open positions, the floating value will be represented here.
• Trading Days (TD) - An important metric showing how many days the strategy was active.
• This is good to know and will be valuable in understanding how long you will need to run this strategy in order to achieve results.
█ FEATURES
These are additional features that extend the original `BTE` features.
- Portfolio backtesting.
- Color coded performance results.
- Circuit Breakers that will stop trading.
- Position reversals on exit. (Simulating the function of always in the market. Similar to strategy.entry functionality)
- Whipsaw Filter
- Moving Average Filter
- Minimum Change Filter
- % Gain Equity Exit
- Popular strategies, (MACD, MA cross, supertrend)
Below are features that were excluded from the original `BTE`
- 2 stage in-trade stops with kick-in rules (This was a subjective decision to remove. I found it to be complex and thwarted my use of the `BTE` for some time.)
- Simple conversion from Study to Strategy modes. (Not possible with multiple securities)
- Coupling with your own external indicator (Not really practical to use with multiple securities, but could be used if signals were generated based on some indicator which was not based on the current chart)
- Use of the Data Window to show detailed bar by bar trade information and global statistics.
- Post Exit Analysis.
- Plotting of reminders and generation of alerts on in-trade events.
- Alerts (These may be added in the future by request when I find the time.)
█ THANKS
The whole PineCoders team for all their shared knowledge and original publication of the BTE and Richard Weismann for his ideas on building robust strategies.
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Why is it ok to backtest on TradingView from now on!TradingView backtester has bad reputation. For a good reason - it was producing wrong results, and it was clear at first sight how bad they were.
But this has changed. Along with many other improvements in its PineScript coding capabilities, TradingView fixed important bug, which was the main reason for miscalculations. TradingView didn't really speak out about this fix, so let me try :)
Have a look at this short code of a swing trading strategy (PLEASE DON'T FOCUS ON BACKTEST RESULTS ATTACHED HERE - THEY DO NOT MATTER). Sometimes entry condition happens together with closing condition for the already ongoing trade. Example: the condition to close Long entry is the same as a condition to enter Short. And when these two aligned, not only a Long was closed and Short was entered (as intended), but also a second Short was entered, too!!! What's even worse, that second short was not controlled with closing conditions inside strategy.exit() function and it very often lead to losses exceeding whatever was declared in "loss=" parameter. This could not have worked well...
But HOORAY!!! - it has been fixed and won't happen anymore. So together with other improvements - TradingView's backtester and PineScript is now ok to work with on standard candlesticks :)
Yep, no need to code strategies and backtest them on other platforms anymore.
----------------
Having said the above, there are still some pitfalls remaining, which you need to be aware of and avoid:
Don't backtest on HeikenAshi, Renko, Kagi candlesticks. They were not invented with backtesting in mind. There are still using wrong price levels for entries and therefore producing always too good backtesting results. Only standard candlesticks are reliable to backtest on.
Don't use Trailing Stop in your code. TradingView operates only on closed candlesticks, not on tick data and because of that, backtester will always assume price has first reached its favourable extreme (so 'high' when you are in Long trade and 'low' when you are in Short trade) before it starts to pull back. Which is rarely the truth in reality. Therefore strategies using Trailing Stop are also producing too good backtesting results. It is especially well visible on higher timeframe strategies - for some reason your strategy manages to make gains on those huge, fat candlesticks :) But that's not reality.
"when=" inside strategy.exit() does not work as you would intuitively expect. If you want to have logical condition to close your trade (for example - crossover(rsi(close,14),20)) you need to place it inside strategy.close() function. And leave StopLoss + TakeProfit conditions inside strategy.exit() function. Just as in attached code.
If you're working with pyramiding, add "process_orders_on_close=ANY" to your strategy() script header. Default setting ("=FIFO") will first close the trade, which was opened first, not the one which was hit by Stop-Loss condidtion.
----------------
That's it, I guess :) If you are noticing other issues with backtester and would like to share, let everyone know in comments. If the issue is indeed a bug, there is a chance TradingView dev team will hear your voice and take it into account when working on other improvements. Just like they heard about the bug I described above.
P.S. I know for a fact that more improvements in the backtesting area are coming. Some will change the game even for non-coding traders. If you want to be notified quickly and with my comment - gimme "follow".
