diff --git a/testy/archive/alpacasnapshot.py b/testy/archive/alpacasnapshot.py new file mode 100644 index 0000000..b7d360c --- /dev/null +++ b/testy/archive/alpacasnapshot.py @@ -0,0 +1,64 @@ +# 2 clients for historical data StockHistoricalDataClient (needs keys), CryptoHistoricalDataClient +# 2 clients for real time data CryptoDataStream, StockDataStream + + +# naimportuju si daneho clienta +from alpaca.data.historical import StockHistoricalDataClient, CryptoHistoricalDataClient + + +#pokdu pouzivam historicke data(tzn. REST) tak si naimportuju dany request object +from alpaca.data.requests import StockLatestQuoteRequest, StockBarsRequest, StockTradesRequest, StockSnapshotRequest + +#objekty se kterymi pak pracuju (jsou soucasi package výše, tady jen informačně) +from alpaca.data import Quote, Trade, Snapshot, Bar +from alpaca.data.models import BarSet, QuoteSet, TradeSet +from alpaca.data.timeframe import TimeFrame, TimeFrameUnit +from v2realbot.utils.utils import zoneNY +from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY +from config import API_KEY, SECRET_KEY +from alpaca.data.enums import DataFeed +from datetime import datetime, timedelta +import pandas as pd +from rich import print +from collections import defaultdict +from pandas import to_datetime +from msgpack.ext import Timestamp +from v2realbot.utils.historicals import convert_daily_bars + +def get_last_close(): + pass + +def get_todays_open(): + pass + +##vrati historicke bary v nasem formatu +def get_historical_bars(symbol: str, time_from: datetime, time_to: datetime, timeframe: TimeFrame): + stock_client = StockHistoricalDataClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=True) + bar_request = StockBarsRequest(symbol_or_symbols=symbol,timeframe=timeframe, start=time_from, end=time_to, feed=DataFeed.SIP) + bars: BarSet = stock_client.get_stock_bars(bar_request) + print("puvodni bars", bars["BAC"]) + print(bars) + return convert_daily_bars(bars[symbol]) + + +#v initu plnime pozadovana historicka data do historicals[] +#zatim natvrdo +#last 30 days bars + + +#get 30 days +time_to = datetime.now(tz=zoneNY) +time_from = time_to - timedelta(days=2) + +bary = get_historical_bars("BAC", time_from, time_to, TimeFrame.Hour) +print(bary) +historicals = defaultdict(dict) +historicals["30"] = bary +print(historicals) + +# stock_client = StockHistoricalDataClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=True) +# snapshotRequest = StockSnapshotRequest(symbol_or_symbols=["BAC"], feed="sip") +# snapshotResponse = stock_client.get_stock_snapshot(snapshotRequest) +# print("snapshot", snapshotResponse) +# snapshotResponse["BAC"]["dailyBar"] + diff --git a/testy/archive/interpolace.py b/testy/archive/interpolace.py index 14a7c4e..60adcaf 100644 --- a/testy/archive/interpolace.py +++ b/testy/archive/interpolace.py @@ -15,3 +15,6 @@ y_interp = spi.interp1d(x, y) #create plot of x vs. y #plt.plot(x, y, '-ob') + + + diff --git a/testy/archive/npinterpol.py b/testy/archive/npinterpol.py new file mode 100644 index 0000000..0e7290c --- /dev/null +++ b/testy/archive/npinterpol.py @@ -0,0 +1,5 @@ +import numpy as np + + +atr10 = 11.1 +print(np.interp(atr10, [1, 10,11,12], [0, 1,100,1])) \ No newline at end of file diff --git a/testy/fourier.py b/testy/fourier.py new file mode 100644 index 0000000..525a95b --- /dev/null +++ b/testy/fourier.py @@ -0,0 +1,22 @@ +import numpy as np +import scipy.fft as fft + + +time_series = np.array(prices) +n = len(time_series) + +# Compute the Fourier transform +yf = fft(time_series) +xf = np.linspace(0.0, 1.0/(2.0), n//2) +# Compute the Fourier transform +yf = np.abs(fft(time_series)) + +# Find the corresponding frequencies +frequencies = xf + +# Find the corresponding amplitudes +amplitudes = 2.0/n * np.abs(yf[:n//2]) + +# Interpret the amplitudes and frequencies +for freq, ampl in zip(frequencies, amplitudes): + print(f"Frequency: {freq}, Amplitude: {ampl}") \ No newline at end of file diff --git a/testy/localmaximatest.py b/testy/localmaximatest.py new file mode 100644 index 0000000..053cdde --- /dev/null +++ b/testy/localmaximatest.py @@ -0,0 +1,106 @@ +import numpy as np +import matplotlib.pyplot as plt +from v2realbot.controller.services import get_archived_runner_details_byID +from v2realbot.common.model import RunArchiveDetail +from scipy.signal import argrelextrema + +id = "c5ae757f-6bdd-4d1f-84a8-98bdaad65a28" + +res, val = get_archived_runner_details_byID(id) +if res < 0: + print(res) + +detail = RunArchiveDetail(**val) +# detail.indicators[0] +price_series = np.array(detail.bars["vwap"]) +#price_series = detail.bars["vwap"] +timestamps = detail.bars["time"] + +prices = [] +#TODO pridat k indikatorum convert to numpy, abych mohl pouzivat numpy operace v expressionu + + +def get_local_maxima_numpy( + series: np.ndarray, + debug=False, +) -> np.ndarray: + """calculate local maximal point""" + if series.size == 0: + return np.array([]) + + # Calculate the difference between adjacent elements. + diff = np.diff(series) + + # Find the indices of the elements where the difference changes sign from positive to negative. + high_index = np.where((diff[:-1] >= 0) & (diff[1:] < 0))[0] + 1 + + # Return a NumPy array containing the local maxima. + return high_index#series[high_index] + +def get_local_minima_numpy( + series: np.ndarray, + debug=False, +) -> np.ndarray: + """calculate local maximal point""" + if series.size == 0: + return np.array([]) + + # Calculate the difference between adjacent elements. + diff = np.diff(series) + + # Find the indices of the elements where the difference changes sign from positive to negative. + low_index = np.where((diff[:-1] <= 0) & (diff[1:] > 0))[0] + 1 + + # Return a NumPy array containing the local maxima. + return low_index#series[high_index] + +def get_local_minima(prices): + return prices[-2] if len(prices) >= 3 and prices[-2] > prices[-3] and prices[-2] > prices[-1] else None + +# iter_prices = [] +# for price in detail.bars["vwap"]: +# iter_prices.append(price) +# get_local_minima(iter_prices) + +def calculate_support_resistance(bars, window=5): + lows = np.array(bars['low']) + highs = np.array(bars['high']) + + rolling_support = np.minimum.accumulate(lows)[::-1][:window][::-1] + rolling_resistance = np.maximum.accumulate(highs)[::-1][:window][::-1] + + return {'rolling_support': rolling_support.tolist(), 'rolling_resistance': rolling_resistance.tolist()} + +rolling = calculate_support_resistance(detail.bars, 5) +print(rolling) + + +# func = "prices[-1] if np.all(prices[-1] > prices[-2:]) else 0" +# #func = "prices[-2] if len(prices) >= 3 and prices[-2] > prices[-3] and prices[-2] > prices[-1] else None" +# for price in price_series: +# prices.append(price) +# print(eval(func)) +# maxima_indices = argrelextrema(price_series, np.greater)[0] +# minima_indices = argrelextrema(price_series, np.less)[0] +# # Print the indices of local maxima and minima +# print("Local Maxima Indices:", maxima_indices) +# print("Local Minima Indices:", minima_indices) + +print("from new function") +maxima_indices = get_local_maxima_numpy(price_series) +minima_indices = get_local_minima_numpy(price_series) +print("Local Maxima Indices:", maxima_indices) +print("Local Minima Indices:", minima_indices) + +# Plot the price series with local maxima and minima +plt.figure(figsize=(10, 6)) +plt.plot(range(len(price_series)), price_series, label='Price Series') +plt.scatter(maxima_indices, price_series[maxima_indices], color='r', label='Local Maxima', zorder=5) +plt.scatter(minima_indices, price_series[minima_indices], color='g', label='Local Minima', zorder=5) +plt.xlabel('Time') +plt.ylabel('Price') +plt.title('Price Series with Local Maxima and Minima') +plt.legend() +plt.show() + + diff --git a/testy/scaffoldings/custom_ind.py b/testy/scaffoldings/custom_ind.py new file mode 100644 index 0000000..685ba59 --- /dev/null +++ b/testy/scaffoldings/custom_ind.py @@ -0,0 +1,74 @@ +import numpy as np +import matplotlib.pyplot as plt +from v2realbot.controller.services import get_archived_runner_details_byID, preview_indicator_byTOML +from v2realbot.common.model import RunArchiveDetail, InstantIndicator +from scipy.signal import argrelextrema +from v2realbot.utils.utils import AttributeDict, zoneNY, zonePRG, safe_get, dict_replace_value, Store, parse_toml_string, json_serial, is_open_hours, send_to_telegram + +##SCAFFOLDING for development of new indicator + +runner_id = "7512b097-1f29-4c61-a331-2b1a40fd3f91" + +toml = """ +#[stratvars.indicators.local_maxik] +type = 'custom' +subtype = 'basestats' +on_confirmed_only = true +cp.lookback = 30 +cp.source = 'vwap' +cp.function = 'maxima' +""" + +toml = """ +type = 'custom' +subtype = 'expression' +on_confirmed_only = true +cp.expression = 'int(utls.is_pivot(high,3))' +""" + +toml = """ +type = 'custom' +subtype = 'expression' +on_confirmed_only = true +cp.expression = 'int(utls.is_pivot(high,3))' +""" + + +res, val = get_archived_runner_details_byID(runner_id) +if res < 0: + print("error fetching runner") + print(res) + +detail = RunArchiveDetail(**val) + +res, toml_parsed = parse_toml_string(toml) +if res < 0: + print("invalid tml",res, toml) +print(toml_parsed) +#toml_parsed = AttributeDict(**toml_parsed) +# for i in toml_parsed["stratvars"]["indicators"]: +# break + +ind: InstantIndicator = InstantIndicator(name="testind", toml=toml) + +result, new_ind_values = preview_indicator_byTOML(id=runner_id, indicator=ind) +if result < 0: + print("error", result, val) + +# detail.indicators[0] +price_series = np.array(detail.bars["vwap"]) +new_ind_value = np.array(new_ind_values) +#price_series = detail.bars["vwap"] +#timestamps = detail.bars["time"] + +# Plot the price series with local maxima and minima +plt.figure(figsize=(10, 6)) +plt.plot(range(len(price_series)), price_series, label='Price') +plt.plot(range(len(new_ind_value)), new_ind_value, label='Indicator') +plt.xlabel('Time') +plt.ylabel('Price') +plt.title('Price Series with Local Maxima and Minima') +plt.legend() +plt.show() + + diff --git a/testy/templatetest.py b/testy/templatetest.py new file mode 100644 index 0000000..ffb54c9 --- /dev/null +++ b/testy/templatetest.py @@ -0,0 +1,19 @@ +import numpy as np +import matplotlib.pyplot as plt +from v2realbot.controller.services import get_archived_runner_details_byID +from v2realbot.common.model import RunArchiveDetail +# Generate sample price data +timestamps = np.arange('2023-10-27', '2023-10-28', dtype='datetime64[s]') +price = 100 + np.arange(100) * 0.5 + +id = "e74b5d35-6552-4dfc-ba59-2eda215af292" + +res, val = get_archived_runner_details_byID(id) +if res < 0: + print(res) + +detail = RunArchiveDetail(**val) +# detail.indicators[0] +price = detail.bars["vwap"] +timestamps = detail.bars["time"] + diff --git a/testy/testsuppressmedium.py b/testy/testsuppressmedium.py new file mode 100644 index 0000000..923536d --- /dev/null +++ b/testy/testsuppressmedium.py @@ -0,0 +1,124 @@ +import numpy as np +import matplotlib.pyplot as plt +from v2realbot.controller.services import get_archived_runner_details_byID +from v2realbot.common.model import RunArchiveDetail +from scipy.signal import argrelextrema +import mplfinance + +id = "e74b5d35-6552-4dfc-ba59-2eda215af292" + +res, val = get_archived_runner_details_byID(id) +if res < 0: + print(res) + +detail = RunArchiveDetail(**val) +# detail.indicators[0] +price_series = np.array(detail.bars["vwap"]) +df = {} +highs = np.array(detail.bars["high"]) +lows = np.array(detail.bars["low"]) + +np_high = np.array(detail.bars["high"]) +np_low = np.array(detail.bars["low"]) +price_series = detail.bars["vwap"] +timestamps = detail.bars["time"] + +prices = [] +#TODO pridat k indikatorum convert to numpy, abych mohl pouzivat numpy operace v expressionu + +# func = "prices[-1] if np.all(prices[-1] > prices[-2:]) else 0" +# #func = "prices[-2] if len(prices) >= 3 and prices[-2] > prices[-3] and prices[-2] > prices[-1] else None" +# for price in price_series: +# prices.append(price) +# print(eval(func)) + +class Sup_Res_Finder(): + def __init__(self, s=None): + if s is None: + self.s = np.mean(np.diff(np.concatenate([[np.nan], np.highs, [np.nan]], axis=0))) + else: + self.s = s + + def isSupport(self, lows, i): + support = lows[i] < lows[i-1] and lows[i] < lows[i+1] \ + and lows[i+1] < lows[i+2] and lows[i-1] < lows[i-2] + + return support + + def isResistance(self, highs, i): + resistance = highs[i] > highs[i-1] and highs[i] > highs[i+1] \ + and highs[i+1] > highs[i+2] and highs[i-1] > highs[i-2] + + return resistance + + def find_levels(self, highs, lows): + levels = [] + + for i in range(2, len(lows) - 2): + if self.isSupport(lows, i): + l = lows[i] + + if not np.any([abs(l - x) < self.s for x in levels]): + levels.append((i, l)) + + elif self.isResistance(highs, i): + l = highs[i] + + if not np.any([abs(l - x) < self.s for x in levels]): + levels.append((i, l)) + + return levels + +def plot_ohlc_with_support_resistance(bars, s=None): + highs = np.array(bars['high']) + lows = np.array(bars['low']) + + finder = Sup_Res_Finder(s=s) + levels = finder.find_levels(highs, lows) + + fig, ax = plt.subplots() + + # Plot the candlesticks + + ax.plot(bars['time'], highs, color='green', linestyle='-', linewidth=0.8) + ax.plot(bars['time'], lows, color='red', linestyle='-', linewidth=0.8) + ax.fill_between(bars['time'], highs, lows, color='green' if highs[0] > lows[0] else 'red', alpha=0.5) + + # Plot the support and resistance levels + + for level in levels: + ax.hlines(level[1], level[0] - 0.5, level[0] + 0.5, color='black', linewidth=1) + + ax.set_xlabel('Time') + ax.set_ylabel('Price') + ax.set_title('OHLC Chart with Support and Resistance Levels') + + plt.show() + + +plot_ohlc_with_support_resistance(detail.bars, 0.05) + +# print(price_series) +# # Find local maxima and minima using the optimized function. +# maxima_indices = argrelextrema(price_series, np.greater)[0] +# minima_indices = argrelextrema(price_series, np.less)[0] +# print(maxima_indices) +# print(minima_indices) +# # # Find local maxima and minima +# # maxima_indices = argrelextrema(price_series, np.greater)[0] +# # minima_indices = argrelextrema(price_series, np.less)[0] + +# Plot the price series with local maxima and minima +# plt.figure(figsize=(10, 6)) +# plt.plot(range(len(price_series)), price_series, label='Price Series') +# plt.scatter(maxima_indices, price_series[maxima_indices], color='r', label='Local Maxima', zorder=5) +# plt.scatter(minima_indices, price_series[minima_indices], color='g', label='Local Minima', zorder=5) +# plt.xlabel('Time') +# plt.ylabel('Price') +# plt.title('Price Series with Local Maxima and Minima') +# plt.legend() +# plt.show() + +# # Print the indices of local maxima and minima +# print("Local Maxima Indices:", maxima_indices) +# print("Local Minima Indices:", minima_indices) diff --git a/testy/vectorbt/test.py b/testy/vectorbt/test.py new file mode 100644 index 0000000..3fb67aa --- /dev/null +++ b/testy/vectorbt/test.py @@ -0,0 +1,23 @@ +import vectorbt as vb + + +class ShortOnCloseBreakoutStrategy: + def init(self): + self.last_close = self.data.close[-1] + + def next(self): + # Enter a short position when the price is below the last day's close + if self.data.close < self.last_close: + self.sell() + + # Exit the short position after 10 ticks + elif self.data.close > self.last_close + 10: + self.buy() + +# Create a backtest object +#backtest = vb.Backtest(ShortOnCloseBreakoutStrategy()) + +# Load the closing prices for the assets in the portfolio +close = vb.YFData.download('AAPL', start='2023-01-01').get('Close') +print(close) +# Backtest the strategy \ No newline at end of file diff --git a/testy/volatilitytest.py b/testy/volatilitytest.py new file mode 100644 index 0000000..0486e8c --- /dev/null +++ b/testy/volatilitytest.py @@ -0,0 +1,51 @@ +import