traderlab

Disclaimer

It is important to note that trading in financial markets involves inherent risks, including the potential loss of capital. The results presented within this package may not be suitable for all investors, and past performance is not indicative of future results.

Users are solely responsible for evaluating the suitability of any trading strategy for their own individual circumstances and risk tolerance. The author of this software shall not be held liable for any losses, damages, or other consequences arising from the use or application of the results presented herein in real trading environments.

Before engaging in any trading activity, users are strongly advised to consult with a qualified financial advisor and to thoroughly understand the risks involved. By using this software, users acknowledge and accept that they do so at their own risk, and the author shall not be held responsible for any outcomes resulting from its use.

What is traderlab?

traderlab is a package for fast trading strategies design, backtesting and quantitative analysis with practical applications. It allows the creation of strategies by the composition of a YAML file where data, indicators, rules and money management, among other information required for the backtesting engine, are specified. Simple commands run the YAML file, show the results and plot them.

This current version has a series of limitations since it has been developed for a specific need and might not be suitable for other uses. Here are the most important ones:

• Only Binance and Yahoo data download is implemented so far. Other sources and datasets are technically possible as long as they contain OHLCV data and this is provided in the expected format.

• Strategy rules are evaluated at the close of the last bar.

• The user can only indicate an out-of-sample period by defining its start date. Then the period will cover from the specified date until last available time in the dataset.

• Each model execution overwrites the results from previous runs. The reason behind this is that a large number of worthless backtests and metrics are usually generated, taking up a lot of space and making it very difficult to explore later. In the future, the user will be able to store the results in a more permanent database, allowing more advanced features.

What is not traderlab?

traderlab is not a package for typical financial analysis and there is not a intention to substitute them. Instead, traderlab is focused on trading markets by means of technical indicators and pursuing a practical approach.

The package does not find for the user profitable strategies neither. The user must provide the indicators and trading rules and he/she is the responsible of evaluating whether or not the strategy is suitable for him/her.

In the current version, traderlab does not execute real trading in any case. As its name suggests, it is intended to be a trading strategies laboratory to test and analyze them.

How does traderlab work?

While other packages in R, Python and other languages require from a moderate to an intensive coding to specify and test a trading strategy, traderlab makes this task easy for the user.

A YAML file (basically a text file) is almost everything a user has to worry about. This file contains what we call a model.

A model is nothing but indicators and rules with variables involved, and additional information. Part of this information specifies the range of values that the variables can take, a preliminary target metric, an out-of-sample period, etc.

The target metric does not exclude other important metrics. All of them will be available after the backtesting, but the target is useful as it informs to the user about the performance without waiting to the end as long as the previewer is set to true.

Installation

Install the current development version of traderlab as follows:

install.packages("devtools")
devtools::install_github("jmartindelasierra/traderlab")

In the DESCRIPTION file you can see that traderlab has many dependencies. If your installation fails and you get lost in the output messages, then be patient and install the dependencies one by one so you can focus on the failing dependency.

Data download

Binance

Binance data can be downloaded with the get_binance_ohlcv_data() function.

This function downloads all the available files in the Binance repository and consolidates them in a single dataset. Finally, it is saved in ./datasets from your working folder.

The following example downloads the 4-hour history for Bitcoin/USDT:

get_binance_ohlcv_data(symbol = "BTCUSDT", timeframe = "4h")

After the download, the file btcusdt_4h.rds will be available in ./datasets.

Yahoo

You can also download other data for different assets with the get_yahoo_ohlcv_data() function.

The data from this source will be limited to daily OHLCV. In addition, you may get data with missing values, which are replaced (not very accurately) with the previous non-missing value.

The following examples download data for SP500, Tesla, and EUR/USD:

get_yahoo_ohlcv_data(symbol = "SPY")
get_yahoo_ohlcv_data(symbol = "TSLA")
get_yahoo_ohlcv_data(symbol = "EUR=X")

The datasets are then saved as spy_1d.rds, tsla_1d.rds and eur=x_1d.rds in ./datasets.