How To Set Backtest Date RangeExample how to select and set date range window to be backtested. Normally when you change chart period it changes the number of days being backtested which means as you increas the chart period (for example from 5min to 15min) you also increase the number of days traded, so you can not compare apples to apples for which period would yield best returns for your strategy. Now you can. Incorporate this code replacing buy and sell with your strategy, then simply input the From and To dates in Format -> Inputs, and then change the chart period to view updated results.
NOTE: There is a limit in backtesting to 2000 orders, so please be aware of this when setting your date ranges. If you set your range too high, you may be exceeding this limit on some periods and not on others, so this would yield incorrect comparison of returns per period. If you see in your backtesting results that you are nearing this limit for one of your periods you are testing, then reduce the date range to a smaller number of days.
Enjoy!
(Thanks to @Gesundheit "Adeel" for pointing me in the right direction on this!)
Optimal Length BackTester [YinYangAlgorithms]This Indicator allows for a ‘Optimal Length’ to be inputted within the Settings as a Source. Unlike most Indicators and/or Strategies that rely on either Static Lengths or Internal calculations for the length, this Indicator relies on the Length being derived from an external Indicator in the form of a Source Input.
This may not sound like much, but this application may allows limitless implementations of such an idea. By allowing the input of a Length within a Source Setting you may have an ‘Optimal Length’ that adjusts automatically without the need for manual intervention. This may allow for Traditional and Non-Traditional Indicators and/or Strategies to allow modifications within their settings as well to accommodate the idea of this ‘Optimal Length’ model to create an Indicator and/or Strategy that adjusts its length based on the top performing Length within the current Market Conditions.
This specific Indicator aims to allow backtesting with an ‘Optimal Length’ inputted as a ‘Source’ within the Settings.
This ‘Optimal Length’ may be used to display and potentially optimize multiple different Traditional Indicators within this BackTester. The following Traditional Indicators are included and available to be backtested with an ‘Optimal Length’ inputted as a Source in the Settings:
Moving Average; expressed as either a: Simple Moving Average, Exponential Moving Average or Volume Weighted Moving Average
Bollinger Bands; expressed based on the Moving Average Type
Donchian Channels; expressed based on the Moving Average Type
Envelopes; expressed based on the Moving Average Type
Envelopes Adjusted; expressed based on the Moving Average Type
All of these Traditional Indicators likewise may be displayed with multiple ‘Optimal Lengths’. They have the ability for multiple different ‘Optimal Lengths’ to be inputted and displayed, such as:
Fast Optimal Length
Slow Optimal Length
Neutral Optimal Length
By allowing for the input of multiple different ‘Optimal Lengths’ we may express the ‘Optimal Movement’ of such an expressed Indicator based on different Time Frames and potentially also movement based on Fast, Slow and Neutral (Inclusive) Lengths.
This in general is a simple Indicator that simply allows for the input of multiple different varieties of ‘Optimal Lengths’ to be displayed in different ways using Tradition Indicators. However, the idea and model of accepting a Length as a Source is unique and may be adopted in many different forms and endless ideas.
Tutorial:
You may add an ‘Optimal Length’ within the Settings as a ‘Source’ as followed in the example above. This Indicator allows for the input of a:
Neutral ‘Optimal Length’
Fast ‘Optimal Length’
Slow ‘Optimal Length’
It is important to account for all three as they generally encompass different min/max length values and therefore result in varying ‘Optimal Length’s’.
For instance, say you’re calculating the ‘Optimal Length’ and you use:
Min: 1
Max: 400
This would therefore be scanning for 400 (inclusive) lengths.
As a general way of calculating you may assume the following for which lengths are being used within an ‘Optimal Length’ calculation:
Fast: 1 - 199
Slow: 200 - 400
Neutral: 1 - 400
This allows for the calculation of a Fast and Slow length within the predetermined lengths allotted. However, it likewise allows for a Neutral length which is inclusive to all lengths alloted and may be deemed the ‘Most Accurate’ for these reasons. However, just because the Neutral is inclusive to all lengths, doesn’t mean the Fast and Slow lengths are irrelevant. The Fast and Slow length inputs may be useful for seeing how specifically zoned lengths may fair, and likewise when they cross over and/or under the Neutral ‘Optimal Length’.
This Indicator features the ability to display multiple different types of Traditional Indicators within the ‘Display Type’.
