PINE LIBRARY
已更新 AGbayLIB
Library "AGbayLIB"
Changes the timeframe period to the given period and returns the data matrix[cyear, cmonth, cday, chour, cminute_, csecond, cfulltime, copen, cclose, chigh, clow, cvolume] and sets the timeframe to the active time period
getTimeFrameValues(active_period_, period_, max_bars_)
: add function description here
Parameters:
active_period_ (string): Current time frame period to be set after getting period_ data
period_ (string): Target time period for returning data
max_bars_ (int): The historical bar count to be get
Returns: An array of data_row type with size of max_bars_ which includes rows of data: [year, month, day, hour, minute_, second, fulltime, open, close, high, clow, volume]
data_row
Fields:
year (series__integer)
month (series__integer)
day (series__integer)
hour (series__integer)
minute (series__integer)
second (series__integer)
fulltime (series__string)
open (series__float)
close (series__float)
high (series__float)
low (series__float)
volume (series__float)
Changes the timeframe period to the given period and returns the data matrix[cyear, cmonth, cday, chour, cminute_, csecond, cfulltime, copen, cclose, chigh, clow, cvolume] and sets the timeframe to the active time period
getTimeFrameValues(active_period_, period_, max_bars_)
: add function description here
Parameters:
active_period_ (string): Current time frame period to be set after getting period_ data
period_ (string): Target time period for returning data
max_bars_ (int): The historical bar count to be get
Returns: An array of data_row type with size of max_bars_ which includes rows of data: [year, month, day, hour, minute_, second, fulltime, open, close, high, clow, volume]
data_row
Fields:
year (series__integer)
month (series__integer)
day (series__integer)
hour (series__integer)
minute (series__integer)
second (series__integer)
fulltime (series__string)
open (series__float)
close (series__float)
high (series__float)
low (series__float)
volume (series__float)
版本注释
Unused import removed版本注释
v3Added:
data_set
Fields:
symbol (series__string)
time_period (series__string)
count (series__integer)
records (array__|data_row|#OBJ)
Updated:
getTimeFrameValues(symbol, period, max_bars, opens, closes, highs, lows, volumes, times)
: Creates an data_set typed object, copies open,close,high,low,volume,time data into records and also calculates trends of records
Parameters:
symbol (string): Symbol
period (string): Target time period for returning data
max_bars (int): The historical bar count to be get
opens (float): The historical bars of open data
closes (float): The historical bars of open data
highs (float): The historical bars of open data
lows (float): The historical bars of open data
volumes (float): The historical bars of open data
times (int): The historical bars of open data
Returns: An data_set object which contains array of data_row type which includes [timestamp, year, month, day, hour, minute, second, fulltime, open, close, high, clow, volume, trend]
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v18Updated:
data_row
Contains candle values
Fields:
timestamp (series int): Time value of the candle
year (series int): Extracted year value from time
month (series int): Extracted month value from time
day (series int): Extracted day value from time
hour (series int): Extracted hour value from time
minute (series int): Extracted minute value from time
second (series int): Extracted second value from time
fulltime (series string)
open (series float): Open value of candle
close (series float): Close value of candle
high (series float): High value of candle
low (series float): Low value of candle
volume (series float): Volume value of candle
trend (series int): Calculated trend value of candle
trend_count (series int): Calculated trending candle count of active candle
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v20Added:
agSetting
Fields:
symbol (series__string)
period (series__string)
iperiod (series__integer)
max_bar_count (series__integer)
min_trend_count (series__integer)
tenkansen_count (series__integer)
kijunsen_count (series__integer)
agCandle
Contains candle values
Fields:
timestamp (series int): Time value of the candle
year (series int): Extracted year value from time
month (series int): Extracted month value from time
day (series int): Extracted day value from time
dayofweek (series int)
hour (series int): Extracted hour value from time
minute (series int): Extracted minute value from time
second (series int): Extracted second value from time
fulltime (series string)
barindex (series int)
open (series float): Open value of candle
close (series float): Close value of candle
high (series float): High value of candle
low (series float): Low value of candle
volume (series float): Volume value of candle
resistantance (series bool)
supply (series bool)
trend (series int): Calculated trend value of candle
trend_count (series int): Calculated trending candle count of active candle
agZigZagNode
Fields:
candle (|agCandle|#OBJ)
candle_index (series__integer)
trend (series__integer)
pinnedCandle (|agCandle|#OBJ)
pinned_candle_index (series__integer)
agSymbolCandles
Fields:
setting (|agSetting|#OBJ)
count (series__integer)
candles (array__|agCandle|#OBJ)
zigzag_nodes (array__|agZigZagNode|#OBJ)
Updated:
getTimeFrameValues(setting, opens, closes, highs, lows, volumes, times, bar_indexes)
: Creates an data_set typed object, copies open,close,high,low,volume,time data into records and also calculates trends of records
Parameters:
setting (agSetting)
opens (float): The historical bars of open data
closes (float): The historical bars of open data
highs (float): The historical bars of open data
lows (float): The historical bars of open data
volumes (float): The historical bars of open data
times (int): The historical bars of open data
bar_indexes (int)
Returns: An agSymbolCandles object which contains array of agCandles type which includes [timestamp, year, month, day, hour, minute, second, fulltime, open, close, high, clow, volume, trend]
Removed:
data_row
Contains candle values
data_set
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v40Added:
isBearishBullish(setting, copen, cclose, clow, chigh)
: Searches bearish and bullish order blocks
Parameters:
setting (agSetting): setting parameters for calculation
copen (float)
cclose (float)
clow (float)
chigh (float)
Returns: [bullishOB, bearishOB, OB_bull, OB_bull_chigh, OB_bull_clow, OB_bull_avg, OB_bear, OB_bear_chigh, OB_bear_clow,OB_bear_avg] Tuple
Updated:
agCandle
Contains candle values
Fields:
timestamp (series int): Time value of the candle
year (series int): Extracted year value from time
month (series int): Extracted month value from time
day (series int): Extracted day value from time
dayofweek (series int)
hour (series int): Extracted hour value from time
minute (series int): Extracted minute value from time
second (series int): Extracted second value from time
fulltime (series string)
barindex (series int)
open (series float): Open value of candle
close (series float): Close value of candle
high (series float): High value of candle
low (series float): Low value of candle
volume (series float): Volume value of candle
resistantance (series bool)
supply (series bool)
trend (series int): Calculated trend value of candle
trend_count (series int): Calculated trending candle count of active candle
is_order (series bool)
is_white (series bool)
is_bullish (series bool)
Pine脚本库
In true TradingView spirit, the author has published this Pine code as an open-source library so that other Pine programmers from our community can reuse it. Cheers to the author! You may use this library privately or in other open-source publications, but reuse of this code in publications is governed by House Rules.
免责声明
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.
Pine脚本库
In true TradingView spirit, the author has published this Pine code as an open-source library so that other Pine programmers from our community can reuse it. Cheers to the author! You may use this library privately or in other open-source publications, but reuse of this code in publications is governed by House Rules.
免责声明
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.