Using data from other sources

Despite not providing functions for downloading data from different sources than Binance and Yahoo, there is not technical inconvenient to use other sources.

As you have probably guessed, traderlab works with open, high, low and close prices, and volume data. Two additional features are also required: open_time and close_time. So, if you want to use other datasets, ensure you can provide to traderlab a data.frame in .rds format with the following columns:

column	class
open_time	POSIXct
open	numeric
high	numeric
low	numeric
close	numeric
volume	numeric
close_time	POSIXct

Note: do not use any of the previous column names for naming your indicators or variables to avoid collisions and unexpected behavior.

Model anatomy

Remember that a model is a YAML file containing the indicators setup, strategy rules, money management and other information for the backtest engine. This concept allows the user create and test strategies avoiding to code them as required in many other solutions.

We made an effort to design a YAML structure as simple as possible resulting in a set of lists of key-values as we will show soon.

The following sections (lists) must exist in a model:

description:
indicators:
rules:
management:
periods:
target:
steps:

Let’s look at each one in detail.

Description

Here is where we annotate descriptive information about the model including the data source path:

description:
  name:
  symbol:
  timeframe:
  data:

data is the only mandatory key and a path to the data source is expected here (e.g. ./datasets/btcusdt_4h.rds). However, we recommend to add values to the other keys as this helps to keep organized your work by giving the model a name and specifying the symbol and timeframe.

Note: strings do not need to be quoted as formats are automatically detected. This applies to the entire YAML file.

Indicators

This section must have the following structure:

indicators:
  name1:
    indicator:
    parameter1:
    parameter2:
  name2:
    indicator:
    parameter1:
  ...

Here, indicators announces the indicators section start. Each indicator must be identified by an unique name of your choice that will be used for referencing later. The indicator key is used to call a listed indicator (see the accepted indicators list) and the following parameter keys are used to setup such indicator.

Rules

The rules section is declared as follows:

rules:
  entry_signal:
  exit_signal:
  stop_loss_price:

entry_signal and exit_signal must contain the logical expressions that indicate whether a trade entry or exit takes place (e.g. close > ema). These two signals are necessary to run a model.

stop_loss_price must contain a expression which evaluation returns a stop loss price (e.g. entry_price - 100). Stop loss is not mandatory.

Management

Here is how you tell the engine about managing the capital:

management:
  position:
  start_capital:
  interest_type:
  reinvest:
  leverage:
  fee_type:
  fee:

position can be long or short.

The reinvest must be provided in a percentage between 0 and 1. If interest_type is simple, the reinvest is recomputed as reinvest * start_capital, reinvesting a fixed capital per trade. If interest_type is compound, each trade will have available the percentage portion of the current capital.

fee_type concerns to a fixed amount or a percentage per trade. If it is configured as per_trade_fixed, fee will be provided in dollars. If on the other hand is configured as per_trade_pct, fee must be provided in a percentage between 0 and 1.

Periods

This section allows us cropping the dataset by defining a start and/or an end date. In addition, oos_start is used to indicate the out-of-sample start period. We can only indicate the start as automatically the last bar in the dataset becomes the out-of-sample end:

periods:
  start:
  end:
  oos_start:

Target

The target is intended for preliminary evaluation of the model. This is especially useful if preview is set to TRUE in the run model call. You must specify a metric to evaluate, set the operation type whether you look to maximize or minimize the metric, and the scope where it is evaluated (in-sample, out-of-sample or full-sample):

target:
  metric:
  operation:
  scope:

Steps

Some keys allow the use of variables given in the format $variable_name. Their values are evaluated according to the specified in this section:

steps:
  variable_1:
  variable_2:
  variable_n:

Running the model

Once the YAML file is completed and saved, running the backtests is as simple as writing a single line:

library(traderlab)
run <- run_model("my_model.yaml", preview = TRUE)

If you save your model in a subfolder, ensure you specify the path (e.g. “./models/my_model.yaml”).