We will go over all of the different ‘Display Types’ with examples on how using a Fast, Slow and Neutral length would impact it:
Simple Moving Average:
In this example above have the Fast, Slow and Neutral Optimal Length formatted as a Slow Moving Average. The first example is on the 15 minute Time Frame and the second is on the 1 Day Time Frame, demonstrating how the length changes based on the Time Frame and the effects it may have.
Here we can see that by inputting ‘Optimal Lengths’ as a Simple Moving Average we may see moving averages that change over time with their ‘Optimal Lengths’. These lengths may help identify Support and/or Resistance locations. By using an 'Optimal Length' rather than a static length, we may create a Moving Average which may be more accurate as it attempts to be adaptive to current Market Conditions.
Bollinger Bands:
Bollinger Bands are a way to see a Simple Moving Average (SMA) that then uses Standard Deviation to identify how much deviation has occurred. This Deviation is then Added and Subtracted from the SMA to create the Bollinger Bands which help Identify possible movement zones that are ‘within range’. This may mean that the price may face Support / Resistance when it reaches the Outer / Inner bounds of the Bollinger Bands. Likewise, it may mean the Price is ‘Overbought’ when outside and above or ‘Underbought’ when outside and below the Bollinger Bands.
By applying All 3 different types of Optimal Lengths towards a Traditional Bollinger Band calculation we may hope to see different ranges of Bollinger Bands and how different lookback lengths may imply possible movement ranges on both a Short Term, Long Term and Neutral perspective. By seeing these possible ranges you may have the ability to identify more levels of Support and Resistance over different lengths and Trading Styles.
Donchian Channels:
Above you’ll see two examples of Machine Learning: Optimal Length applied to Donchian Channels. These are displayed with both the 15 Minute Time Frame and the 1 Day Time Frame.
Donchian Channels are a way of seeing potential Support and Resistance within a given lookback length. They are a way of withholding the High’s and Low’s of a specific lookback length and looking for deviation within this length. By applying a Fast, Slow and Neutral Machine Learning: Optimal Length to these Donchian Channels way may hope to achieve a viable range of High’s and Low’s that one may use to Identify Support and Resistance locations for different ranges of Optimal Lengths and likewise potentially different Trading Strategies.
Envelopes / Envelopes Adjusted:
Envelopes are an interesting one in the sense that they both may be perceived as useful; however we deem that with the use of an ‘Optimal Length’ that the ‘Envelopes Adjusted’ may work best. We will start with examples of the Traditional Envelope then showcase the Adjusted version.
Envelopes:
As you may see, a Traditional form of Envelopes even produced with a Machine Learning: Optimal Length may not produce optimal results. Unfortunately this may occur with some Traditional Indicators and they may need some adjustments as you’ll notice with the ‘Envelopes Adjusted’ version. However, even without the adjustments, these Envelopes may be useful for seeing ‘Overbought’ and ‘Oversold’ locations within a Machine Learning: Optimal Length standpoint.
Envelopes Adjusted:
By adding an adjustment to these Envelopes, we may hope to better reflect our Optimal Length within it. This is caused by adding a ratio reflection towards the current length of the Optimal Length and the max Length used. This allows for the Fast and Neutral (and potentially Slow if Neutral is greater) to achieve a potentially more accurate result.
Envelopes, much like Bollinger Bands are a way of seeing potential movement zones along with potential Support and Resistance. However, unlike Bollinger Bands which are based on Standard Deviation, Envelopes are based on percentages +/- from the Simple Moving Average.
We will conclude our Tutorial here. Hopefully this has given you some insight into how useful adding a ‘Optimal Length’ within an external (secondary) Indicator as a Source within the Settings may be. Likewise, how useful it may be for automation sake in the sense that when the ‘Optimal Length’ changes, it doesn’t rely on an alert where you need to manually update it yourself; instead it will update Automatically and you may reap the benefits of such with little manual input needed (aside from the initial setup).
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
[Sextan] Backtesting with L2 Reversal Labels as an input sourceLevel: 1
NOTE: This is ONLY an EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" intput signal with "(blackcat) L2 Reversal Labels", and you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error.
I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example.
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
[fareid] Quick Backtest Framework█ OVERVIEW
This Framework allows Pine Coders to quickly code Study() based signal/strategy and validate its viability before proceed to code with more advance/complex customized rules for entry, exit, trailstop, risk management etc..
