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snippets/vbt-snippets.md
David Brazda 15439bb98f fix
2024-10-16 12:36:47 +02:00

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import vectorbtpro as vbt
from lightweight_charts import Panel, chart, PlotDFAccessor, PlotSRAccessor
t15data = None

if not hasattr(pd.Series, 'lw'):
    pd.api.extensions.register_series_accessor("lw")(PlotSRAccessor)

if not hasattr(pd.DataFrame, 'lw'):
    pd.api.extensions.register_dataframe_accessor("lw")(PlotDFAccessor)

FETCHING DATA

#fetching from remote db from lib.db import Connection SYMBOL = "BAC" SCHEMA = "ohlcv_1s" #time based 1s other options ohlcv_vol_200 (volume based ohlcv with resolution of 200), ohlcv_renko_20 (renko with 20 bricks size) ... DB = "market_data"

con = Connection(db_name=DB, default_schema=SCHEMA, create_db=True) basic_data = con.pull(symbols=[SYMBOL], schema=SCHEMA,start="2024-08-01", end="2024-08-08", tz_convert='America/New_York')

#Fetching from YAHOO symbols = ["AAPL", "MSFT", "AMZN", "TSLA", "AMD", "NVDA", "SPY", "QQQ", "META", "GOOG"] data = vbt.YFData.pull(symbols, start="2024-09-28", end="now", timeframe="1H", missing_columns="nan")

#endregion

DISCOVERY

#get parameters of method vbt.IF.list_locations() #lists categories vbt.IF.list_indicators(pattern="vbt") #all in category vbt vbt.IF.list_indicators("*sma") vbt.phelp(vbt.indicator("talib:MOM").run)

DATA/WRAPPER

Available methods for data

Main data container (servees as a wrapper for symbol oriented or feature oriented data)

data.transform()
data.dropna()
data.feature_oriented vs data.symbol_oriented #returns True/False if cols are features or symbols
data.data #dictionary either feature oriented or
data.ohlcv #OHLCV mixin filters only ohlcv feature and offers methods http://5.161.179.223:8000/vbt-doc/api/data/base/index.html#vectorbtpro.data.base.OHLCDataMixin
data.base #base mixin - implicit offers functions wrapper methods  http://5.161.179.223:8000/vbt-doc/api/data/base/index.html#vectorbtpro.data.base.BaseDataMixin
    - data.symbol_wrapper
    - data.feature_wrapper
    - data.features


show(t1data.data["BAC"])

#display returns on top of ohlcv
t1data.ohlcv.data["BAC"].lw.plot(left=[(t1data.returns, "returns")], precision=4)

create WRAPPER manually

wrapper methods

#create wrapper from existing objects
wrapper = data.symbol_wrapper # one column for each symbol
wrapper = data.get_symbol_wrapper() # symbol - level, one column for each symbol  (BAC a pod tim series)
wrapper = data.get_feature_wrapper() #feature level, one column for each feature (open,high...)
wrapper = df.vbt.wrapper

#Create an empty array with the same shape, index, and columns as in another array
new_float_df = wrapper.fill(np.nan)  
new_bool_df = wrapper.fill(False)  
new_int_df = wrapper.fill(-1)  

#display df/series
from itables import show
show(t1data.close)

RESAMPLING

config

from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig
from vectorbtpro import _typing as tp
from vectorbtpro.generic import nb as generic_nb

_feature_config: tp.ClassVar[Config] = HybridConfig(
    {
        "buyvolume": dict(
            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
                resampler,
                generic_nb.sum_reduce_nb,
            )
        ),
        "sellvolume": dict(
            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
                resampler,
                generic_nb.sum_reduce_nb,
            )
        ),
        "trades": dict(
            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
                resampler,
                generic_nb.sum_reduce_nb,
            )
        )
    }
)

basic_data._feature_config = _feature_config

ddd #1s to 1T t1data = basic_data'open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume'.resample("1T") t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())

#using resampler (with more control over target index) resampler_s = vbt.Resampler(target_data.index, source_data.index, source_freq="1T", target_freq="1s") basic_data.resample(resampler_s)