There are different functions we can use to analyze and extract insights from the resulting data. We find more suitable to briefly present these functions in the following example.

Example

Here we want to illustrate the analysis capabilities that this package offers. In the next example we will see how to implement a basic moving average crossover strategy as well as the steps to run the model and analyzing its results.

The model file

The following YAML represents a simple two-EMA crossover strategy for Bitcoin at 4-hour bars data:

version: 1

description:
  name: Basic EMA crossover
  symbol: BTCUSDT
  timeframe: 4h
  data: ./datasets/btcusdt_4h.rds

indicators:
  ema1:
    indicator: EMA
    source: close
    periods: $ema1_periods
  ema2:
    indicator: EMA
    source: close
    periods: $ema2_periods

rules:
  entry_signal: ema1 > ema2
  exit_signal: ema1 < ema2
  stop_loss_price: entry_price - 0.1 * entry_price

management:
  position: long
  start_capital: 10000
  interest_type: simple
  reinvest: 1
  leverage: 1
  fee_type: per_trade_pct
  fee: 0.002

periods:
  start: 2018-01-01
  end:
  oos_start: 2023-01-01

target:
  metric: return_dd
  operation: max
  scope: is

steps:
  ema1_periods: [40, 50, 5]
  ema2_periods: [90, 100, 5]
  

Note: if you work with a Unix based system, you might require a newline at the end of your document to avoid an error.

Let’s comment the setup:

The version is not mandatory so far. We are just annotating this for future developments.

The description section indicates that this model is using a 4-hour Bitcoin dataset.

Two indicators are setup. They are two EMAs evaluated at the close for each bar. The periods are not fixed but referenced to two custom variables: $ema1_periods and $ema2_periods.

The rules meet a typical two-EMA crossover strategy. The entry_signal will be TRUE when ema1 crosses above ema2. The exit_signal will be TRUE when ema1 crosses below ema2. A stop loss has also been setup such that the trades will close when the price is a 10% below the entry price. All three rules refer at the bar close.

Concerning the management setup, when the entry signal happens, a long position is open. The start_capital is $10000 and because of the simple interest_type, a fixed amount of capital (as maximum) of 100% * $10000 = $10000 will be available per trade. There is no leverage (1x) and finally, the fee_type is configured as a % per trade which is fixed to 0.2% in the fee key.

The object periods specifies that the dataset starts on 2018-01-01 and the out-of-sample period does on 2023-01-01. The out-of-sample period ends at the last bar of the dataset always.

The section target indicates the metric goal to monitor in the backtesting process. We want to evaluate the max (maximum) return_dd (return / drawdown) on is (in-sample period). This target does not exclude the calculation of other metrics.

In steps, we assign values to the two custom variables defined before. We want ema1_periods to go from 40 to 50 in increments of 5 (i.e. 40, 45, 50). ema2_periods will go from 90 to 100 in increments of 5 (i.e. 90, 95, 100). Note that here, the ‘$’ symbol does not appear anymore.

First checks

One of the things we would like to know before running the model is the number of steps it will take as well as the variable values in each step. In the context of this package, a step can be understand as a single backtest, so our model will run 9 backtests:

get_model_steps("./models/basic_ema_xover.yml")

$n_steps
[1] 9

$steps
  $ema1_periods $ema2_periods
1            40            90
2            45            90
3            50            90
4            40            95
5            45            95
6            50            95
7            40           100
8            45           100
9            50           100

We might also want to see how the indicators look like for some specific step. Plotting the indicators is very useful to verify that they are configured as expected and our strategy setup makes sense. This feature still needs some improvements, but for the moment we can do like this:

library(ggplot2)

plot_trades(model = "./models/basic_ema_xover.yml",
            step = 1,
            from = "2024-01-01",
            to = "2024-03-31",
            show_trades = FALSE) +
    geom_line(aes(x = close_time, y = ema1), color = "steelblue3") +
    geom_line(aes(x = close_time, y = ema2), color = "orange3")

Or perhaps we are more interested in how the strategy signals occur. In this case, our code do not need the traces associated to the indicators:

plot_trades(model = "./models/basic_ema_xover.yml",
            step = 1,
            from = "2023-01-01",
            to = "2024-03-31",
            show_trades = TRUE)

Of course, we can combine signals and indicators in the same plot by switching back show_trades to TRUE and keeping the lines.