This is somewhat an upgraded version of my earlier personal template with different strategy used, cleaner code
and additional features.
█ USE CASES
- You have an idea for trade signal and need a quick way to verify its potential before writing lengthy/complicated code
- You found a study script for trading signal in public library and want to validate it profitability with minimum effort before including it in your trading playbook
█ FEATURES
- Alert: Ready to use alert function based on signals from your custom indicator.
- Visual Backtest: Auto-plot entry, stop-loss and take profit for simple strategy performance analysis
- Backtest Statistic: Provide basic key metrics based on backtest strategy
- BTE External Signal Protocol: Ready to use code that will supply required state to PineCoders Backtesting & Trading Engine if you wish to have more advance and sophisticated backtesting engine
Notes: All of the above features have On/Off toggle
█ Description & How To Use
This Framework consist of 5 Modules but you only need to edit the first 2 Modules:
Module1: Indicator
Module2: Framework Input Protocol
Module3: Alert
Module4: Backtest
Module5: Backtest & Trading Engine
Tips: The source-code includes collapsible block by module for easy navigating
Module1: Indicator:
-----------------------------------------------------------------------------------
Main Module. Place custom indicator input parameter/calculation/indicator plotting here
Sample Strategy: Double MACD Crossover
MACD Signal: 1st MACD Cross above signal line indicate Buy Signal
1st MACD Cross below signal line indicate Sell Signal
MACD Filter: 2nd MACD is above 0 line indicate Uptrend
2nd MACD is below 0 line indicate Downtrend
Module2: Framework Input Protocol:
-----------------------------------------------------------------------------------
Use this module to connect main indicator/signal calculated in Module1 to the rest of the framework's module
4 variables needed to be defined here:
1. Uptrend
2. Dntrend
3. BuySignal
4. SellSignal
i'm not sure how to place a code snippet here to show you example so in the source code i already put a comment in Module2 on which part u need to edit. I hope its pretty simple to use.
Module3: Alert Module Description:
-----------------------------------------------------------------------------------
As long as the variables in Module2 properly defined, the alert module is ready to use without any further modification.
Input:
Enable Alert --> Enable TV's alert and plot signal to chart
Alert Type --> Set to take Buy only, Sell only or Both alert
Module4: Backtest Module Description:
-----------------------------------------------------------------------------------
As long as the variables in Module2 properly defined, the backtest module is ready to use without any further modification.
Input:
Backtest Stat --> Enable Backtest Statistic Label
Backtest Visual --> Enable Backtest visual simulation
Backtest Type --> Set to take Buy only or Sell only or both
SL Type -->
ATR : Set SL in ATR times Multiplier below entry price
Fixed : Set SL in fixed point below entry point (in 'Dollar'). e.g. for Stocks -> 0.5 equals to 50cent while for EURUSD currency -> 0.005 equal to 50 pips
HiLo Bar: Set SL at highest/lowest wick of previous bar plus/minus Fixed point. e.g. EURUSD HiLo=3 and Fixed Point = 0.0005, buy trade will place SL 5 Pips below lowest of previous 3 bar
SL ATR Multi --> Set Lookback Period used for SL's ATR calculation
SL ATR Multi --> Set ATR Multiplier for SL
SL Fixed --> Set Fixed Level for SL
SL Bar --> Set Number of previous bar to check for SL placement
TP RR Ratio --> Set TP based on RR multiplier. e.g. 2 means TP level will be twice further from entry point compared to Entry-SL distance.
Notes: The point is for preliminary testing, so it only supports 1 trade at a time and no Trailing Stop
Module5: Backtest & Trading Engine Description:
-----------------------------------------------------------------------------------
As long as the variables in Module2 properly defined, the Pinecoders BTE module is ready to use without any further modification.
Input:
External Signal Protocol --> Set ESP State to send to "Backtesting & Trading Engine "
Signal With Filter --> Use this to send entry signal that already filtered by this study indicator (without stoploss level)
Signal Without Filter --> Use this to send raw entry signal that are NOT YET FILTERED by this study indicator (without stoploss level)
Signal and Stop With Filter --> Use this to send entry signal WITH StopLoss that already filtered by this study indicator (with stoploss level)
Signal and Stop Without Filter --> Use this to send raw entry signal WITH StopLoss that are NOT YET FILTERED by this study indicator (with stoploss level)
Notes: Backtesting & Trading Engine already have built-in Filter, Entries and Stop Level. e.g. Unselect all their filter state if only want to use custom filter and make sure send Signal with Filter (with or without SL level)
█ DISCLAIMER:
This framework main objective is to create my personal indicator template so that i just have to modify the indicator module for preliminary testing in future.