REALIGN

REALIGN method - runs on data object (OHLCV) - (open feature realigns leftbound, rest of features rightboud) .resample("1T").first().ffill()

ffill=True = same frequency as t1data.index
ffill=False = keeps original frequency but moved to where data are available ie. instead of 15:30 to 15:44 for 15T bar
t15data_realigned = t15data.realign(t1data.index, ffill=True, freq="1T") #freq - target frequency

REALIGN_CLOSING accessors

t15data_realigned_close = t15data.close.vbt.realign_closing(t1data.index, ffill=True, freq="1T")
t15data_realigned_open = t15data.open.vbt.realign_open(t1data.index, ffill=True, freq="1T")

#realign_closing accessor just calls #return self.realign(*args, source_rbound=False, target_rbound=False, **kwargs) #realign opening #return self.realign(*args, source_rbound=True, target_rbound=True, **kwargs)

#using RESAMPLER #or resampler_s = vbt.Resampler(t15data.index, t1data.index, source_freq="1T", target_freq="1s") t15close_realigned_with_resampler = t1data.data["BAC"].realign_closing(resampler_s)

SIGNALS

cond1 = data.get("Low") < bb.lowerband
#comparing with previous value
cond2 = bandwidth > bandwidth.shift(1)
#comparing with value week ago  
cond2 = bandwidth > bandwidth.vbt.ago("7d")
mask = cond1 & cond2
mask.sum()

ENTRIES/EXITS time based

#create entries/exits based on open of first symbol
entries = pd.DataFrame.vbt.signals.empty_like(data.open.iloc[:,0]) 

#create entries/exits based on symbol level
symbol_wrapper = data.get_symbol_wrapper()
entries = symbol_wrapper.fill(False)
exits = symbol_wrapper.fill(False)

entries.vbt.set(
    True, 
    every="W-MON", 
    at_time="00:00:00",
    indexer_method="bfill",  # this time or after
    inplace=True
)
exits.vbt.set(
    True, 
    every="W-MON", 
    at_time="23:59:59", 
    indexer_method="ffill",  # this time or before
    inplace=True
)

STOPS

doc from_signal

  • StopExitPrice (Which price to use when exiting a position upon a stop signal?)
  • StopEntryPrice (Which price to use as an initial stop price?)

price = close.vbt.wrapper.fill() price[entries] = entry_price price[exits] = exit_price

OHLCSTX modul

  • exit signal generator based on price and stop values doc

WINDOW OPEN/CLOSE

END OF DAY EXITS

sr = t1data.data["BAC"]
last_n_daily_rows = sr.groupby(sr.index.date).tail(4) #or N last rows
second_last_daily_row = sr.groupby(sr.index.date).nth(-2) #or Nth last row
second_last_two_rows = sr.groupby(sr.index.date).apply(lambda x: x.iloc[-3:-1]).droplevel(0) #or any slice of rows
#create exit array
exits = t1data.get_symbol_wrapper().fill(False)
exits.loc[last_n_daily_rows.index] = True
#visualize
t1data.ohlcv.data["BAC"].lw.plot(right=[(t1data.close,"close",exits)], size="s")

#REGULAR EXITS -EVERY HOUR/D/WEEK exits
exits.vbt.set(
    True, 
    every="H" # "min" "2min" "2H" "W-MON"+at time "D"+time
    #at_time="23:59:59", 
    indexer_method="ffill",  # this time or before
    inplace=True
)

DF/SR ACCESSORS

Generic

  • for common taks (docs)

rolling_apply - runs custom function over a rolling window of a fixed size (number of bars or frequency)

expanding_apply - runs custome function over expanding the window from the start of the data to the current poin

from numba import njit
mean_nb = njit(lambda a: np.nanmean(a))
hourly_anchored_expanding_mean = t1data.close.vbt.rolling_apply("1H", mean_nb) #ROLLING to FREQENCY or with fixed windows rolling_apply(10,mean_nb)
t1data.ohlcv.data["BAC"].lw.plot(right=[(t1data.close,"close"),(hourly_anchored_expanding_mean, "hourly_anchored_expanding_mean")], size="s")
#NOTE for anchored "1D" frequency - it measures timedelta that means requires 1 day between reseting (16:00 end of market, 9:30 start - not a full day, so it is enOugh to set 7H)

df['a'].vbt.overlay_with_heatmap(df['b']).show()