numpy as np +import matplotlib.pyplot as plt +from v2realbot.controller.services import get_archived_runner_details_byID +from v2realbot.common.model import RunArchiveDetail +# Generate sample price data +timestamps = np.arange('2023-10-27', '2023-10-28', dtype='datetime64[s]') +price = 100 + np.arange(100) * 0.5 + +id = "e74b5d35-6552-4dfc-ba59-2eda215af292" + +res, val = get_archived_runner_details_byID(id) +if res < 0: + print(res) + +detail = RunArchiveDetail(**val) +# detail.indicators[0] +price = detail.bars["vwap"] +timestamps = detail.bars["time"] + +# Calculate the standard deviation of price changes over a specified time interval +def calculate_volatility(price, window): + volatility = np.zeros_like(price) + for i in range(window, len(price)): + volatility[i] = np.std(price[i - window: i]) + return volatility + +# Set a threshold for the indicator +threshold = 0.4 + +# Identify breakout points based on the threshold +def identify_breakouts(volatility, threshold): + return volatility > threshold + +# Plot the price data and the volatility breakout points +def plot_data(timestamps, price, breakout_points): + plt.figure(figsize=(12, 6)) + plt.plot(timestamps, price, label='Price') + breakout_timestamps = timestamps[np.where(breakout_points)[0]] + breakout_prices = price[np.where(breakout_points)[0]] + plt.scatter(breakout_timestamps, breakout_prices, color='r', label='Volatility Breakout') + plt.xlabel('Time') + plt.ylabel('Price') + plt.title('Intraday Volatility Breakout Indicator') + plt.legend() + plt.show() + +# Applying the functions +window = 30 +volatility = calculate_volatility(price, window) +breakout_points = identify_breakouts(volatility, threshold) +plot_data(timestamps, price, breakout_points) \ No newline at end of file diff --git a/v2realbot/ENTRY_ClassicSL_v01.py b/v2realbot/ENTRY_ClassicSL_v01.py index e1c79b8..6f744ad 100644 --- a/v2realbot/ENTRY_ClassicSL_v01.py +++ b/v2realbot/ENTRY_ClassicSL_v01.py @@ -4,12 +4,13 @@ from v2realbot.strategy.base import StrategyState from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account from v2realbot.utils.utils import zoneNY, print -from datetime import datetime +from v2realbot.utils.historicals import get_historical_bars +from datetime import datetime, timedelta from rich import print as printanyway from threading import Event import os from traceback import format_exc - +from alpaca.data.timeframe import TimeFrame, TimeFrameUnit from v2realbot.strategyblocks.newtrade.prescribedtrades import execute_prescribed_trades from v2realbot.strategyblocks.newtrade.signals import signal_search from v2realbot.strategyblocks.activetrade.activetrade_hub import manage_active_trade @@ -124,6 +125,30 @@ def init(state: StrategyState): state.ind_mapping = {**local_dict_inds, **local_dict_bars} printanyway("IND MAPPING DONE:", state.ind_mapping) + #30 DAYS historicall data fill - pridat do base pokud se osvedci + # -1 je vždy včerejšek v tomto případě + #diky tomu mají indikátory data 30 dní zpět (tzn. můžu počítat last day close, atp) + #do budoucna systematizovat přístup k historickým dat + # např. historicals.days state.historical.bars["days"]atp. + #nyní jednoucelne state.dailyBars + + #LIVE a PAPER - bereme time now + #BT - bereme time bt_start + if state.mode in (Mode.LIVE, Mode.PAPER): + time_to = datetime.now(tz=zoneNY) + else: + time_to = state.bt.bp_from + + + #TBD pridat i hour data - pro pocitani RSI na hodine + #get 30 days (time_from musí být alespoň -2 aby to bralo i vcerejsek) + time_from = time_to - timedelta(days=40) + time_to = time_to - timedelta(days=1) + state.dailyBars = get_historical_bars(state.symbol, time_from, time_to, TimeFrame.Day) + #printanyway("daily bars FILLED", state.dailyBars) + #zatim ukladame do extData - pro instant indicatory a gui + state.extData["dailyBars"] = state.dailyBars + def main(): name = os.path.basename(__file__) se = Event() diff --git a/v2realbot/controller/services.py b/v2realbot/controller/services.py index 3272a8a..fd51d28 100644 --- a/v2realbot/controller/services.py +++ b/v2realbot/controller/services.py @@ -277,18 +277,20 @@ def is_runner_running(id: UUID): def save_history(id: UUID, st: object, runner: Runner, reason: str = None): - #zkousime precist profit z objektu - try: - profit = st.state.profit - trade_count = len(st.state.tradeList) - except Exception as e: - profit = str(e) + # #zkousime precist profit z objektu + # try: + # profit = st.state.profit + # trade_count = len(st.state.tradeList) + # except Exception as e: + # profit = str(e) - for i in db.stratins: - if str(i.id) == str(id): - i.history += "START:"+str(runner.run_started)+"STOP:"+str(runner.run_stopped)+"ACC:"+runner.run_account.value+"M:"+runner.run_mode.value+"PROFIT:"+str(round(profit,2))+ "TradeCNT:"+str(trade_count) + "REASON:" + str(reason) - #i.history += str(runner.__dict__)+"
" - db.save() + #zapisujeme pouze reason - pouzito jen pri exceptione + if reason is not None: + for i in db.stratins: + if str(i.id) == str(id): + i.history += "\nREASON:" + str(reason) + #i.history += str(runner.__dict__)+"
