26 KiB
26 KiB
SUPERTREND¶
- kombinace supertrendu na vice urovnich
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from dotenv import load_dotenv #as V2realbot is client , load env variables here env_file = "/Users/davidbrazda/Documents/Development/python/.env" # Load the .env file load_dotenv(env_file) from v2realbot.utils.utils import zoneNY import pandas as pd import numpy as np import vectorbtpro as vbt # from itables import init_notebook_mode, show import datetime from itertools import product from v2realbot.config import DATA_DIR from lightweight_charts import JupyterChart, chart, Panel, PlotAccessor from IPython.display import display # init_notebook_mode(all_interactive=True) vbt.settings.set_theme("dark") vbt.settings['plotting']['layout']['width'] = 1280 vbt.settings.plotting.auto_rangebreaks = True # Set the option to display with pagination pd.set_option('display.notebook_repr_html', True) pd.set_option('display.max_rows', 10) # Number of rows per page
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# Define the market open and close times market_open = datetime.time(9, 30) market_close = datetime.time(16, 0) entry_window_opens = 1 entry_window_closes = 370 forced_exit_start = 380 forced_exit_end = 390 #LOAD FROM PARQUET #list all files is dir directory with parquet extension dir = DATA_DIR + "/notebooks/" import os files = [f for f in os.listdir(dir) if f.endswith(".parquet")] print('\n'.join(map(str, files))) file_name = "ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet" ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow') #filter ohlcv_df to certain date range (assuming datetime index) #ohlcv_df = ohlcv_df.loc["2024-02-12 9:30":"2024-02-14 16:00"] #add vwap column to ohlcv_df #ohlcv_df["hlcc4"] = (ohlcv_df["close"] + ohlcv_df["high"] + ohlcv_df["low"] + ohlcv_df["close"]) / 4 basic_data = vbt.Data.from_data(vbt.symbol_dict({"BAC": ohlcv_df}), tz_convert=zoneNY) ohlcv_df= None basic_data.wrapper.index.normalize().nunique()
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basic_data.data["BAC"].info()
Add resample function to custom columns
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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
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s1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']] s5data = s1data.resample("5s") s5data = s5data.transform(lambda df: df.between_time('09:30', '16:00').dropna()) 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()) # t1data.data["BAC"].info() t30data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample("30T") t30data = t30data.transform(lambda df: df.between_time('09:30', '16:00').dropna()) # t30data.data["BAC"].info() s1close = s1data.close t1close = t1data.close t30close = t30data.close t30volume = t30data.volume #resample on specific index resampler = vbt.Resampler(t30data.index, s1data.index, source_freq="30T", target_freq="1s") t30close_realigned = t30close.vbt.realign_closing(resampler) #resample 1min to s resampler_s = vbt.Resampler(t1data.index, s1data.index, source_freq="1T", target_freq="1s") t1close_realigned = t1close.vbt.realign_closing(resampler_s)
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vbt.IF.list_indicators("*vwap") vbt.phelp(vbt.VWAP.run)
VWAP¶
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t1vwap_h = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="H") t1vwap_d = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="D") t1vwap_t = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="T") t1vwap_h_real = t1vwap_h.vwap.vbt.realign_closing(resampler_s) t1vwap_d_real = t1vwap_d.vwap.vbt.realign_closing(resampler_s) t1vwap_t_real = t1vwap_t.vwap.vbt.realign_closing(resampler_s) #t1vwap_5t.xloc["2024-01-3 09:30:00":"2024-01-03 16:00:00"].plot()
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#m30data.close.lw.plot() #quick few liner pane1 = Panel( histogram=[ #(s1data.volume, "volume",None, 0.8), #(m30volume, "m30volume",None, 1) ], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)] right=[ (s1data.close, "1s close"), (t1data.close, "1min close"), (t1vwap_t, "1mvwap_t"), (t1vwap_h, "1mvwap_h"), (t1vwap_d, "1mvwap_d"), (t1vwap_t_real, "1mvwap_t_real"), (t1vwap_h_real, "1mvwap_h_real"), (t1vwap_d_real, "1mvwap_d_real") # (t1close_realigned, "1min close realigned"), # (m30data.close, "30min-close"), # (m30close_realigned, "30min close realigned"), ], ) ch = chart([pane1], size="s", xloc=slice("2024-05-1 09:30:00","2024-05-25 16:00:00"))
SUPERTREND¶
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supertrend_s1 = vbt.SUPERTREND.run(s1data.high, s1data.low, s1data.close, period=5, multiplier=3) direction_series_s1 = supertrend_s1.direction supertrend_t1 = vbt.SUPERTREND.run(t1data.high, t1data.low, t1data.close, period=14, multiplier=3) direction_series_t1 = supertrend_t1.direction supertrend_t30 = vbt.SUPERTREND.run(t30data.high, t30data.low, t30data.close, period=14, multiplier=3) direction_series_t30 = supertrend_t30.direction resampler_1t_sec = vbt.Resampler(direction_series_t1.index, direction_series_s1.index, source_freq="1T", target_freq="1s") resampler_30t_sec = vbt.Resampler(direction_series_t30.index, direction_series_s1.index, source_freq="30T", target_freq="1s") direction_series_t1_realigned = direction_series_t1.vbt.realign_closing(resampler_1t_sec) direction_series_t30_realigned = direction_series_t30.vbt.realign_closing(resampler_30t_sec) #supertrend_s1.xloc["2024-01-3 09:30:00":"2024-01-03 16:00:00"].plot()