SIGNAL ACCESSORS

RANKING - partitioning

#pos_rank -1 when False, 0, 1 ... for consecutive Trues, allow_gaps defautlne False
# sample_mask = pd.Series([True, True, False, True, True])
ranked = sample_mask.vbt.signals.pos_rank()
ranked == 1 #select each second signal in each partition
ranked = sample_mask.vbt.signals.pos_rank(allow_gaps=True)
(ranked > -1) & (ranked % 2 == 1) #Select each second signal globally

entries.vbt.signals.first() #selects only first entries in each group
entries.vbt.signals.from_nth(n) # pos_rank >= n in each group, all from Nth

#AFTER - with variants _after which resets partition each reset array
#maximum number of exit signals after each entry signal
exits.vbt.signals.pos_rank_after(entries, reset_wait=0).max() + 1 #Count is the maximum rank plus one since ranks start with zero. We also assume that an entry signal comes before an exit signal if both are at the same timestamp by passing reset_wait=0.

entries.vbt.signals.total_partitions


#partition_pos_rank - all members of each partition have the same rank
ranked = sample_mask.vbt.signals.partition_pos_rank(allow_gaps=True) #0,0,-1,1,1
ranked == 1 # the whole second partition

Base Accessors

exits.vbt.set(
    True, 
    every="W-MON", 
    at_time="23:59:59", 
    indexer_method="ffill",  # this time or before
    inplace=True
)

Stoploss/Takeprofit

doc StopOrders

SL - ATR based

atr = data.run("atr").atr
pf = vbt.Portfolio.from_signals(
    data,
    entries=entries,
    sl_stop=atr / sub_data.close
)

EXIT after time

using from signals

f = vbt.PF.from_signals(..., td_stop="7 days")  
pf = vbt.PF.from_signals(..., td_stop=pd.Timedelta(days=7))
pf = vbt.PF.from_signals(..., td_stop=td_arr) 
#EXIT at time
pf = vbt.PF.from_signals(..., dt_stop="16:00")  #exit at 16 and later
pf = vbt.PF.from_signals(..., dt_stop=datetime.time(16, 0))
pf = vbt.PF.from_signals(  #exit last bar before
    ..., 
    dt_stop="16:00", 
    arg_config=dict(dt_stop=dict(last_before=True))
)

CALLBACKS -

  • a signal function (signal_func_nb)
    • can dynamically generate signals (True, True, False,False)
    • runs at beginning of bar
  • an adjustment function (adjust_func_nb) - doc
    • runs only if signal function above was not provided, but entry,exit arrays
    • runs before default signal function ls_signal_func_nb
      • can change pending limit orders etc.
  • a post-signal function (post_signal_func_nb)
  • post-segment function (post_segment_func_nb)

all of them are accessing SignalContext (c) as named tuple SignalContaxt (contains various metrics) such as:

  • last_limit_info - 1D with latest limit order per column
  • order_counts
  • last_return ...

"""

MEMORY

save an information piece at one timestamp and re-use at a later timestamp when using callbacks memory

Usecases:

INDICATORS DEV

#REGISTER CUSTOM INDICATOR
vbt.IndicatorFactory.register_custom_indicator(
    SupportResistance,
    name="SUPPRES",
    location=None,
    if_exists='raise'
)

#RUN INDICATOR on DATA WRAPPER
cdlbreakaway = s1data.run(vbt.indicator("talib:CDLHAMMER"), skipna=True, timeframe=["12s"])