We also have the choice of getting a dataframe of this pre-process with the help of our set_indicators() function:

s1 <- set_indicators("./models/basic_ema_xover.yml", step = 1)

set_indicators() returns the OHLCV data from the model decorated with the indicator outputs, entry/exit signals, stop loss level, etc., before fees. This is especially helpful for a first exploratory analysis of our trading idea and for plotting other features in the dataset:

ggplot(s1 |> dplyr::filter(open_time >= "2024-01-01",
                           close_time <= "2024-03-31"),
       aes(x = close_time)) +
  geom_line(aes(y = volume)) +
  theme_bw() +
  labs(x = NULL, y = "Volume")

The backtest

In this section, we perform an actual backtesting with the run_model() function:

run <- run_model("./models/basic_ema_xover.yml", preview = TRUE)

By previewing the results, we will see a plot like in the image below. The upper half is the area for the current step and the lower one applies for the best step found so far (according to the target defined in the model).

Two plots are shown by row. The left one is the balance (the cumulative results + the capital) and the right one is the percentage return per year. Giving these two plots we can have a good idea about the performance before a deeper analysis.

Note: an issue is known for the previewing feature. If the plot pane (in RStudio) has not enough area to fit the plots, a series of warnings related to graphics state are expected. This does not have an effect on the execution nor the stored data.

With the previous variable assignment, run will keep a structure with some data that we can reuse later:

• data: string with the path to the dataset.
• model: string with the path to the YAML file.
• target: dataframe with the target function and scope.
• step: the step from set of steps that the target is met.
• variables: dataframe with the variable values at such step.
• metrics: dataframe comparing the metrics per scope at such step.

We also have the choice of making no variable assignment by simply running run_model("./models/basic_ema_xover.yml"), but our recommendation is to make a variable assignment to keep reference values.

Exploratory analysis

After running the model, we can obtain very general information in terms of timings and signals for a first contact with the results:

The following code computes the timings from the stored balances:

balances() |>
    timings()

The output shows information about the total time covered by the dataset and the time per scope. In the example, the total duration of the dataset is about 6 years and 3 months. The in-sample period has a duration of 5 years and represents almost an 80% of the data. The out-of-sample period has a duration of 1 year and 3 months, and represents a 20% of the data:

$total_time
                start                 end            duration
1 2018-01-01 04:59:59 2024-04-01 01:59:59 6y 3m -1d 21H 0M 0S

$scope_time
  scope               start                 end            duration  bars         p
1    is 2018-01-01 04:59:59 2023-01-01 00:59:59 5y 0m -1d 20H 0M 0S 10940 0.7999415
2   oos 2023-01-01 04:59:59 2024-04-01 01:59:59 1y 3m -1d 21H 0M 0S  2736 0.2000585

The next commands shows information about the signals. The signals() function requires the number of step this time. The variable assignment from the run_model() is useful here. Since we are interested in the best step from the backtesting execution, we will get it by simply calling run$step:

balances() |>
  signals(run$step)

Specifically, this step has 61 total entries and 60 exits (the last trade is not closed in the chosen period). We also obtain the number of entries/exits per scope, as well as its proportion (82% for in-sample, 18% for out-of-sample). Other information such exposure (52% in this example) and average trade duration (2.7 weeks) are available:

$total_entries
[1] 61

$total_exits
[1] 60

$scope
  scope entries exits p_entries   p_exits
1    is      49    49 0.8032787 0.8166667
2   oos      12    11 0.1967213 0.1833333

