Price and Volume functions

price(int offset) : var

priceO(int offset) : var

priceC(int offset) : var

priceH(int offset) : var

priceL(int offset) : var

priceReal() : var

marketVal(int offset) : var

marketVol(int offset) : var

Parameters:

Returns:

seriesO(): vars

seriesH(): vars

seriesL(): vars

seriesC(): vars

priceSet (int offset, var Open, var High, var Low, var Close)

priceQuote (var Timestamp, var Quote) : int

Parameters:

Returns:

priceRecord ()

Remarks:

• The priceO, priceH, priceL, priceC functions can be also used under the names priceOpen, priceHigh, priceLow, priceClose.
• At the initial run of the strategy before an asset is selected, all price functions return 0. After switching to a different asset, the price and market functions automatically change to the prices and market data of that asset. In backtesting or training prices are affected by Detrend flags.
• If not otherwise mentioned, all prices are ask prices. This way stops, entry limits, or profit targets can be calculated without a distinction between long and short trades. Zorro automatically handles conversion from Ask to Bid when entering or exiting trades, calculating trade profit, or setting limit levels. For getting the bid price, subtract Spread; for getting the pip value of a price, divide by PIP. The current bid/ask mean price is priceClose() - 0.5*Spread. More variants of prices - i.e. the Center Price, the Typical Price, the Haiken Ashi Price, the VWAP etc. - can be found on the indicators page. Live prices can be switched between different types (f.i. quotes and trades) with the SET_PRICETYPE command when supported by the broker.
• For changing the price range of an asset, f.i. for shifting negative prices to the positive scale, use PriceOffset.
• When loading price data, the prices are checked for plausibility dependent on the Outlier parameter. Invalid prices, such as negative values or outliers, are automatically corrected or removed. Setting Detrend = NOPRICE before calling asset() prevents that asset and price data is checked and invalid prices are removed.
• When connected to a broker or exchange, prices are updated at any incoming tick; when connected to an online price source, at any bar. Outliers and stock splits are detected and can be evaluated with the PriceJump variable. Outliers are removed, splits are logged.
• If the LEAN flag is set and M1 historical data is used, open and close prices of a bar are not explicitely stored for preserving memory. They are derived from the mean prices of its first and last M1 tick.
• If LookBack is not explicitely set, it is automatically expanded to the biggest offset value. If offset is higher than the current bar number (Bar), the price of the first bar is returned.
• When using multiple time frames in the strategy (TimeFrame variable), the offset parameter gives the number of time frames rather than the number of bars. The function priceOpen returns the price from the begin of the time frame, priceClose from its end, priceHigh, priceLow the maximum and minimum price within the time frame, and price the current average of the time frame. Synchronizing time frames (TimeFrame = 0) to certain events or dates is not supported by price functions; use price series for that when required.
• In live trading, marketVal and marketVol return spread and volume from the broker API, when available. If volume is not available, a proxy is returned, such as the tick frequency. The type of returned volume is described on the specific broker page (f.i. IB).Some broker APIs support the SET_VOLTYPE command for setting up the volume source between ask/bid or trade volumes. Historical volume is often the sum of quote sizes or trade volumes of the current minute or current day. Live volume is often the accumulated trade volume of the current day or trading session. The functions return the current value when Offset is 0, otherwise the average of the time frame. Depending on which sort of raw volume they return, it can be converted by script to the total volume per time frame. For instance, to convert accumulated volume to per-minute volume, subtract the volume from 1 bar ago from the current volume, and divide by BarPeriod. See examples below.
    In test and train modes, marketVol returns the fVol element of .t6 historical data, or otherwise 0. The marketVal function returns the fVal element of .t6 data, and the ask-bid spread of .t1 or .t2 data, otherwise 0.
•  Depending on the broker, live volume can be different to historical volume where delayed transactions, busted trades, combos and unreportable trades are filtered out. Likewise, outliers are often filtered out of historical price data. Trade signals should therefore not rely on comparing live volume with historical volume or live volatility with historical volatility.
• For basket trading, artificial assets can be created by combining the prices from several real assets. An example can be found under Tips & Tricks.
• For evaluating live or historical order book data or BBO data, and for generating order flow profiles, use the orderUpdate function.
• For calculating VWAP prices from a given time period, store prices and volumes in series and apply the VWAV function.
• If the PEEK flag is set, price functions can peek into the future by using negative offsets. This is often used for training machine learning algorithms (see advise) with historical price data. If PEEK mode is not set, negative offsets will generate an error message.
• In a TMF or tick function, priceC(0) returns the last price quote, updated every live or historical tick when new price data becomes available. marketVal(0) returns the last ask-bid spread and marketVol(0) returns the last trade or quote volume if available.
• If .t1 data contains both ask and bid quotes, marketVal returns the recent ask-bid spread. In .t6 data it returns the fVal value. The command if(Test) Spread = max(0,marketVal()); can be used for backtesting with variable spread when it is available in the historical .t1, .t2, or .t6 data.
• An example of using the priceQuote function for simulating a HFT system can be found on Financial Hacker.

Examples:

BarPeriod = 60; // set up 1 hour bars (60 minutes)

TimeFrame = 4;
asset("EUR/USD");
vars EUR4Hs = series(price(0));	// create a series of 4 hour EUR mean prices
...
TimeFrame = frameSync(24);
asset("SPX500");
vars SPXDays = series(priceC(0));	// create a series of daily S&P 500 close prices
TimeFrame = 1;
...

// plot a daily spread histogram
void run()
{
  BarPeriod = 10;
  PlotLabels = 4;
  plotBar("Spread",
    (60*hour(0)+minute(0))/BarPeriod,tod(0),marketVal(0),AVG,RED);
}

// convert accumulated daily trade volume to per-bar volume
var volumeDiff(var* Buffer,var Volume)
{
// Buffer[1] = previous accumulated volume
  if(Buffer[1] > Volume) // day change? Reset volume
    Buffer[1] = 0;
  Buffer[0] = Volume-Buffer[1]; // per-bar volume difference
  Buffer[1] = Volume;
  return Buffer[0]; 
}

void run()
{
  BarPeriod = 1;
  LookBack = 0;
  
  assetList("AssetsIB");
  brokerCommand(SET_PRICETYPE,2); // trade prices
  brokerCommand(SET_VOLTYPE,4); // trade volume
  
  vars VBuffer = series(0,-2);
  var Volume = marketVol(0);
  if(Live) // if marketVol is daily accumulated volume
    Volume = volumeDiff(VBuffer,Volume);
  printf("# Vol %.0f",Volume);
}	

See also: