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keys to env variables, optimalizations

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This commit is contained in:
David Brazda
2024-02-10 21:02:00 +07:00
parent d8bcc4bb8f
commit eff78e8157
5 changed files with 62 additions and 24 deletions
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14
v2realbot/config.py
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@ -2,6 +2,7 @@ from alpaca.data.enums import DataFeed
from v2realbot.enums.enums import Mode, Account, FillCondition
from appdirs import user_data_dir
from pathlib import Path
import os
#directory for generated images and basic reports
MEDIA_DIRECTORY = Path(__file__).parent.parent.parent / "media"
@ -123,23 +124,22 @@ HEARTBEAT_TIMEOUT=5
WEB_API_KEY="david"
#PRIMARY PAPER
ACCOUNT1_PAPER_API_KEY = 'PKGGEWIEYZOVQFDRY70L'
ACCOUNT1_PAPER_SECRET_KEY = 'O5Kt8X4RLceIOvM98i5LdbalItsX7hVZlbPYHy8Y'
ACCOUNT1_PAPER_API_KEY = os.environ.get('ACCOUNT1_PAPER_API_KEY')
ACCOUNT1_PAPER_SECRET_KEY = os.environ.get('ACCOUNT1_PAPER_SECRET_KEY')
ACCOUNT1_PAPER_MAX_BATCH_SIZE = 1
ACCOUNT1_PAPER_PAPER = True
ACCOUNT1_PAPER_FEED = DataFeed.SIP
#PRIMARY LIVE
ACCOUNT1_LIVE_API_KEY = 'AKB5HD32LPDZC9TPUWJT'
ACCOUNT1_LIVE_SECRET_KEY = 'Xq1wPSNOtwmlMTAd4cEmdKvNDgfcUYfrOaCccaAs'
ACCOUNT1_LIVE_API_KEY = os.environ.get('ACCOUNT1_LIVE_API_KEY')
ACCOUNT1_LIVE_SECRET_KEY = os.environ.get('ACCOUNT1_LIVE_SECRET_KEY')
ACCOUNT1_LIVE_MAX_BATCH_SIZE = 1
ACCOUNT1_LIVE_PAPER = False
ACCOUNT1_LIVE_FEED = DataFeed.SIP
#SECONDARY PAPER - Martin
ACCOUNT2_PAPER_API_KEY = 'PKPDTCQLNHCBC2D9GQFB'
ACCOUNT2_PAPER_SECRET_KEY = 'c1Z2V0gBleQmwHYCreqqTs45Jy33RqPGrofuSayz'
ACCOUNT2_PAPER_API_KEY = os.environ.get('ACCOUNT2_PAPER_API_KEY')
ACCOUNT2_PAPER_SECRET_KEY = os.environ.get('ACCOUNT2_PAPER_SECRET_KEY')
ACCOUNT2_PAPER_MAX_BATCH_SIZE = 1
ACCOUNT2_PAPER_PAPER = True
ACCOUNT2_PAPER_FEED = DataFeed.IEX

16
v2realbot/loader/aggregator.py
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@ -48,7 +48,7 @@ class TradeAggregator:
self.excludes = excludes
self.skip_cache = skip_cache
if mintick >= resolution:
if resolution > 0 and mintick >= resolution:
print("Mintick musi byt mensi nez resolution")
raise Exception
@ -320,13 +320,13 @@ class TradeAggregator:
#TODO: do budoucna vymyslet, kdyz bude mene tradu, tak to radit vzdy do spravneho intervalu
#zarovname time prvniho baru podle timeframu kam patří (např. 5, 10, 15 ...) (ROUND)
if self.align == StartBarAlign.ROUND and self.bar_start == 0:
t = datetime.fromtimestamp(data['t'])
t = datetime.fromtimestamp(data['t'], tz=zoneUTC)
t = t - timedelta(seconds=t.second % self.resolution,microseconds=t.microsecond)
self.bar_start = datetime.timestamp(t)
#nebo pouzijeme datum tradu zaokrouhlene na vteriny (RANDOM)
else:
#ulozime si jeho timestamp (odtum pocitame resolution)
t = datetime.fromtimestamp(int(data['t']))
t = datetime.fromtimestamp(int(data['t']), tz=zoneUTC)
#timestamp
self.bar_start = int(data['t'])
@ -376,7 +376,7 @@ class TradeAggregator:
if self.mintick != 0 and self.lastBarConfirmed:
#d zacatku noveho baru musi ubehnout x sekund nez posilame updazte
#pocatek noveho baru + Xs musi byt vetsi nez aktualni trade
if (self.newBar['time'] + timedelta(seconds=self.mintick)) > datetime.fromtimestamp(data['t']):
if (self.newBar['time'] + timedelta(seconds=self.mintick)) > datetime.fromtimestamp(data['t'], tz=zoneUTC):
#print("waiting for mintick")
return []
else:
@ -756,8 +756,14 @@ class TradeAggregator:
Ve strategii je třeba počítat s tím, že open v nepotvrzeném baru není finální.
"""""
#pocet ticku např. 10ticků, případně pak na procenta
if self.resolution < 0: # Treat as percentage
reference_price = self.lastConfirmedBar['close'] if self.lastConfirmedBar is not None else float(data['p'])
brick_size = abs(self.resolution) * reference_price / 100.0
else: # Treat as absolute value pocet ticku
brick_size = self.resolution
#pocet ticku např. 10ticků, případně pak na procenta
#brick_size = self.resolution
#potvrzene pripravene k vraceni
confirmedBars = []
#potvrdi existujici a nastavi k vraceni