" + db.save() #Capsule to run the thread in. Needed in order to update db after strat ends for any reason# def capsule(target: object, db: object, inter_batch_params: dict = None): @@ -302,7 +304,7 @@ def capsule(target: object, db: object, inter_batch_params: dict = None): target.start() print("Strategy instance stopped. Update runners") - reason = "SHUTDOWN OK" + reason = None except Exception as e: reason = "SHUTDOWN Exception:" + str(e) + format_exc() #raise RuntimeError('Exception v runneru POZOR') from e @@ -324,7 +326,7 @@ def capsule(target: object, db: object, inter_batch_params: dict = None): i.run_instance = None i.run_pause_ev = None i.run_stop_ev = None - #ukladame radek do historie (pozdeji refactor) + #ukladame jen pro zapis exception reasonu save_history(id=i.strat_id, st=target, runner=i, reason=reason) #store in archive header and archive detail archive_runner(runner=i, strat=target, inter_batch_params=inter_batch_params) @@ -453,8 +455,14 @@ def batch_run_manager(id: UUID, runReq: RunRequest, rundays: list[RunDay]): #promenna pro sdileni mezi runy jednotlivych batchů (např. daily profit) inter_batch_params = dict(batch_profit=0, batch_rel_profit=0) note_from_run_request = runReq.note + first = None + last = None for day in rundays: cnt += 1 + if cnt == 1: + first = day.start + elif cnt == cnt_max: + last = day.end print("Datum od", day.start) print("Datum do", day.end) runReq.bt_from = day.start @@ -468,6 +476,21 @@ def batch_run_manager(id: UUID, runReq: RunRequest, rundays: list[RunDay]): break print("Batch manager FINISHED") + ##TBD sem zapsat do hlavicky batchů! abych měl náhled - od,do,profit, metrics + batch_abs_profit = 0 + batch_rel_profit = 0 + try: + #print(inter_batch_params) + batch_abs_profit = inter_batch_params["batch_profit"] + batch_rel_profit = inter_batch_params["batch_rel_profit"] + except Exception as e: + print("inter batch params problem", inter_batch_params, str(e)+format_exc()) + + for i in db.stratins: + if str(i.id) == str(id): + i.history += "\nBatch: "+str(batch_id)+" "+str(first)+" "+str(last)+" P:"+str(int(batch_abs_profit))+ "R:"+str(round(batch_rel_profit,4)) + #i.history += str(runner.__dict__)+"
" + db.save() #stratin run @@ -643,13 +666,14 @@ def populate_metrics_output_directory(strat: StrategyInstance, inter_batch_param #naplneni batch sum profitu if inter_batch_params is not None: - res["profit"]["batch_sum_profit"] = inter_batch_params["batch_profit"] + res["profit"]["batch_sum_profit"] = int(inter_batch_params["batch_profit"]) res["profit"]["batch_sum_rel_profit"] = inter_batch_params["batch_rel_profit"] - #rel_profit rozepsane zisky - res["profit"]["rel_profits"] = strat.state.rel_profit_cum #rel_profit zprumerovane - res["profit"]["rel_profit_cum"] = float(np.mean(strat.state.rel_profit_cum)) if len(strat.state.rel_profit_cum) > 0 else 0 + res["profit"]["daily_rel_profit_avg"] = float(np.mean(strat.state.rel_profit_cum)) if len(strat.state.rel_profit_cum) > 0 else 0 + #rel_profit rozepsane zisky + res["profit"]["daily_rel_profit_list"] = strat.state.rel_profit_cum + #metrikz z prescribedTrades, pokud existuji try: @@ -1117,10 +1141,10 @@ def get_testlists(): # endregion #WIP - instant indicators -def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator): +def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator, save: bool = True): try: if indicator.name is None: - return (-2, "name is required") + return (-2, ["name is required"]) #print("na zacatku", indicator.toml) @@ -1163,16 +1187,6 @@ def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator): if res < 0: return (-2, "no archived runner {id}") - #TODO - conditional udelat podminku - # if value == "conditional": - # conditions = state.vars.indicators[indname]["cp"]["conditions"] - # for condname,condsettings in conditions.items(): - # state.vars.indicators[indname]["cp"]["conditions"][condname]["cond_dict"] = get_conditions_from_configuration(action=KW.change_val+"_if", section=condsettings) - # printanyway(f'creating workdict for {condname} value {state.vars.indicators[indname]["cp"]["conditions"][condname]["cond_dict"]}') - - #TODO - podporit i jine nez custom? - - detail = RunArchiveDetail(**val) #print("toto jsme si dotahnuli", detail.bars) @@ -1192,7 +1206,7 @@ def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator): ##dame nastaveni indikatoru do tvaru, ktery stratvars ocekava (pro dynmaicke inicializace) stratvars = AttributeDict(indicators=AttributeDict(**{jmeno:toml_parsed})) - print("stratvars", stratvars) + #print("stratvars", stratvars) state = StrategyState(name="XX", symbol = "X", stratvars = AttributeDict(**stratvars), interface=interface) @@ -1203,6 +1217,10 @@ def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator): state.bars = new_bars state.indicators = new_inds + #pridavame dailyBars z extData + if hasattr(detail, "ext_data") and "dailyBars" in detail.ext_data: + state.dailyBars = detail.ext_data["dailyBars"] + #print("daiyl bars added to state.dailyBars", state.dailyBars) print("delka",len(detail.bars["close"])) #intitialize indicator mapping - in order to use eval in expression @@ -1230,7 +1248,8 @@ def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator): for key in detail.indicators[0]: state.indicators[key].append(detail.indicators[0][key][i]) - new_inds[indicator.name].append(0) + #inicializujeme 0 v novém indikatoru + state.indicators[indicator.name].append(0) try: populate_dynamic_indicators(new_data, state) @@ -1241,9 +1260,8 @@ def preview_indicator_byTOML(id: UUID, indicator: InstantIndicator): print(str(e) + format_exc()) - print("Done - static", f"delka {len(state.indicators[indicator.name])}", state.indicators[indicator.name]) - #print("Done", f"delka {len(new_inds[indicator.name])}", new_inds[indicator.name]) - + #print("Done", state.indicators[indicator.name]) + new_inds[indicator.name] = state.indicators[indicator.name] #ukládáme do ArchRunneru diff --git a/v2realbot/indicators/moving_averages.py b/v2realbot/indicators/moving_averages.py index 745d218..d35616f 100644 --- a/v2realbot/indicators/moving_averages.py +++ b/v2realbot/indicators/moving_averages.py @@ -59,6 +59,7 @@ def zlema(data: Any, period: int = 50, use_series=False) -> Any: return pd.Series(zlema) if use_series else zlema + def sma(data: Any, period: int = 50, use_series=False) -> Any: """ Finding the moving average of a dataset @@ -80,8 +81,14 @@ def hma(data: Any, period: int = 50, use_series=False) -> Any: hma = ti.hma(data, period) return pd.Series(hma) if use_series else hma +def linreg(data: Any, period: int = 50, use_series=False) -> Any: + if check_series(data): + use_series = True + data = convert_to_numpy(data) + linreg = ti.linreg(data, period) + return pd.Series(linreg) if use_series else linreg -def kaufman_adaptive_ma(data: Any, period: int = 50, use_series=False) -> Any: +def kama(data: Any, period: int = 50, use_series=False) -> Any: if check_series(data): use_series = True data = convert_to_numpy(data) diff --git a/v2realbot/loader/aggregator.py b/v2realbot/loader/aggregator.py index f466f78..db4ccf1 100644 --- a/v2realbot/loader/aggregator.py +++ b/v2realbot/loader/aggregator.py @@ -369,6 +369,11 @@ class TradeAggregator2Queue(TradeAggregator): self.queue = queue self.symbol = symbol + #accepts loaded queue and sents it to given output + async def ingest_cached(self, cached_queue): + for element in cached_queue: + self.queue.put(element) + async def ingest_trade(self, data): #print("ingest ve threadu:",current_thread().name) res = await super().ingest_trade(data, self.symbol) @@ -400,6 +405,11 @@ class TradeAggregator2List(TradeAggregator): # if os.path.exists(self.debugfile): # os.remove(self.debugfile) + #accepts loaded queue and sents it to given output + async def ingest_cached(self, cached_queue): + for element in cached_queue: + self.btdata.append((element['t'],element['p'])) + async def ingest_trade(self, data): #print("ted vstoupil do tradeagg2list ingestu") res1 = await super().ingest_trade(data, self.symbol) diff --git a/v2realbot/loader/cacher.py b/v2realbot/loader/cacher.py new file mode 100644 index 0000000..edefb91 --- /dev/null +++ b/v2realbot/loader/cacher.py @@ -0,0 +1,63 @@ +from v2realbot.loader.aggregator import TradeAggregator, TradeAggregator2List, TradeAggregator2Queue +from alpaca.trading.requests import GetCalendarRequest +from alpaca.trading.client import TradingClient +from alpaca.data.live import StockDataStream +from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR, OFFLINE_MODE +from alpaca.data.enums import DataFeed +from alpaca.data.historical import StockHistoricalDataClient +from alpaca.data.requests import StockLatestQuoteRequest, StockBarsRequest, StockTradesRequest +from threading import Thread, current_thread +from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, zoneNY, print +from v2realbot.utils.tlog import tlog +from datetime import datetime, timedelta, date +from threading import Thread +import asyncio +from msgpack.ext import Timestamp +from msgpack import packb +from pandas import to_datetime +import pickle +import os +from rich import print +import queue +from alpaca.trading.models import Calendar +from v2realbot.enums.enums import RecordType, StartBarAlign +from datetime import datetime, timedelta +from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, Queue,is_open_hours,zoneNY +from queue import Queue +from rich import print +from v2realbot.enums.enums import Mode +import threading + +class Cacher: + def __init__(self, + + rectype: RecordType = RecordType.BAR, + timeframe: int = 5, + minsize: int = 100, + update_ltp: bool = False, + align: StartBarAlign = StartBarAlign.ROUND, + mintick: int = 0, + exthours: bool = False): +#vstupuje seznam aggregatoru - obvykle 1 pro queue, 1 pro backtest engine +def get_cached_agg_data(agg_list, open, close): + file_path = DATA_DIR + "/cache/"+populate_file_name(agg_list[0], open, close) + + if os.path.exists(file_path): + ##denní file existuje + #loadujeme ze souboru + #pokud je start_time < trade < end_time + #odesíláme do queue + #jinak pass + with open (file_path, 'rb') as fp: + agg_data = pickle.load(fp) + print("Loading AGGREGATED DATA from CACHE", file_path) + + return agg_data + +def store_cache_agg_data(aggregator, open, close): + pass + #ulozi data do fajlu + +def populate_file_name(aggregator, open, close): + aggregated_file = aggregator.symbol + '-' + str(aggregator.rectype) + "-" + aggregator.timeframe + "-" + aggregator.minsize + "-" + aggregator.align + aggregator.mintick + str(aggregator.exthours) + '-' + str(int(open.timestamp())) + '-' + str(int(close.timestamp())) + '.cache' + return aggregated_file \ No newline at end of file diff --git