The sample strategy included are for educational purpose only. Use at your own risk
credit: LucF/PineCoders for a lot of his scripts that i use as a guide to complete this
Template Trailing Strategy (Backtester)💭 Overview
💢 What is the "Template Trailing Strategy” ❓
The "Template Trailing Strategy" (TTS) is a back-tester orchestration framework. It supercharges the implementation-test-evaluation lifecycle of new trading strategies, by making it possible to plug in your own trading idea.
While TTS offers a vast number of configuration settings, it primarily allows the trader to:
Test and evaluate your own trading logic that is described in terms of entry, exit, and cancellation conditions.
Define the entry and exit order types as well as their target prices when the limit, stop, or stop-limit order types are used.
Utilize a variety of options regarding the placement of the stop-loss and take-profit target(s) prices and support for well-known techniques like moving to breakeven and trailing.
Provide well-known quantity calculation methods to properly handle risk management and easily evaluate trading strategies and compare them.
Alert on each trading event or any related change through a robust and fully customizable messaging system.
All the above, build a robust tool that, once learned, significant and repetitive work that strategy developers often implement individually on every strategy script is eliminated. Taking advantage of TradingView’s built-in backtesting engine the evaluation of the trading ideas feels natural.
By utilizing the TTS one can easily swap “trading logic” by testing, evaluating, and comparing each trading idea and/or individual component of a strategy.
Finally, TTS, through its per-event alert management (and debugging) system, provides a fully automated solution that supports automated trading with real brokers via webhooks.
NOTE: The “Template Trailing Strategy” does not dictate the way you can combine different (types of) indicators or how you should combine them. Thus, it should not be confused as a “Trading System”, because it gives its user full flexibility on that end (for better or worse).
💢 What is a “Signal Indicator” ❓
“Signal Indicator” (SI) is an indicator that can output a “signal” that follows a specific convention so that the “Template Trailing Strategy” can “understand” and execute the orders accordingly. The SI realizes the core trading logic signaling to the TTS when to enter, exit, or cancel an order. A SI instructs the TTS “when” to enter or exit, and the TTS determines “how” to enter and exit the position once the Signal Indicator generates a signal.
A very simple example of a Signal Indicator might be a 200-day Simple Moving Average Signal. When the price of the security closes above the 200-day SMA, a SI would provide TTS with a “long entry signal”. Once TTS receives the “long entry signal”, the TTS will open a long position and send an alert or automated trade message via webhook to a broker, based on the Entry settings defined in TTS. If the TTS Entry settings specify a “Market” order type, then the open long position will be executed by TTS immediately. But if the TTS Entry settings specify a “Stop” order type with a 1% Stop Distance, then when the price of the security rises by 1% after the “long entry signal” occurs, the TTS will open a long position and the Long Entry alert or webhook to the broker will be sent.
🤔 How to Guide
💢 How to connect a “signal” from a “Signal Indicator” ❓
The “Template Trailing Strategy” was designed to receive external signals from a “Signal Indicator”. In this way, a “new trading idea” can be developed, configured, and evaluated separately from the TTS. Similarly, the SI can be held constant, and the trading mechanics can change in the TTS settings and back-tested to answer questions such as, “Am I better with a different stop loss placement method, what if I used a limit order instead of a stop order to enter, what if I used 25% margin instead of trading spot market?”
To make that possible by connecting an external signal indicator to TTS, you should:
Add in the same chart, the “Signal Indicator” of your choice (e.g. “Two MA Signal Indicator” , “Click Signal Indicator” , “Signal Adapter” , “Signal Composer” ) and the “Template Trailing Strategy”.
Go to the “Settings/Inputs” tab in the “🛠️ STRATEGY” group of the TTS and change the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" to “🔨External”
Go to the “🔨 STRATEGY – EXTERNAL” group settings of the TTS and change the “🔌𝐒𝐢𝐠𝐧𝐚𝐥 🛈➡” to the output signal of the “Signal Indicator” you want to connect. The selected combo box option should look like “:🔌Signal to TTS” where should correspond to the short title of your “Signal Indicator”
💢 How to create a Custom Trading logic ❓
The “Template Trailing Strategy” provides two ways to plug in your custom trading logic. Both of them have their advantages and disadvantages.