$exposure
   bars trading p_trading
1 13676    7209 0.5271278

$avg_bars
[1] 114.9893

$avg_duration
[1] "1655845.43828265s (~2.74 weeks)"

If we move to a more performance related results, the most immediate action is to read the data kept in the run variable. By simply typing run, an interesting summary is returned. Now we are interested in the metrics report and the values of the variables at this step:

$variables
      variable value
1 ema1_periods    45
2 ema2_periods    95

$metrics
              metric           is           oos         full
1             trades  49.00000000   11.00000000  60.00000000
2         pct_return   3.02195212    1.18380896   4.20576108
3               cagr   0.32098662    0.86968194   0.30217963
4           win_rate   0.28571429    0.45454545   0.31666667
5      profit_factor   2.74150157    5.81593220   3.12297708
6           wl_ratio   0.40000000    0.83333333   0.46341463
7           rr_ratio   3.53155523    2.87923985   3.36847639
8           avg_bars 109.02040816  150.50000000 117.18032787
9           exposure   0.49277879    0.66447368   0.52712782
10        expectancy 616.72492249 1076.18996173 700.96017968
11   max_consec_wins   3.00000000    2.00000000   3.00000000
12 max_consec_losses   7.00000000    3.00000000   7.00000000
13   risk_adj_return   6.13247193    1.78157388   7.97863622
14            pct_dd  -0.42532318   -0.09689864  -0.42532318
15         return_dd   7.02247107    9.48943890   9.77342933
16           cagr_dd   0.75468876    8.97517206   0.71047063
17      sharpe_ratio   0.57773545    1.07703944   0.52590032
18         r_squared   0.85038789    0.30889049   0.86246072
19           k_ratio   0.33771018    0.21260849   0.32100899
20               sqn   1.41034537    1.70042654   1.82885513
21               var  -0.09991205   -0.07203091  -0.07103929
22              cvar  -0.11305337   -0.07327635  -0.10748702

There are other functions that allow exploring the balances, variables and metrics obtained from the model execution. If we are not satisfied with the results for the target metrics, we can explore other metrics.

In the following example we filter the dataset returned by metrics(). We are interested in the in-sample period and only the steps that meet a return / drawdown greater than 5. We reshape the result and sort in descending mode by Sharpe ratio. If we printed at this point, we would see that the best step is the number 6, so the next function filters by 6 and finally we print the scope comparison:

metrics() |>
    filter_is() |>
    filter_metrics(return_dd > 5) |>
    pivot_metrics() |>
    sort_metrics(-sharpe_ratio) |>
    filter_step(6) |>
    compare_scopes() |>
    print(n = 25)

If this resulted in better metrics for us, we could get the variable configuration as follows:

variables() |>
    filter_step(6)

There is much more to explore with the functions provided by the package, but going deeper in this aspect is not the purpose of this example nor document.

Performance plots

Printing metrics is essential as it is the way of analyzing our strategy with numbers, but making visuals of the performance is also necessary.

Balance and drawdown

The first plot one can expect is the balance as shown in the previewing. The plot_balance() function offers this and adds drawdown subplots:

plot_balance(step = run$step)

The out-of-sample period is shadowed and two drawdown subplots are shown. We are usually interested in percentage terms, but in this plot we have available the drawdown in monetary terms as well. This double representation of loss helps us to understand the risk of the strategy and allows us keeping focus on the money we would lose.

Returns

Once known the balance curve and drawdown, we might be curious about the individual returns, their distribution and how they accumulate in monthly and annual terms. This information is given by the plot_returns() function:

plot_returns(step = run$step)

Durations

An strategy performance must also consider timing aspects as the duration of the trades. This is just what plot_durations() gives us:

plot_durations(step = run$step)

The durations are given in number of bars so we must multiply by the timeframe if we want them in terms of time.