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# aligned_ups= pd.Series(False, index=direction_real.index) # aligned_downs= pd.Series(False, index=direction_real.index) # aligned_ups = direction_real == 1 & supertrend.direction == 1 # aligned_ups
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s5close = s5data.data["BAC"].close s5open = s5data.data["BAC"].open s5high = s5data.data["BAC"].high s5close_prev = s5close.shift(1) s5open_prev = s5open.shift(1) s5high_prev = s5high.shift(1) #gap nahoru od byci svicky a nevraci se zpet na jeji uroven entry_ups = (s5close_prev > s5open_prev) & (s5open > s5high_prev + 0.010) & (s5close > s5close_prev) entry_ups.value_counts() #entry_ups.info()
Entry window¶
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market_open = datetime.time(9, 30) market_close = datetime.time(16, 0) entry_window_opens = 10 entry_window_closes = 370
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entry_window_open= pd.Series(False, index=entry_ups.index) # Calculate the time difference in minutes from market open for each timestamp elapsed_min_from_open = (entry_ups.index.hour - market_open.hour) * 60 + (entry_ups.index.minute - market_open.minute) entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True #entry_window_open entry_ups = entry_ups & entry_window_open # entry_ups
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s5vwap_h = vbt.VWAP.run(s5data.high, s5data.low, s5data.close, s5data.volume, anchor="H") s5vwap_d = vbt.VWAP.run(s5data.high, s5data.low, s5data.close, s5data.volume, anchor="D") # s5vwap_h_real = s5vwap_h.vwap.vbt.realign_closing(resampler_s) # s5vwap_d_real = s5vwap_d.vwap.vbt.realign_closing(resampler_s)
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pane1 = Panel( ohlcv=(s5data.data["BAC"],), #(series, entries, exits, other_markers) histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6), opacity")] right=[#(bbands,), #[(series, name, entries, exits, other_markers)] (s5data.data["BAC"].close, "close", entry_ups), (s5data.data["BAC"].open, "open"), (s5vwap_h, "vwap5s_H",), (s5vwap_d, "vwap5s_D",) # (t1data.data["BAC"].vwap, "vwap"), # (t1data.close, "1min close"), # (supertrend_s1.trend,"STtrend"), # (supertrend_s1.long,"STlong"), # (supertrend_s1.short,"STshort") ], left = [ #(direction_series_s1,"direction_s1"), # (direction_series_t1,"direction_t1"), # (direction_series_t30,"direction_t30") ], # right=[(bbands.upperband, "upperband",), # (bbands.lowerband, "lowerband",), # (bbands.middleband, "middleband",) # ], #[(series, name, entries, exits, other_markers)] middle1=[], middle2=[], ) # pane2 = Panel( # ohlcv=(t1data.data["BAC"],uptrend_m30, downtrend_m30), #(series, entries, exits, other_markers) # histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6), opacity")] # left=[#(bbands,), #[(series, name, entries, exits, other_markers)] # (direction_real,"direction30min_real"), # ], # # left = [(supertrendm30.direction,"STdirection30")], # # # right=[(bbands.upperband, "upperband",), # # # (bbands.lowerband, "lowerband",), # # # (bbands.middleband, "middleband",) # # # ], #[(series, name, entries, exits, other_markers)] # middle1=[], # middle2=[], # title = "1m") ch = chart([pane1], sync=True, size="s", xloc=slice("2024-02-20 09:30:00","2024-02-22 16:00:00"), precision=6)
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# data = s5data.xloc["2024-01-03 09:30:00":"2024-03-10 16:00:00"] # entry = entry_ups.vbt.xloc["2024-01-03 09:30:00":"2024-03-10 16:00:00"].obj pf = vbt.Portfolio.from_signals(close=s5data, entries=entry_ups, direction="longonly", sl_stop=0.05/100, tp_stop = 0.05/100, fees=0.0167/100, freq="5s")
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pf.xloc["2024-01-26 09:30:00":"2024-02-28 16:00:00"].positions.plot()
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pf.xloc["2024-01-26 09:30:00":"2024-01-28 16:00:00"].plot()
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pd.set_option('display.max_rows', None) pf.stats() # pf.xloc["monday"].stats()
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buyvolume = t1data.data["BAC"].buyvolume sellvolume = t1data.data["BAC"].sellvolume totalvolume = buyvolume + sellvolume #adjust to minimal value to avoid division by zero sellvolume_adjusted = sellvolume.replace(0, 1e-10) oibratio = buyvolume / sellvolume #cumulative order flow (net difference) cof = buyvolume - sellvolume # Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume. order_imbalance = cof / totalvolume order_imbalance = order_imbalance.fillna(0) #nan nahradime 0 order_imbalance_allvolume = cof / t1data.data["BAC"].volume order_imbalance_sma = vbt.indicator("talib:EMA").run(order_imbalance, timeperiod=5) short_signals = order_imbalance.vbt < -0.5 #short_entries = oibratio.vbt < 0.01 short_signals.value_counts() short_signals.name = "short_entries" #.fillna(False) short_exits = short_signals.shift(-2).fillna(False).astype(bool)
