Signature | Description | Parameters |
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#include <DataFrame/DataFrameFinancialVisitors.h> template<typename T, typename I = unsigned long, std::size_t A = 0> struct BollingerBand; // ------------------------------------- template<typename T, typename I = unsigned long, std::size_t A = 0> using bband_v = BollingerBand<T, I, A>; |
This is a “single action visitor”, meaning it is passed the whole data vector in one call and you must use the single_act_visit() interface. This functor class calculates Bollinger bands. A Bollinger Band® is a technical analysis tool defined by a set of trendlines. They are plotted as two standard deviations, both positively and negatively, away from a simple moving average (SMA) of a security's price and can be adjusted to user preferences. Bollinger Bands® was developed by technical trader John Bollinger and designed to give investors a higher probability of identifying when an asset is oversold or overbought. The constructor takes:
BollingerBand(double upper_band_multiplier, double lower_band_multiplier, std::size_t moving_mean_period, bool biased = false)There are 2 methods that give you the results:
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T: Column data type I: Index type A: Memory alignment boundary for vectors. Default is system default alignment |
#include <DataFrame/DataFrameFinancialVisitors.h> template<typename T, typename I = unsigned long, std::size_t A = 0> struct AccelerationBandsVisitor; // ------------------------------------- template<typename T, typename I = unsigned long, std::size_t A = 0> using aband_v = AccelerationBandsVisitor<T, I, A>; |
This is a “single action visitor”, meaning it is passed the whole data vector in one call and you must use the single_act_visit() interface. This visitor calculates the Acceleration Bands indicators. It requires 3 input columns in the order of close, high, low. The Acceleration Bands System was introduced in 2002 by Price Headley. The concept is based on the idea of getting into a trade just as the security is trending but before its price moves heavily in one direction or another. The Acceleration Bands measure volatility over a user-defined number of bars (default is often the past 20 bars). They are plotted using a simple moving average as the midpoint, with the upper and lower bands being of equal distance from the midpoint, similar to Bollinger Bands. explicit AccelerationBandsVisitor(size_t roll_period = 20, double multiplier = 4); roll_period The averaging period multiplier Applied to high/low ratioget_upper_band() returns the upper band vector get_result() returns the mid band vector get_lower_band() returns the lower band vector |
T: Column data type I: Index type A: Memory alignment boundary for vectors. Default is system default alignment |
static void test_BollingerBand() { std::cout << "\nTesting BollingerBand{ } ..." << std::endl; MyDataFrame::set_thread_level(10); std::vector<unsigned long> idx = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 }; std::vector<double> d1 = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 19, 18, 17, 17, 16, 15, 14, 13, 14, 13, 12, 11, 12, 10, 9, 8, 7, 6, 7, 5 }; MyDataFrame df; df.load_data(std::move(idx), std::make_pair("col_1", d1)); using bollinger_band_t = BollingerBand<double>; bollinger_band_t visitor(2.0, 2.0, 5); df.single_act_visit<double>("col_1", visitor); auto &upper_to_raw = visitor.get_upper_band_to_raw(); auto &raw_to_lower = visitor.get_raw_to_lower_band(); assert(upper_to_raw.size() == 40); assert(std::isnan(upper_to_raw[3])); assert(fabs(upper_to_raw[8] - 1.16228) < 0.00001); assert(fabs(upper_to_raw[12] - 1.16228) < 0.00001); assert(fabs(upper_to_raw[38] - 2.68035) < 0.00001); assert(fabs(upper_to_raw[39] - 3.88035) < 0.00001); assert(raw_to_lower.size() == 40); assert(std::isnan(raw_to_lower[1])); assert(fabs(raw_to_lower[8] - 5.16228) < 0.00001); assert(fabs(raw_to_lower[12] - 5.16228) < 0.00001); assert(fabs(raw_to_lower[38] - 1.88035) < 0.00001); assert(fabs(raw_to_lower[39] - 0.680351) < 0.00001); MyDataFrame::set_thread_level(0); }
// ----------------------------------------------------------------------------- static void test_AccelerationBandsVisitor() { std::cout << "\nTesting AccelerationBandsVisitor{ } ..." << std::endl; StrDataFrame df; try { df.read("data/SHORT_IBM.csv", io_format::csv2); aband_v<double, std::string> aband; df.single_act_visit<double, double, double>("IBM_Close", "IBM_High", "IBM_Low", aband); // Upper-band // assert(aband.get_upper_band().size() == 1721); assert(std::isnan(aband.get_upper_band()[0])); assert(std::isnan(aband.get_upper_band()[18])); assert(std::abs(aband.get_upper_band()[19] - 191.2407) < 0.0001); assert(std::abs(aband.get_upper_band()[25] - 187.2326) < 0.0001); assert(std::abs(aband.get_upper_band()[30] - 185.7256) < 0.0001); assert(std::abs(aband.get_upper_band()[35] - 185.0129) < 0.0001); assert(std::abs(aband.get_upper_band()[1720] - 127.0993) < 0.0001); assert(std::abs(aband.get_upper_band()[1712] - 130.0339) < 0.0001); assert(std::abs(aband.get_upper_band()[1707] - 129.903) < 0.0001); // Mid-band // assert(aband.get_result().size() == 1721); assert(std::isnan(aband.get_result()[0])); assert(std::isnan(aband.get_result()[18])); assert(std::abs(aband.get_result()[19] - 184.436) < 0.0001); assert(std::abs(aband.get_result()[25] - 180.7035) < 0.0001); assert(std::abs(aband.get_result()[30] - 179.142) < 0.0001); assert(std::abs(aband.get_result()[35] - 178.817) < 0.0001); assert(std::abs(aband.get_result()[1720] - 119.8055) < 0.0001); assert(std::abs(aband.get_result()[1712] - 123.058) < 0.0001); assert(std::abs(aband.get_result()[1707] - 122.7085) < 0.0001); // Lower-band // assert(aband.get_lower_band().size() == 1721); assert(std::isnan(aband.get_lower_band()[0])); assert(std::isnan(aband.get_lower_band()[18])); assert(std::abs(aband.get_lower_band()[19] - 177.8282) < 0.0001); assert(std::abs(aband.get_lower_band()[25] - 174.2877) < 0.0001); assert(std::abs(aband.get_lower_band()[30] - 172.3706) < 0.0001); assert(std::abs(aband.get_lower_band()[35] - 172.6929) < 0.0001); assert(std::abs(aband.get_lower_band()[1720] - 113.2393) < 0.0001); assert(std::abs(aband.get_lower_band()[1712] - 116.8539) < 0.0001); assert(std::abs(aband.get_lower_band()[1707] - 116.1055) < 0.0001); } catch (const DataFrameError &ex) { std::cout << ex.what() << std::endl; } }