✍️ Develop your own Customized “Signal Indicator” 💥
The first approach is meant to be used for relatively more complex trading logic. The advantages of this approach are the full control and customization you have over the trading logic and the relatively simple configuration setup by having two scripts only. The downsides are that you have to have some experience with pinescript or you are willing to learn and experiment. You should also know the exact formula for every indicator you will use since you have to write it by yourself. Copy-pasting from existing open-source indicators will get you started quite fast though.
The idea here is either to create a new indicator script from scratch or to copy an existing non-signal indicator and make it a “Signal Indicator”. To create a new script, press the “Pine Editor” button below the chart to open the “Pine Editor” and then press the “Open” button to open the drop-down menu with the templates. Select the “New Indicator” option. Add it to your chart to copy an existing indicator and press the source code {} button. Its source code will be shown in the “Pine Editor” with a warning on top stating that this is a read-only script. Press the “create a working copy”. Now you can give a descriptive title and a short title to your script, and you can work on (or copy-paste) the (other) indicators of your interest. Having all the information needed to make your decision the only thing you should do is define a DealConditions object and plot it like this:
import jason5480/tts_convention/4 as conv
// Calculate the start, end, cancel start, cancel end conditions
dealConditions = conv.DealConditions.new(
startLongDeal = ,
startShortDeal = ,
endLongDeal = ,
endShortDeal = ,
cnlStartLongDeal = ,
cnlStartShortDeal = ,
cnlEndLongDeal = ,
cnlEndShortDeal = )
// Use this signal in scripts like "Template Trailing Strategy" and "Signal Composer" that can use its value
// Emit the current signal value according to the "two channels mod div" convention
plot(series = conv.getSignal(dealConditions), title = '🔌Signal to TTS', color = color.olive, display = display.data_window + display.status_line, precision = 0)
You should write your deal conditions appropriately based on your trading logic and put them in the code section shown above by replacing the “…” part after “=”. You can omit the conditions that are not relevant to your logic. For example, if you use only market orders for entering and exiting your positions the cnlStartLongDeal, cnlStartShortDeal, cnlEndLongDeal, and cnlEndShortDeal are irrelevant to your case and can be safely omitted from the DealConditions object. After successfully compiling your new custom SI script add it to the same chart with the TTS by pressing the “Add to chart” button. If all goes well, you will be able to connect your “signal” to the TTS as described in the “How to connect a “signal” from a “Signal Indicator”?” guide.
🧩 Adapt and Combine existing non-signal indicators 💥
The second approach is meant to be used for relatively simple trading logic. The advantages of this approach are the lack of pine script and coding experience needed and the fact that it can be used with closed-source indicators as long as the decision-making part is displayed as a line in the chart. The drawback is that you have to have a subscription that supports the “indicator on indicator” feature so you can connect the output of one indicator as an input to another indicator. Please check if your plan supports that feature here
To plug in your own logic that way you have to add your indicator(s) of preference in the chart and then add the “Signal Adapter” script in the same chart as well. This script is a “Signal Indicator” that can be used as a proxy to define your custom logic in the CONDITIONS group of the “Settings/Inputs” tab after defining your inputs from your preferred indicators in the VARIABLES group. Then a “signal” will be produced, if your logic is simple enough it can be directly connected to the TTS that is also added to the same chart for execution. Check the “How to connect a “signal” from a “Signal Indicator”?” in the “🤔 How to Guide“ for more information.
If your logic is slightly more complicated, you can add a second “Signal Adapter” in your chart. Then you should add the “Signal Composer” in the same chart, go to the SIGNALS group of the “Settings/Inputs” tab, and connect the “signals” from the “Signal Adapters”. “Signal Composer” is also a SI so its composed “signal” can be connected to the TTS the same way it is described in the “How to connect a “signal” from a “Signal Indicator”?” guide.
At this point, due to the composability of the framework, you can add an arbitrary number (bounded by your subscription of course) of “Signal Adapters” and “Signal Composers” before connecting the final “signal” to the TTS.
💢 How to set up ⏰Alerts ❓
The “Template Trailing Strategy” provides a fully customizable per-even alert mechanism. This means that you may have an entirely different message for entering and exiting into a position, hitting a stop-loss or a take-profit target, changing trailing targets, etc. There are no restrictions, and this gives you great flexibility.
First of all, you have to enable the alerts of the events that interest you. Go to the “🔔 ALERT MESSAGES” module of the TTS settings and check the “Enable…” checkbox of the events you are interested in. For each specific event, you will find a text area where you can type the exact message you want to receive when the event occurs. What’s more, there are placeholders you can use that will be replaced by the TTS with the actual values before the message is sent. The placeholder categories are the following and the placeholder names are self-explanatory.
Chart info: {{ticker}}, {{base_currency}}, {{quote_currency}}
Quantities and percentages: {{base_quantity}}, {{quote_quantity}}, {{quote_quantity_perc}},
{{take_profit_base_quantity}}, {{remaining_quantity_perc}}, {{remaining_base_quantity}}, {{risk_perc}}
Target prices: {{stop_loss_price}}, {{entry_price}}, {{entry+_price}}, {{entry-_price}},
{{exit_price}}, {{exit+_price}}, {{exit-_price}}, {{take_profit_price_1}},
{{take_profit_price_2}}, {{take_profit_price_3}}, {{take_profit_price_4}}, {{take_profit_price_5}}
❗ To get the message on the other side you have to set a strategy alert as described here and use the {{strategy.order.alert_message}} placeholder as text in the “Message Box” that contains the message that came from the TTS.
💢 How to execute my orders in a broker ❓
To execute your orders in a broker that supports webhook integration, you should enable the appropriate alerts in the “Template Trailing Strategy” first (see the “How to set up Alerts?” guide above). Then you should go to the “Create Alert/Notifications” tab check the “Webhook URL” and paste the URL provided by your broker. You have to read the documentation of your broker for more information on what messages are expected.
Keep in mind that some brokers have deep integration with TradingView so a per-event alert approach might be overkill.
📑 Definitions
This section tries to give some definitions in terms that appear in the “Settings/Inputs" tab of the “Template Trailing Strategy”
💢 What is Trailing ❓
Trailing is a technique where a price target follows another “barrier” price (usually high or low) by trying to keep a maximum distance from the “barrier” when it moves in only one direction (up or down). When the “barrier” moves in the other direction the price target will not change. There are as many types of trailing as price targets, which means that there are entry trailing, exit trailing, stop-loss trailing, and take-profit trailing techniques.
💢 What is a Moonbag ❓
A Moonbag in a trade is the quantity of the position that is reserved and will not be exited even if all take-profit targets defined in the strategy are hit, the quantity will be exited only if the stop-loss is hit or a close signal is received. This makes the stop-loss trailing technique in a trend-following strategy a good candidate to take advantage of a Moonbag.
💢 What is Distance ❓
Distance is the difference between two prices.
💢 What is Bias ❓
Bias is a psychological phenomenon where you make decisions based on market sentiment. For example, when you want to enter a long position you have a long bias, and when you want to exit from the long position you have a short bias. It is the other way around for the short position.
💢 What is the Margin Distance of a price target ❓
The Margin Distance of a price target is the distance that the target will deviate from its initial price. The direction of this deviation depends on the bias of the market. For example, suppose you are in a long position, and you set a take-profit target to the local high (HHLL). In that case, adding a margin of five ticks will place your take-profit target 5 ticks below this local high because you have a short bias when exiting a long position. When the bias is long the margin will be added resulting in a higher target price and when you have a short bias the margin will be subtracted.
⚙️ Settings
In the “Settings/Inputs” tab of the “Template Trailing Strategy”, you can find all the customizable settings that are provided by the framework. The variety of those settings is vast; hence we will only scratch the surface here. However, for every setting, there is an information icon 🛈 where you can learn more if you mouse over it. The “Settings/Inputs” tab is divided into ten main groups. Each one of them is responsible for one module of the framework. Every setting is part of a group that is named after the module it represents. So, to spot the module of a setting find the title that appears above it comes with an emoji and uppercase letters. Some settings might have the same name but belong to different modules e.g. “Distance Method”. Some settings are indented, which means that are closely related to the non-indented setting above. Usually, intended settings provide further configuration for one or more options of the non-intended setting. The groups that correspond to each module of the framework are the following:
📆 FILTERS
In this module time filters are implemented. You can define a DateTime window for your strategy to run. You can also specify a session by selecting the days of the week and the time range you want to operate.