4
v2realbot/strategy/StrategyClassicSL.py
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@ -41,6 +41,7 @@ class StrategyClassicSL(Strategy):
if rel_profit >= float(max_sum_profit_to_quit_rel):
self.state.ilog(e=f"QUITTING MAX SUM REL PROFIT REACHED {max_sum_profit_to_quit_rel=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.state.vars.pending = "max_sum_profit_to_quit_rel"
if self.mode not in [Mode.BT, Mode.PREP]:
send_to_telegram(f"QUITTING MAX SUM REL PROFIT REACHED {max_sum_profit_to_quit_rel=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.signal_stop = True
return True
@ -48,6 +49,7 @@ class StrategyClassicSL(Strategy):
if rel_profit < 0 and rel_profit <= float(max_sum_loss_to_quit_rel):
self.state.ilog(e=f"QUITTING MAX SUM REL LOSS REACHED {max_sum_loss_to_quit_rel=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.state.vars.pending = "max_sum_loss_to_quit_rel"
if self.mode not in [Mode.BT, Mode.PREP]:
send_to_telegram(f"QUITTING MAX SUM REL LOSS REACHED {max_sum_loss_to_quit_rel=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.signal_stop = True
return True
@ -56,6 +58,7 @@ class StrategyClassicSL(Strategy):
if float(self.state.profit) >= float(max_sum_profit_to_quit):
self.state.ilog(e=f"QUITTING MAX SUM ABS PROFIT REACHED {max_sum_profit_to_quit=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.state.vars.pending = "max_sum_profit_to_quit"
if self.mode not in [Mode.BT, Mode.PREP]:
send_to_telegram(f"QUITTING MAX SUM ABS PROFIT REACHED {max_sum_profit_to_quit=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.signal_stop = True
return True
@ -63,6 +66,7 @@ class StrategyClassicSL(Strategy):
if float(self.state.profit) < 0 and float(self.state.profit) <= float(max_sum_loss_to_quit):
self.state.ilog(e=f"QUITTING MAX SUM ABS LOSS REACHED {max_sum_loss_to_quit=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.state.vars.pending = "max_sum_loss_to_quit"
if self.mode not in [Mode.BT, Mode.PREP]:
send_to_telegram(f"QUITTING MAX SUM ABS LOSS REACHED {max_sum_loss_to_quit=} {self.state.profit=} {rel_profit=} relprofits:{str(self.state.rel_profit_cum)}")
self.signal_stop = True
return True

12
v2realbot/strategyblocks/newtrade/sizing.py
View File

@ -9,6 +9,7 @@ from traceback import format_exc
from v2realbot.strategyblocks.newtrade.conditions import go_conditions_met, common_go_preconditions_check
from v2realbot.strategyblocks.indicators.helpers import get_source_series
import numpy as np
from scipy.interpolate import interp1d
def get_size(state: StrategyState, data, signaloptions: dict, direction: TradeDirection):
return state.vars.chunk * get_multiplier(state, signaloptions, direction)
@ -134,7 +135,16 @@ def get_multiplier(state: StrategyState, data, signaloptions: dict, direction: T
return multiplier
state.ilog(lvl=1,e=f"SIZER - Input value of {pattern_source} value {input_value}", options=options, time=state.time)
multiplier = np.interp(input_value, pattern_source_axis, pattern_size_axis)
#puvodni jednoducha interpolace
#multiplier = np.interp(input_value, pattern_source_axis, pattern_size_axis)
# Updated interpolation function for smoother results
# Create the interpolation function
f = interp1d(pattern_source_axis, pattern_size_axis, kind='cubic')
# Interpolate the input value using the interpolation function
multiplier = f(input_value)
state.ilog(lvl=1,e=f"SIZER - Interpolated value {multiplier}", input_value=input_value, pattern_source_axis=pattern_source_axis, pattern_size_axis=pattern_size_axis, options=options, time=state.time)
if multiplier > 1 or multiplier <= 0:

30
v2realbot/tools/sizingpattervisual.py
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@ -1,6 +1,6 @@
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
# Sem zadat pattern X a Y pro VIZUALIZACI
#minutes
@ -14,16 +14,34 @@ pattern_y = [0.1, 0.5, 0.8, 1, 0.6, 0.1]
#celkový profit
# Generating a range of input values for interpolation
input_values = np.linspace(min(pattern_x), max(pattern_x), 500)
multipliers = np.interp(input_values, pattern_x, pattern_y)
input_values = np.linspace(min(pattern_x), max(pattern_x), 1000) # Increase the number of points
# Plotting
# Bezier interpolation
interp_func = interp1d(pattern_x, pattern_y, kind='cubic')
multipliers_cubic = interp_func(input_values)
multipliers_linear = np.interp(input_values, pattern_x, pattern_y)
# Plotting multipliers_linear and multipliers_cubic on the same canvas
plt.figure(figsize=(10, 6))
plt.plot(pattern_x, pattern_y, 'o', label='Original Points')
plt.plot(input_values, multipliers, label='Interpolated Values')
plt.plot(input_values, multipliers_linear, label='Linear Interpolation')
plt.plot(input_values, multipliers_cubic, label='Cubic Interpolation')
plt.xlabel('X values')
plt.ylabel('Interpolated Multipliers')
plt.title('Interpolation Chart')
plt.title('Interpolation Comparison')
plt.legend()
plt.grid(True)
plt.show()
# # Plotting multipliers_cubic
# plt.figure(figsize=(10, 6))
# plt.plot(pattern_x, pattern_y, 'o', label='Original Points')
# plt.plot(input_values, multipliers_cubic, label='Cubic Interpolation')
# plt.xlabel('X values')
# plt.ylabel('Interpolated Multipliers')
# plt.title('Cubic Interpolation Chart')
# plt.legend()
# plt.grid(True)
# plt.show()