agregace volume baru, SL optimizace + goalprice
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David Brazda
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testy/ascending.py
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testy/ascending.py
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testy/fibonaccistoploss.py
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testy/fibonaccistoploss.py
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import numpy as np
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from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus
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from typing import Tuple
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from copy import deepcopy
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from v2realbot.strategy.base import StrategyState
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from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_override_for_active_trade, keyword_conditions_met
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from v2realbot.utils.utils import safe_get
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# FIBONACCI PRO PROFIT A SL
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##most used fibonacci retracement levels
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# 23.6% retracement level = (stop loss price - current price) * 0.236 + current price
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# 38.2% retracement level = (stop loss price - current price) * 0.382 + current price
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# 50.0% retracement level = (stop loss price - current price) * 0.500 + current price
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# 61.8% retracement level = (stop loss price - current price) * 0.618 + current price
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# 78.6% retracement level = (stop loss price - current price) * 0.786 + current price
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#cil: moznost pouzit fibanocci scale pro castecny stoploss exit (percentage at each downlevel)
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#a zároveň exit, případně add at each up level
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#up retracements (profit retracement)
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# exit part of position at certain -
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# [0.236, 0.382, 0.618, 1.0] - 25% off at each level? a nebo 5% add? - TBD vymyslet jak pojmout v direktive?
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#down retracement (stoploss retracement)
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# exit part of position at certain levels - TBD jak zapsat v dsirektive?
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# [0.236, 0.382, 0.618, 1.0] - 25% off at each level
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# #tridu, kterou muze vyuzivat SL a Profit optimizer
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class SLOptimizer:
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""""
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Class to handle SL positition optimization for active trade. It is assumed that two instances exists
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one for LONG trade and one for SHORT. During evaluate call, settings is initialized from trade setting
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and used for every call on that trade. When evaluate is called on different trade, it is again initialized
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according to new trade settings.
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-samostatna instance pro short a long
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-zatim pri opakovem prekroceni targetu nic nedelame (target aplikovany jen jednouo)
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exit_levels = aktuální levely, prekroceny je povazovan za vyuzitý a maze se
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exit_sizes = aktualní size multipliers, prekroceny je povazovan za vyuzitý a maze se
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init_exit_levels, init_exit_sizes - puvodni plne
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"""
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def __init__(self, direction: TradeDirection) -> None:
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##init - make exit size same length:
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self.direction = direction
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self.last_trade = 0
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# def reset_levels(self):
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# self.exit_levels = self.init_exit_levels
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# self.exit_sizes = self.init_exit_sizes
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def get_trade_details(self, state: StrategyState):
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trade: Trade = state.vars.activeTrade
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#jde o novy trade - resetujeme levely
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if trade.id != self.last_trade:
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#inicializujeme a vymazeme pripadne puvodni
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if self.initialize_levels(state) is False:
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return None, None
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self.last_trade = trade.id
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#return cost_price, sl_price
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return state.avgp, trade.stoploss_value
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def initialize_levels(self, state):
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directive_name = 'SL_opt_exit_levels_'+str(self.direction)
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SL_opt_exit_levels = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
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directive_name = 'SL_opt_exit_sizes_'+str(self.direction)
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SL_opt_exit_sizes = get_override_for_active_trade(state=state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None))
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if SL_opt_exit_levels is None or SL_opt_exit_sizes is not None:
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print("no directives found: SL_opt_exit_levels/SL_opt_exit_sizes")
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return False
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if len(SL_opt_exit_sizes) == 1:
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SL_opt_exit_sizes = SL_opt_exit_sizes * len(SL_opt_exit_levels)
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if len(SL_opt_exit_sizes) != len(SL_opt_exit_levels):
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raise Exception("exit_sizes doesnt fit exit_levels")
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self.init_exit_levels = deepcopy(SL_opt_exit_levels)
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self.init_exit_sizes = deepcopy(SL_opt_exit_sizes)
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self.exit_levels = SL_opt_exit_levels
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self.exit_sizes = SL_opt_exit_sizes
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print(f"new levels initialized {self.exit_levels=} {self.exit_sizes=}")