🛠️ STRATEGY
This module contains the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" that defines if the “Template Trailing Strategy” will operate using the Internal or the External (“Signal Indicator”) conditions. Some general settings can be applied regardless of the mode.
🔨 STRATEGY – EXTERNAL
This sub-module makes the connection between the external signal of the “Signal Indicator” and the “Template Trailing Strategy”. It takes effect only if the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" is set to “🔨External”.
🔧 STRATEGY – INTERNAL
This sub-module defines the internal strategy logic and it's used as an example to demonstrate this framework. It should produce the same results as if the “Two MA Signal Indicator” was used as a “signal” in external mode. It takes effect only if the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" is set to “🔧Internal”.
🎢 VOLATILITY
This module defines the volatility parameters that are used in various other settings like average true range and standard deviation. It also makes it clear whether their values are updated during a trade (DYNAMIC) or not (STATIC).
🔷 ENTRY
This module defines how the start deal conditions will be executed by defining the order type of your entry and all necessary parameters to execute them.
🎯 TAKE PROFIT
This module defines the take-profit targets placement logic. The number of the take-profit targets to use, their distance from the entry price, and the distance from each other are only some of the features that can be configured.
🛑 STOP LOSS
This module defines the stop-loss target placement logic. The distance from the entry price, move to break even, and start trailing after a take-profit target is hit are only some of the features that can be configured.
🟪 EXIT
This module defines how the end deal conditions will be executed by defining the order type of your exit and all necessary parameters to execute them.
💰 QUANTITY/RISK MANAGEMENT
This module defines the method that calculates the amount of money you will put into each trade. Also, the percentage of the Moonbag quantity can be configured.
📊 ANALYTICS
This module can visualize some extra analytics of the strategy in the chart and calculate some metrics to measure the overall performance.
🔔 ALERT MESSAGES
This module defines all the messages that can be emitted per event during the strategy execution.
😲 Caveats
💢 Does “Template Trailing Strategy” has a repainting behavior ❓
The answer is that the “Template Trailing Strategy” does not repaint as long as the “Signal Indicator” that is connected also does not repaint. If you developed your own SI make sure that you understand and know how to prevent this behavior. The publication by @PineCoders here will give you a good idea on how to avoid most of the repainting cases.
⚠️There is an exception though, when the “Enable Trail⚠️💹” checkbox is checked, the Take Profit trailing feature is enabled, and a tick-based approach is used, meaning that after a while, when the TradingView discards all the real-time data, assumptions will be made by the backtesting engine that will cause a form of repainting. To avoid making false assumptions please disable this feature in the early stages and evaluate its usefulness in your strategy later on, after first confirming the success of the logic without this feature. In this case, consider turning on the bar magnifier feature. This way you will get more accurate backtest results when the Take Profit trailing feature is enabled.
💢 Can “Template Trailing Strategy” satisfy all my trading strategies ❓
While this framework can satisfy quite a large number of trading strategies there are cases where it cannot do so. For example, if you have a custom logic for your stop-loss or take-profit placement, or if you want to dollar cost average, then it might be better to start a new strategy script from scratch.
⚠️ It is not recommended to copy the official TTS code and start developing unless you are a pine wizard! Even in that case, there is a stiff learning curve that might not be worth your time. Last, you must consider that I do not offer support for customized versions of the TTS script and if something goes wrong in the process you are all alone.
🤗 Thanks
Special thanks to @upslidedown and @metadimensional, who regularly gave feedback all those years and helped me to shape the framework as it is today! Thanks to @EltAlt, @PlusUltraTrading, and everyone else who contributed by either filing a “defect report” or asking questions that helped me to understand what improvements were necessary.
Enjoy!
Jason
WhaleCrew BacktesterBacktesting indicators is easy , just add the following line of code to your script:
plot(longEntry ? 1 : shortEntry ? -1 : longTP ? 0.5 : shortTP ? -0.5 : 0, color=na, editable=false, title='Backtest')
These numbers are defined as constants in the backtester source-code.
After adding this indicator to your chart:
1. Open Settings
2. Select supported indicator to backtest
3. Select if you want to enter Longs and/or Shorts
4. Open the 'Strategy Tester' at the bottom to check the performance
Remember:
past performance is not indicative of future results
repainting indicators will create wrong/unrealistic results