#FROM EXPRESSION http://5.161.179.223:8000/vbt-doc/api/indicators/factory/#vectorbtpro.indicators.factory.IndicatorFactory.from_expr
WMA = vbt.IF(
    class_name='WMA',
    input_names=['close'],
    param_names=['window'],
    output_names=['wma']
).from_expr("wm_mean_nb(close, window)")

wma = WMA.run(t1data.close, window=10)
wma.wma

FAV INDICATORS

#for TALIB indicator always use skipna=True

#TALIB INDICATORS can do realing closing : timeframe=["1T"]
mom_multi = vbt.indicator("talib:MOM").run(t1data.close, timeperiod=5, timeframe=["1T","5T"], skipna=True) #returned 5T can be directly compared with 1T

#ANCHORED indciators vbt.indicator("talib:MOM") becomes AnchoredIndicator("talib:MOM", anchor="D") - freq of pd.Grouper
from ttools import AnchoredIndicator
mom_anch_d = AnchoredIndicator("talib:MOM", anchor='30min').run(t1data.data["BAC"].close, timeperiod=10)
mom = vbt.indicator("talib:MOM").run(t1data.data["BAC"].close, timeperiod=10, skipna=True)
t1data.ohlcv.data["BAC"].lw.plot(auto_scale=[mom_anch_d, mom])

#FIBO RETRACEMENT
fibo = vbt.indicator("technical:FIBONACCI_RETRACEMENTS").run(t1data.close, skipna=True)
#fibo.fibonacci_retracements

fibo_plusclose = t1data.close + fibo.fibonacci_retracements
fibo_minusclose = t1data.close - fibo.fibonacci_retracements
#fibo_plusclose
Panel(
            auto_scale=[fibo_plusclose["BAC"]],
            ohlcv=(t1data.ohlcv.data["BAC"],),
            histogram=[],
            right=[(fibo_plusclose["BAC"],),(fibo_minusclose["BAC"],)],
            left=[],
            middle1=[(fibo.fibonacci_retracements["BAC"],"fibonacci_retracements")],
            middle2=[]
            ).chart(size="xs")

#CHOPINESS indicator
chopiness = vbt.indicator("technical:CHOPINESS").run(s1data.open, s1data.high, s1data.low, s1data.close, t1data.volume, skipna=True)
s1data.ohlcv.data["BAC"].lw.plot(auto_scale=[chopiness])

#anchored VWAP
t1vwap_h = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="H")
t1vwap_h_real = t1vwap_h.vwap.vbt.realign_closing(resampler_s)

#BBANDS = vbt.indicator("pandas_ta:BBANDS")
mom_anch_d = AnchoredIndicator("talib:MOM", anchor='30min').run(t1data.data["BAC"].close, timeperiod=10)
mom = vbt.indicator("talib:MOM").run(t1data.data["BAC"].close, timeperiod=10, skipna=True)
#macd = vbt.indicator("talib:MACD").run(t1data.data["BAC"].close) #, timeframe=["1T"]) #, 
t1data.ohlcv.data["BAC"].lw.plot(auto_scale=[mom_anch_d, mom])

GROUPING - SPLITTING

#SPLITTER - splitting wrapper based on index
#http://5.161.179.223:8000/vbt-doc/tutorials/cross-validation/splitter/index.html#anchored
daily_splitter = vbt.Splitter.from_grouper(t1data.index, "D", split=None) #DOES contain last DAY

daily_splitter = vbt.Splitter.from_ranges( #doesnt contain last DY
    t1data.index,
    every="D",  
    split=None
)
daily_splitter.stats()
daily_splitter.plot()
daily_splitter.coverage()

#TAKING and APPLY MANUALLY - run UDF on ALL takes and concatenates
taken = daily_splitter.take(t1data)
inds = []
for series in taken:
    mom = vbt.indicator("talib:MOM").run(series.close, timeperiod=10, skipna=True)
    inds.append(mom)
mom_daily = vbt.base.merging.row_stack_merge(inds) #merge
mom = vbt.indicator("talib:MOM").run(t1data.close, timeperiod=10, skipna=True)
t1data.ohlcv.data["BAC"].lw.plot(left=[(mom_daily, "daily_splitter"),(mom, "original mom")]) #OHLCV with indicators on top