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return True
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def get_initial_abs_levels(self, state):
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"""
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Returns price levels corresponding to initial setting of exit_levels
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"""
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cost_price, sl_price = self.get_trade_details(state)
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if cost_price is None or sl_price is None:
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return []
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curr_sl_distance = np.abs(cost_price - sl_price)
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if self.direction == TradeDirection.SHORT :
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return [cost_price + exit_level * curr_sl_distance for exit_level in self.init_exit_levels]
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else:
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return [cost_price - exit_level * curr_sl_distance for exit_level in self.init_exit_levels]
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def get_remaining_abs_levels(self, state):
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"""
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Returns price levels corresponding to remaing exit_levels for current trade
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"""
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cost_price, sl_price = self.get_trade_details(state)
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if cost_price is None or sl_price is None:
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return []
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curr_sl_distance = np.abs(cost_price - sl_price)
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if self.direction == TradeDirection.SHORT :
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return [cost_price + exit_level * curr_sl_distance for exit_level in self.exit_levels]
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else:
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return [cost_price - exit_level * curr_sl_distance for exit_level in self.exit_levels]
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def eval_position(self, state, data) -> Tuple[float, float]:
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"""Evaluates optimalization for current position and returns if the given level was
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met and how to adjust exit position.
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"""
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cost_price, sl_price = self.get_trade_details(state)
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if cost_price is None or sl_price is None:
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print("no settings found")
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return (None, None)
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current_price = data["close"]
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# Calculate the distance of the cost prcie from the stop-loss value
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curr_sl_distance = np.abs(cost_price - sl_price)
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level_met = None
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exit_adjustment = None
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if len(self.exit_levels) == 0 or len(self.exit_sizes) == 0:
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print("levels exhausted")
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return (None, None)
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#for short
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if self.direction == TradeDirection.SHORT :
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#first available exit point
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level_price = cost_price + self.exit_levels[0] * curr_sl_distance
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if current_price > level_price:
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# Remove the first element from exit_levels.
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level_met = self.exit_levels.pop(0)
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# Remove the first element from exit_sizes.
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exit_adjustment = self.exit_sizes.pop(0)
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#for shorts
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else:
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#price of first available exit point
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level_price = cost_price - self.exit_levels[0] * curr_sl_distance
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if current_price < level_price:
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# Remove the first element from exit_levels.
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level_met = self.exit_levels.pop(0)
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# Remove the first element from exit_sizes.
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exit_adjustment = self.exit_sizes.pop(0)
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return level_met, exit_adjustment
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#0.236, 0.382, 0.5, 0.618, 0.786, 1
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exit_levels_input = [0.236, 0.382, 0.618, 1]
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exit_sizes_input = [0.5] #or [0.25, 0.25, 0.25]
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long_sl_optimizer = SLOptimizer(exit_levels_input, exit_sizes_input, TradeDirection.SHORT)
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#short_sl_optimizer = SLOptimizer(exit_levels, exit_sizes, TradeDirection.SHORT)
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print(long_sl_optimizer.get_remaining_abs_levels(90,100))
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#new LONG trade
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level_met, exit_adjustment = long_sl_optimizer.eval_position(current_price=95,cost_price=90,sl_price=100)
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print(long_sl_optimizer.get_remaining_abs_levels(90,100))
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print(level_met, exit_adjustment)
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print(long_sl_optimizer.__dict__)
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level_met, exit_adjustment = long_sl_optimizer.eval_position(current_price=95,cost_price=90,sl_price=100)
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print(long_sl_optimizer.get_remaining_abs_levels(90,100))
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print(level_met, exit_adjustment)
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print(long_sl_optimizer.__dict__)
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level_met, exit_adjustment = long_sl_optimizer.eval_position(current_price=95,cost_price=90,sl_price=100)
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print(long_sl_optimizer.get_remaining_abs_levels(90,100))
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print(level_met, exit_adjustment)
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print(long_sl_optimizer.__dict__)
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#long_sl_optimizer.eval_position(100,120)
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