Benchmark

Making profitable strategies with low drawdowns is our target, but at the end of the day, our strategies must also beat certain benchmarks. One simple benchmark is the buy and hold strategy and what we must question is if our trading strategy is better than that, meaning that our effort to design and analyze strategies makes sense. The plot_benchmark() function shows us a comparison:

plot_benchmark(step = run$step)

The two strategies are compared in the same plot as they are represented in percentage terms. Additional information is useful here like the portion of time that the trading strategy is above the buy and hold strategy, the correlation of both series and the correlation in annual return terms.

Excursions

An interesting and important trading performance study is the analysis of the excursions per trade so we can basically see what loss assume each winning and losing trade. We can access this information with the plot_excursions() function, which returns a compound plot with different information about the % price excursions from the entry price:

plot_excursions(step = run$step)

Robustness

traderlab offers a robustness test based on Monte Carlo simulations with the plot_monte_carlo() function. This function allows different configurations that helps us to determine if our strategy is robust enough:

plot_monte_carlo(step = run$step, scope = "oos", samples = 1000)

For a series of relevant performance metrics, the values for in-sample and out-of-sample are compared. The metrics distribution as a result of the Monte Carlo simulations and its average are also shown. In addition, in the console are printed the simulated values for different confidence intervals.

Giving a training about this test and plot is out of the scope of this document.

List of indicators

This is the list of available indicators and their required parameters:

Absolute Range (AR)

• Output: high - low

Example:

indicators:
  ar:
    indicator: AR

Aroon

• Parameter:
  • periods
• Outputs:
  • up
  • dn
  • osc: up - dn
• Range:
  • up: [0, 100]
  • dn: [0, 100]
  • osc: [-100, 100]

Example:

indicators:
  aroon:
    indicator: Aroon
    periods: 6

Average Directional Movement Index (ADX)

• Parameter:
  • periods
• Outputs:
  • dip: positive direction index
  • din: negative direction index
  • dx: direction index
  • adx: average direction index (trend strength)
• Range: [0, 100]

Example:

indicators:
  adx:
    indicator: ADX
    periods: 20

Average True Range (ATR)

• Parameter:
  • periods: number of bars to average.
• Output: mean(TR)

Example:

indicators:
  atr:
    indicator: ATR
    periods: 10

Bollinger bands

• Parameters:
  • periods
  • sd
• Outputs:
  • upr = SMA + SD * 2
  • avg = SMA
  • lwr = SMA - SD * 2
  • pct = (close - lwr) / (upr - lwr)

Example:

indicators:
  bol:
    indicator: Bollinger
    periods: 10
    sd: 2

Custom indicator

• Parameter:
  • expression: custom logic

Example:

indicators:
  hl_avg:
    indicator: custom_indicator
    expression: (high + low) / 2

Day of the month

• Parameters:
  • source: open_time or close_time

Example:

indicators:
  mday:
    indicator: day_of_month
    source: close_time

Day of the week

• Parameters:
  • source: open_time or close_time

Example:

indicators:
  wday:
    indicator: day_of_week
    source: close_time

Days to month end

• Parameter:
  • source: open_time or close_time
• Output: days in current month - current day

Example:

indicators:
  dtme:
    indicator: days_to_month_end
    source: close_time

Daily value

• Parameter:
  • source: open, high or low
• Output: current day open, high or low

Example:

indicators:
  daily_high:
    indicator: daily_value
    source: high

Donchian channels

• Parameter:
  • periods
• Outputs:
  • upr
  • avg
  • lwr

Example:

indicators:
  dch:
    indicator: Donchian
    periods: 10

Down in a row

• Parameter:
  • source
  • periods

Example:

indicators:
  dir:
    indicator: down_in_row
    source: close
    periods: 3

Exponential Moving Average (EMA)

• Parameters:
  • source: open, high, low or close
  • periods: number of bars to average

Example:

indicators:
  ema:
    indicator: EMA
    source: close
    periods: 20

Gaussian noise

This is not an indicator, but it is formulated and applied on the OHLCV data in the same way