#TAKING and APPLY AUTOMATIC
daily_splitter = vbt.Splitter.from_grouper(t1data.index, "D", split=None) #DOES contain last DAY
def indi_run(sr):
    return vbt.indicator("talib:MOM").run(sr.close, timeperiod=10, skipna=True)

res = daily_splitter.apply(indi_run, vbt.Takeable(t1data), merge_func="row_stack", freq="1T") 


#use of IDX accessor (docs:http://5.161.179.223:8000/vbt-doc/api/base/accessors/index.html#vectorbtpro.base.accessors.BaseIDXAccessor)
daily_grouper = t1data.index.vbt.get_grouper("D")

#grouper instance can be iterated over
for name, indices in daily_grouper.iter_groups():
    print(name, indices)

#PANDAS GROUPING - series/df grouping resulting in GroupBySeries placeholder that can be aggregated(sum, mean), transformed iterated over or fitlered
for name, group in t1data.data["BAC"].close.groupby(pd.Grouper(freq='D')):
    print(name, group)

CHARTING

Using custom lightweight-charts-python

#LW df/sr accessor
t1data.ohlcv.data["BAC"].lw.plot(left=[(mom_multi, "mom_multi")]) #OHLCV with indicators on top


t5data.ohlcv.data["BAC"].lw.plot(
    left=[(mom_multi.real, "mom"),(mom_multi_beztf, "mom_beztf"), (mom_5t_orig, "mom_5t_orig"), (mom_5t_orig_realigned, "mom_5t_orig_realigned")],
    right=[(t1data.data["BAC"].close, "t1 close"),(t5data.data["BAC"].close, "t5 close")],
    size="s") #.loc[:,(20,"1T","BAC")]

#SINGLE PANEL
Panel(
            auto_scale=[cdlbreakaway],
            ohlcv=(t1data.ohlcv.data["BAC"],entries),
            histogram=[],
            right=[],
            left=[],
            middle1=[],
            middle2=[]
            ).chart(size="xs")

#MULTI PANEL
pane1 = Panel(
    #auto_scale=[mom_multi, mom_multi_1t],
    #ohlcv=(t1data.data["BAC"],),  #(series, entries, exits, other_markers)
    #histogram=[(order_imbalance_allvolume, "oivol")], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
    right=[(t1data.data["BAC"].close,"close 1T"),(t5data.data["BAC"].close,"close 5T"),(mom_multi_1t.close, "mom multi close")], # [(series, name, entries, exits, other_markers)]
    left=[(mom_multi, "mom_multi"), (mom_multi_1t, "mom_multi_1t")],
    #middle1=[],
    #middle2=[],
    #xloc="2024-02-12 09:30",
    precision=3
)
pane2 = Panel(....)

ch = chart([pane1, pane2], size="s")

MULTIACCOUNT

Simultaneous LONG and short (hedging) In vbt position requires one column of data, so hedging is possible by using two columns representing the same asset but different directions, then stack both portfolio together column stacking pf_join = vbt.PF.column_stack((pf1, pf2), group_by=True)

CUSTOM SIMULATION

ANALYSIS

ROBUSTNESS

pf_stats.sort_values(by='Sharpe Ratio', ascending=False).iloc[::-1].vbt.heatmap().show() #works when there are more metrics

#endregion

UTILS

#MEMORY
sr.info()


#peak memory usage, running once
with vbt.MemTracer() as tracer:
    my_pipeline()

print(tracer.peak_usage())

#CACHE
vbt.print_cache_stats()  
vbt.print_cache_stats(vbt.PF) 

vbt.flush() #clear cache and collect garbage
vbt.clear_cache(pf) #of specific


#TIMING
#running once
with vbt.Timer() as timer:
    my_pipeline()

print(timer.elapsed())

#multiple times
print(vbt.timeit(my_pipeline))

#NUMBA
#numba doesnt return error when indexing out of bound, this raises the error
import os
os.environ["NUMBA_BOUNDSCHECK"] = "1"