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This Service, export the account Orders/History Orders/Deals/Position on each new change. It save at a common folder with path = <DATE>\\<Server Name>.<Login>.<DATE>.<filetype>.csv Each file type have an header You can use it to receive OMS changes and source SQL Servers outside metatrader with bash scripts or others tools, each time the file change you can read it and get new updates, it works using SharedRead option when opening/flock the file
This indicator represent option and basis values as a tax, using this formula: Tax = ( ( strike / ( basis - option ) ) - 1 ) * 100 It's not time based (don't use time to expire information) You can set a fixed strike value, and a fixed basis symbol if needed. Some exchanges/brokers don't provide this information at marketdata and that's the only method to use this information when not provided
This script use DIDI+BB indicators and display alerts, send mail, send notifications, and play sounds. Scanner can monitor market watch using ***MARKET*** as symbol name, with all timeframes Inputs: media_lenta=3 media_base=8 media_longa=20 bb_periodo=20 bb_shift=0 bb_deviation=1.96 symbols - Symbol list, separated by ; when using ***MARKET*** symbols from marketwatch will be included wait_interval_between_signals - Seconds between signals, default =60, but high
A useful feature may be to determine the normal frequency of round-sized trades, and monitor deviations from that expected value. The ML algorithm could, for example, determine if a larger-than-usual proportion of round-sized trades is associated with trends, as human traders tend to bet with a fundamental view, belief, or conviction. Conversely, a lower-than-usual proportion of round-sized trades may increase the likelihood that prices will move sideways, as silicon traders do not typically
Easley et al. study the frequency of trades per trade size, and find that trades with round sizes are abnormally frequent. For example, the frequency rates quickly decay as a function of trade size, with the exception of round trade sizes {5, 10, 20, 25, 50, 100, 200,…}. These authors attribute this phenomenon to socalled “mouse” or “GUI” traders, that is, human traders who send orders by clicking buttons on a GUI (Graphical User Interface). In the case of the E-mini S&P 500, for example
Easley et al. [2011] proposed a high-frequency estimate of PIN, which they named volume-synchronized probability of informed trading (VPIN). This procedure adopts a volume clock, which synchronizes the data sampling with market activity. For additional details and case studies of VPIN, see Easley et al. [2013]. A number of studies have found that VPIN indeed has predictive power: Abad and Yague [2012], Bethel et al. [2012], Cheung et al. [2015], Kim et al. [2014], Song et al. [2014], Van Ness et
Hasbrouck follows up on Kyle’s and Amihud’s ideas, and applies them to estimating the price impact coefficient based on trade-and-quote (TAQ) data. Consistent with most of the literature, Hasbrouck recommends 5-minute time-bars for sampling ticks. This indicator is based on Advances in Financial Machine Learning 2018 - Marcos Lopez de Prado book, Page 289. --- This code can run slow when using big timeframes, it use copytick to get dollar volume, in a next version I will implement a
This indicator implements the Corwin and Schultz Estimator based on Advances in Financial Machine Learning 2018 - Marcos Lopez de Prado, page 284. It's a spread estimator using high/low and a rolling window (configurable). It works with low liquidity markets as a robust estimator. It's interesting when you need to track spread changes, more information can be found at Marcos book
Amihud studies the positive relationship between absolute returns and illiquidity. In particular, he computes the daily price response associated with one dollar of trading volume, and argues its value is a proxy of price impact. Despite its apparent simplicity, Hasbrouck [2009] found that daily Amihud’s lambda estimates exhibit a high rank correlation to intraday estimates of effective spread. This indicator is based on Advances in Financial Machine Learning 2018 - Marcos Lopez de
In Kyle’s model, the variable 𝜆 captures price impact. Illiquidity increases with uncertainty about v and decreases with the amount of noise Kyle argues that the market maker must find an equilibrium between profit maximization and market efficiency. This indicator is based on Advances in Financial Machine Learning 2018 - Marcos Lopez de Prado book, Page 288
This indicator implements the Rool Model Estimator based on Advances in Financial Machine Learning 2018 - Marcos Lopez de Prado, page 283. It's a spread estimator using trade by trade (copytick) values from last trades (configurable). It works with low liquidity markets as a robust estimator considering the covariance between price diferences. It's interesting when you need to track spread changes
This indicator implements the High Low Volatility Estimator based on Advances in Financial Machine Learning 2018 - Marcos Lopez de Prado, page 284. It's a robust estimator using High/Low values from past bars (configurable). It's robusto to Gaps (open/close of days to stock markets) since it's using high/low ration and not the close price. It's interesting when you need to avoid trading at a specific level of volatility
This indicator get all events/countries/values from metatrader terminal calendar and print veritical lines at chart, you can configure anything with inputs (string to filter names/url/code/countries/currencies), select period to filter, nws interval before and after current time (every information about time is seconds based), you can filter importance, type, sector, unit, multiplier, impact, timemode, frequency, forecast/previous % increase/decrease interval, actual/previous % increase/decrease
This indicator works in Brazil market, it should be used on options symbols. the indicator create 8 buffers, Delta, Gamma, Vega, Rho, Theta, Time(working days), Implied Volatility, R (interest rate), and can be used to view greeks over time. It shouldn't be used to trade, since it's cpu intensive, it's just a historic viewer, you can see what happened with greeks and teach friends :)
It's a simple indicator that execute GARCH(1,1) function, it can be used with basis stocks and options, when garch is bellow implied volatility probably implied volatility tend to goes down, and when garch is higher than implied volatility probably implied volatility goes up. Can be used with options trading when buying / selling volatility, for example, buy ATM put and call when garch is higher than implied volatility and sell when it's lower
From https://en.wikipedia.org/wiki/Fractional_calculus: Fractional calculus is a branch of mathematical analysis that studies the several different possibilities of defining real number powers or complex number powers of the differentiation operator D This indicator execute fracional differentiation on data, you can select the differentiation D value and execute some tweaks like execute Log(value) before difefrentiation or execute
Export Deals to CSV - Excell File location: HistoryDeals/<SERVER>/<LOGIN>.<DATETIME>.csv Works as an Script, reading all DEALS in history and exporting it as a plain text CSV file There's no option to change. There's no WebRequest. There's no DLL use. I need to write 300 characters to allow this product being exposed at MQL5 market place. It's simple to use, plug it in graph and wait completion