• Parameters:
  • source
  • pct_dev: deviation from the source value in %

Example:

indicators:
  noisy_close:
    indicator: noise
    source: close
    pct_dev: 0.005

Highest value

• Parameters:
  • source: open, high, low or close
  • periods

Example:

indicators:
  highest:
    indicator: highest_value
    source: close
    periods: 15

Hour

• Parameters:
  • source: open_time or close_time

Example:

indicators:
  hour:
    indicator: hour
    source: close_time

Hours to day end

• Parameter:
  • source: open_time or close_time

Example:

indicators:
  htde:
    indicator: hours_to_day_end
    source: close_time

Hull Moving Average (HMA)

• Parameters:
  • source: open, high, low or close
  • periods: number of bars to average

Example:

indicators:
  hma:
    indicator: HMA
    source: close
    periods: 20

Internal Bar Strength (IBS)

• Output: 100 * (close - low) / (high - low)
• Range: [0, 100]

Example:

indicators:
  ibs:
    indicator: IBS

Keltner channels

• Parameters:
  • periods
  • atr_periods
• Outputs:
  • upr
  • avg
  • lwr

Example:

indicators:
  klt:
    indicator: Keltner
    periods: 20
    atr_periods: 2

Lag

• Parameters:
  • source
  • periods

Example:

indicators:
  close1:
    indicator: lag_indicator
    source: close
    periods: 1

Linear regression

• Parameters:
  • source: open, high, low or close
  • periods
• Outputs:
  • fit = OLS fitting
  • lwr = lower limit from 95% prediction interval
  • upr = upper limit from 95% prediction interval
  • slope

Example:

indicators:
  reg:
    indicator: linear_regression
    source: close
    periods: 20

Lowest value

• Parameters:
  • source: open, high, low or close
  • periods

Example:

indicators:
  lowest:
    indicator: lowest_value
    source: close
    periods: 15

Month

• Parameter:
  • source: open_time or close_time

Example:

indicators:
  month:
    indicator: month
    source: close_time

Moving Average Convergence Divergence (MACD)

• Parameters:
  • fast_periods
  • slow_periods
  • signal_periods
• Outputs:
  • macd
  • signal
• Range: [-n, n]

Example:

indicators:
  macd:
    indicator: MACD
    fast_periods: 5
    slow_periods: 10
    signal_periods: 10

Number of bars

Example:

indicators:
  nbars:
    indicator: n_bars

Percentage Price Oscillator (PPO)

• Parameters:
  • fast_periods
  • slow_periods
  • signal_periods
• Outputs:
  • ppo: 100 * (fast EMA - slow EMA) / slow EMA
  • hist: PPO - signal EMA
• Range: [-n, n]

Example:

indicators:
  ppo:
    indicator: PPO
    source: close
    fast_periods: 5
    slow_periods: 10
    signal_periods: 10

Pivot points

• Outputs:
  • p = (high + low + close) / 3
  • r1 = (p * 2) - low
  • r2 = p + (high - low)
  • s1 = (p * 2) - high
  • s2 = p - (high - low)

Example:

indicators:
  pvts:
    indicator: pivot_points

Range highest value

• Parameters:
  • source: open, high, low or close
  • start: start hour 0-23
  • end: end hour 0-23

Example:

indicators:
  rhv:
    indicator: range_highest_value
    source: close
    start: 0
    end: 14

Range lowest value

• Parameters:
  • source: open, high, low or close
  • start: start hour 0-23
  • end: end hour 0-23

Example:

indicators:
  rlv:
    indicator: range_lowest_value
    source: close
    start: 0
    end: 14

Relative Strength Indicator (RSI)

• Parameters:
  • source: open, high, low or close
  • periods
• Output: 100 - 100 / (1 + (average wins / average losses))
• Range: [0, 100]

Example:

indicators:
  rsi:
    indicator: RSI
    source: close
    periods: 20

Simple Moving Average (SMA)