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pane1 = Panel( ohlcv=(t1data.data["BAC"],), #(series, entries, exits, other_markers) histogram=[(order_imbalance_allvolume, "oib_allvolume", "rgba(53, 94, 59, 0.6)",0.5), (t1data.data["BAC"].trades, "trades",None,0.4), ], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)] # right=[ # (supertrend.trend,"STtrend"), # (supertrend.long,"STlong"), # (supertrend.short,"STshort") # ], # left = [(supertrend.direction,"STdirection")], # right=[(bbands.upperband, "upperband",), # (bbands.lowerband, "lowerband",), # (bbands.middleband, "middleband",) # ], #[(series, name, entries, exits, other_markers)] middle1=[], middle2=[], ) pane2 = Panel( ohlcv=(basic_data.data["BAC"],), #(series, entries, exits, other_markers) left=[(basic_data.data["BAC"].trades, "trades")], histogram=[(basic_data.data["BAC"].trades, "trades_hist", "white", 0.5)], #"rgba(53, 94, 59, 0.6)" # ], # [(series, name, "rgba(53, 94, 59, 0.6)")] # right=[ # (supertrend.trend,"STtrend"), # (supertrend.long,"STlong"), # (supertrend.short,"STshort") # ], # left = [(supertrend.direction,"STdirection")], # right=[(bbands.upperband, "upperband",), # (bbands.lowerband, "lowerband",), # (bbands.middleband, "middleband",) # ], #[(series, name, entries, exits, other_markers)] middle1=[], middle2=[], ) ch = chart([pane1, pane2], size="m")
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#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th) #long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th) #test na daily s reversem crossed 0 short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01 #min value of threshold long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01 #min # thirty_up_signal = t3slope.vbt.crossed_above(0.01) # thirty_down_signal = t3slope.vbt.crossed_below(-0.01) fig = plot_2y_close(priminds=[], secinds=[t3slope], close=t1data.close) #short_signal.vbt.signals.plot_as_entries(basic_data.close, fig=fig) short_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="SHORTS", line=dict(color="#ffe476"), marker=dict(color="red", symbol="triangle-down"), fill=None, connectgaps=True, )) long_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="LONGS", line=dict(color="#ffe476"), marker=dict(color="limegreen"), fill=None, connectgaps=True, )) # thirty_down_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="DOWN30", # line=dict(color="#ffe476"), # marker=dict(color="yellow", symbol="triangle-down"), # fill=None, # connectgaps=True, # )) # thirty_up_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name="UP30", # line=dict(color="#ffe476"), # marker=dict(color="grey"), # fill=None, # connectgaps=True, # )) # thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name="30min slope", # line=dict(color="yellow"), # fill=None, # connectgaps=True, # )) fig.show() # print("short signal") # print(short_signal.value_counts()) #forced_exit = pd.Series(False, index=close.index) forced_exit = basic_data.symbol_wrapper.fill(False) #entry_window_open = pd.Series(False, index=close.index) entry_window_open= basic_data.symbol_wrapper.fill(False) # Calculate the time difference in minutes from market open for each timestamp elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute) entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True #print(entry_window_open.value_counts()) forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True short_entries = (short_signal & entry_window_open) short_exits = forced_exit entries = (long_signal & entry_window_open) exits = forced_exit #long_entries.info() #number of trues and falses in long_entries # print(short_exits.value_counts()) # print(short_entries.value_counts()) #fig = plot_2y_close([],[momshort, rocp], close) #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) #print(sl_stop) #short_entries=short_entries, short_exits=short_exits, # pf = vbt.Portfolio.from_signals(close=basic_data, entries=short_entries, exits=exits, tsl_stop=0.005, tp_stop = 0.05, fees=0.0167/100, freq="1s") #sl_stop=sl_stop, tp_stop = sl_stop, # pf.stats()
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forced_exit = t1data.symbol_wrapper.fill(False) #entry_window_open = pd.Series(False, index=close.index) entry_window_open= t1data.symbol_wrapper.fill(False) # Calculate the time difference in minutes from market open for each timestamp elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute) entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True #print(entry_window_open.value_counts()) forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True short_entries = (short_signals & entry_window_open) short_exits = forced_exit entries = (long_signals & entry_window_open) exits = forced_exit pf = vbt.Portfolio.from_signals(close=t1data, entries=entries, exits=exits, short_entries=short_entries, short_exits=exits, td_stop=2, time_delta_format="rows", tsl_stop=0.005, tp_stop = 0.005, fees=0.0167/100)#, freq="1s") #sl_stop=sl_stop, tp_stop = sl_stop, pf.stats()
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pf.plot()
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pf.get_drawdowns().records_readable
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pf.orders.records_readable