• Parameters:
  • source: open, high, low or close
  • periods

Example:

indicators:
  sma:
    indicator: SMA
    source: close
    periods: 20

Stochastic oscillator

• Parameters:
  • source: if not provided, source = HLC
  • fast_k_periods
  • fast_d_periods
  • slow_d_periods
• Outputs:
  • fastK
  • fastD
  • slowD
• Range: [0, 1]

Example:

indicators:
  stoch:
    indicator: stochastic
    fast_k_periods: 13
    fast_d_periods: 6
    slow_d_periods: 2

True Range (TR)

• Output: max(high - low, high - close[-1], close[-1] - low)

Example:

indicators:
  tr:
    indicator: TR

Up in a row

• Parameter:
  • source
  • periods

Example:

indicators:
  uir:
    indicator: up_in_row
    source: close
    periods: 3

Volume-Weighted Average Price (VWAP)

• Parameters:
  • price_source: open, high, low or close
  • volume_source: volume
  • periods: number of bars to average

Example:

indicators:
  vwap:
    indicator: VWAP
    price_source: close
    volume_source: volume
    periods: 20

Williams %R

• Parameter:
  • periods
• Output: ((highest high - close) / (highest high - lowest low)) * (- 100)
• Range: [-100, 0]

Example:

indicators:
  wpr:
    indicator: WPR
    periods: 20

Metrics

Number of trades

• Name: trades
• Output: number of closed trades

Return %

• Name: pct_return
• Output: (final balance - initial balance) / initial balance
• Range: [0, 1]

Compound Annual Growth Rate (CAGR)

• Name: cagr
• Output: (final balance / initial balance)^(1 / years) - 1

Win rate

• Name: win_rate
• Output: winning trades / total trades
• Range: [0, 1]

Profit factor

• Name: profit_factor
• Output: sum(positive returns) / |sum(negative returns)|

Wins-to-Losses ratio

• Name: wl_ratio
• Output: number of positive returns / number of negative returns

Risk-to-Reward ratio

• Name: rr_ratio
• Output: mean(r_multiples), where r_multiples = (target price - entry price) / (entry price - stop loss price)

Stop loss is required to compute this metric.

Average bars

• Name: avg_bars
• Output: mean(trade durations)

Exposure

• Name: exposure
• Output: number of trading bars / total bars

Expectancy

• Name: expectancy
• Output: win % * mean(positive returns) - loss % * |mean(negative returns)|

Maximum consecutive wins

• Name: max_consec_wins
• Output: number of consecutive trades with positive return

Maximum consecutive losses

• Name: max_consec_losses
• Output: number of consecutive trades with negative return

Expected annual return

• Name: exp_a_ret
• Output: mean(annual returns)

Standard deviation of annual returns

• Name: std_a_ret
• Output: sd(annual returns)

Risk-adjusted return

• Name: risk_adj_return
• Output: total % return / exposure

Maximum drawdown %

• Name: pct_dd
• Output: max(drawdown)
• Range: [-1, 0]

Return / drawdown

• Name: return_dd
• Output: (final balance - initial balance) / max(drawdown)

CAGR / drawdown

• Name: cagr_dd
• Output: CAGR / max(drawdown)

Sharpe ratio (annual and 5% risk-free rate benchmark)

• Name: sharpe_ratio
• Output: (mean(annual returns) - risk-free rate) / sd(annual returns)

R²

• Name: r_squared
• Output: R² statistic from linear regression
• Range: [-1, 1]

K-ratio

• Name: k_ratio
• Output: slope of regression line / (standard error of slope * total bars)

System Quality Number (SQN)

• Name: sqn
• Output: (mean(r multiples) / sd(r multiples)) * sqrt(if(number of trades > 100, number of trades, 100), where r_multiples = (target price - entry price) / (entry price - stop loss price)

Stop loss is required to compute this metric.

Monthly Value-at-Risk

• Name: var
• Output: 5th percentile of monthly returns

Monthly Conditional Value-at-Risk

• Name: cvar
• Output: mean under 5th percentile of monthly returns

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