a/v2realbot/loader/trade_offline_streamer.py b/v2realbot/loader/trade_offline_streamer.py index f312200..a5c68d8 100644 --- a/v2realbot/loader/trade_offline_streamer.py +++ b/v2realbot/loader/trade_offline_streamer.py @@ -1,4 +1,5 @@ from v2realbot.loader.aggregator import TradeAggregator, TradeAggregator2List, TradeAggregator2Queue +#from v2realbot.loader.cacher import get_cached_agg_data from alpaca.trading.requests import GetCalendarRequest from alpaca.trading.client import TradingClient from alpaca.data.live import StockDataStream @@ -132,6 +133,21 @@ class Trade_Offline_Streamer(Thread): print("time_to je pred zacatkem marketu. Vynechavame tento den.") continue + + #check if we have aggregated data in cache + + #agg dat found, load it from file + #and call cacher + #trade daily file + + #vstupuje pole agregatoru, open, close daneho dne + #cached_aggregated_data = get_cached_agg_data(self.to_run[symbpole[0]], day.open, day.close) + + # if cached_aggregated_data is not None: + # #poslu agregovana data do ingest cache aggregatorů pro přeposlání do jednotlivých kanálů + + + #trade daily file daily_file = str(symbpole[0]) + '-' + str(int(day.open.timestamp())) + '-' + str(int(day.close.timestamp())) + '.cache' print(daily_file) file_path = DATA_DIR + "/"+daily_file diff --git a/v2realbot/ml/ml.py b/v2realbot/ml/ml.py index c2f6e24..39cfa93 100644 --- a/v2realbot/ml/ml.py +++ b/v2realbot/ml/ml.py @@ -1,388 +1,389 @@ -from sklearn.preprocessing import StandardScaler -from keras.models import Sequential -from v2realbot.enums.enums import PredOutput, Source, TargetTRFM -from v2realbot.config import DATA_DIR -from joblib import dump -import v2realbot.ml.mlutils as mu -from v2realbot.utils.utils import slice_dict_lists -import numpy as np -from copy import deepcopy -import v2realbot.controller.services as cs -#Basic classes for machine learning -#drzi model a jeho zakladni nastaveni +# from sklearn.preprocessing import StandardScaler +# # from keras.models import Sequential +# from v2realbot.enums.enums import PredOutput, Source, TargetTRFM +# from v2realbot.config import DATA_DIR +# from joblib import dump +# # import v2realbot.ml.mlutils as mu +# from v2realbot.utils.utils import slice_dict_lists +# import numpy as np +# from copy import deepcopy +# import v2realbot.controller.services as cs +# #Basic classes for machine learning +# #drzi model a jeho zakladni nastaveni -#Sample Data -sample_bars = { - 'time': [1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15], - 'high': [10, 11, 12, 13, 14,10, 11, 12, 13, 14,10, 11, 12, 13, 14], - 'low': [8, 9, 7, 6, 8,8, 9, 7, 6, 8,8, 9, 7, 6, 8], - 'volume': [1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300], - 'close': [9, 10, 11, 12, 13,9, 10, 11, 12, 13,9, 10, 11, 12, 13], - 'open': [9, 10, 8, 8, 8,9, 10, 8, 8, 8,9, 10, 8, 8, 8], - 'resolution': [1, 1, 1, 1, 1,1, 1, 1, 1, 1,1, 1, 1, 1, 1] -} +# #Sample Data +# sample_bars = { +# 'time': [1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15], +# 'high': [10, 11, 12, 13, 14,10, 11, 12, 13, 14,10, 11, 12, 13, 14], +# 'low': [8, 9, 7, 6, 8,8, 9, 7, 6, 8,8, 9, 7, 6, 8], +# 'volume': [1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300], +# 'close': [9, 10, 11, 12, 13,9, 10, 11, 12, 13,9, 10, 11, 12, 13], +# 'open': [9, 10, 8, 8, 8,9, 10, 8, 8, 8,9, 10, 8, 8, 8], +# 'resolution': [1, 1, 1, 1, 1,1, 1, 1, 1, 1,1, 1, 1, 1, 1] +# } -sample_indicators = { - 'time': [1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15], - 'fastslope': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115], - 'fsdelta': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115], - 'fastslope2': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115], - 'ema': [1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300] -} +# sample_indicators = { +# 'time': [1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15], +# 'fastslope': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115], +# 'fsdelta': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115], +# 'fastslope2': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115], +# 'ema': [1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300] +# } -#Trida, která drzi instanci ML modelu a jeho konfigurace -#take se pouziva jako nastroj na pripravu dat pro train a predikci -#pozor samotna data trida neobsahuje, jen konfiguraci a pak samotny model -class ModelML: - def __init__(self, name: str, - pred_output: PredOutput, - bar_features: list, - ind_features: list, - input_sequences: int, - target: str, - target_reference: str, - train_target_steps: int, #train - train_target_transformation: TargetTRFM, #train - train_epochs: int, #train - train_runner_ids: list = None, #train - train_batch_id: str = None, #train - version: str = "1", - note : str = None, - use_bars: bool = True, - train_remove_cross_sequences: bool = False, #train - #standardne StandardScaler - scalerX: StandardScaler = StandardScaler(), - scalerY: StandardScaler = StandardScaler(), - model: Sequential = Sequential()) -> None: +# #Trida, která drzi instanci ML modelu a jeho konfigurace +# #take se pouziva jako nastroj na pripravu dat pro train a predikci +# #pozor samotna data trida neobsahuje, jen konfiguraci a pak samotny model +# class ModelML: +# def __init__(self, name: str, +# pred_output: PredOutput, +# bar_features: list, +# ind_features: list, +# input_sequences: int, +# target: str, +# target_reference: str, +# train_target_steps: int, #train +# train_target_transformation: TargetTRFM, #train +# train_epochs: int, #train +# train_runner_ids: list = None, #train +# train_batch_id: str = None, #train +# version: str = "1", +# note : str = None, +# use_bars: bool = True, +# train_remove_cross_sequences: bool = False, #train +# #standardne StandardScaler +# scalerX: StandardScaler = StandardScaler(), +# scalerY: StandardScaler = StandardScaler(), +# model, #Sequential = Sequential() +# )-> None: - self.name = name - self.version = version - self.note = note - self.pred_output: PredOutput = pred_output - #model muze byt take bez barů, tzn. jen indikatory - self.use_bars = use_bars - #zajistime poradi - bar_features.sort() - ind_features.sort() - self.bar_features = bar_features - self.ind_features = ind_features - if (train_runner_ids is None or len(train_runner_ids) == 0) and train_batch_id is None: - raise Exception("train_runner_ids nebo train_batch_id musi byt vyplnene") - self.train_runner_ids = train_runner_ids - self.train_batch_id = train_batch_id - #target cílový sloupec, který je používám přímo nebo transformován na binary - self.target = target - self.target_reference = target_reference - self.train_target_steps = train_target_steps - self.train_target_transformation = train_target_transformation - self.input_sequences = input_sequences - self.train_epochs = train_epochs - #keep cross sequences between runners - self.train_remove_cross_sequences = train_remove_cross_sequences - self.scalerX = scalerX - self.scalerY = scalerY - self.model = model +# self.name = name +# self.version = version +# self.note = note +# self.pred_output: PredOutput = pred_output +# #model muze byt take bez barů, tzn. jen indikatory +# self.use_bars = use_bars +# #zajistime poradi +# bar_features.sort() +# ind_features.sort() +# self.bar_features = bar_features +# self.ind_features = ind_features +# if (train_runner_ids is None or len(train_runner_ids) == 0) and train_batch_id is None: +# raise Exception("train_runner_ids nebo train_batch_id musi byt vyplnene") +# self.train_runner_ids = train_runner_ids +# self.train_batch_id = train_batch_id +# #target cílový sloupec, který je používám přímo nebo transformován na binary +# self.target = target +# self.target_reference = target_reference +# self.train_target_steps = train_target_steps +# self.train_target_transformation = train_target_transformation +# self.input_sequences = input_sequences +# self.train_epochs = train_epochs +# #keep cross sequences between runners +# self.train_remove_cross_sequences = train_remove_cross_sequences +# self.scalerX = scalerX +# self.scalerY = scalerY +# self.model = model - def save(self): - filename = mu.get_full_filename(self.name,self.version) - dump(self, filename) - print(f"model {self.name} save") +# def save(self): +# filename = mu.get_full_filename(self.name,self.version) +# dump(self, filename) +# print(f"model {self.name} save") - #create X data with features - def column_stack_source(self, bars, indicators, verbose = 1) -> np.array: - #create SOURCE DATA with features - # bars and indicators dictionary and features as input - poradi_sloupcu_inds = [feature for feature in self.ind_features if feature in indicators] - indicator_data = np.column_stack([indicators[feature] for feature in self.ind_features if feature in indicators]) +# #create X data with features +# def column_stack_source(self, bars, indicators, verbose = 1) -> np.array: +# #create SOURCE DATA with features +# # bars and indicators dictionary and features as input +# poradi_sloupcu_inds = [feature for feature in self.ind_features if feature in indicators] +# indicator_data = np.column_stack([indicators[feature] for feature in self.ind_features if feature in indicators]) - if len(bars)>0: - bar_data = np.column_stack([bars[feature] for feature in self.bar_features if feature in bars]) - poradi_sloupcu_bars = [feature for feature in self.bar_features if feature in bars] - if verbose == 1: - print("poradi sloupce v source_data", str(poradi_sloupcu_bars + poradi_sloupcu_inds)) - combined_day_data = np.column_stack([bar_data,indicator_data]) - else: - combined_day_data = indicator_data - if verbose == 1: - print("poradi sloupce v source_data", str(poradi_sloupcu_inds)) - return combined_day_data +# if len(bars)>0: +# bar_data = np.column_stack([bars[feature] for feature in self.bar_features if feature in bars]) +# poradi_sloupcu_bars = [feature for feature in self.bar_features if feature in bars] +# if verbose == 1: +# print("poradi sloupce v source_data", str(poradi_sloupcu_bars + poradi_sloupcu_inds)) +# combined_day_data = np.column_stack([bar_data,indicator_data]) +# else: +# combined_day_data = indicator_data +# if verbose == 1: +# print("poradi sloupce v source_data", str(poradi_sloupcu_inds)) +# return combined_day_data - #create TARGET(Y) data - def column_stack_target(self, bars, indicators) -> np.array: - target_base = [] - target_reference = [] - try: - try: - target_base = bars[self.target] - except KeyError: - target_base = indicators[self.target] - try: - target_reference = bars[self.target_reference] - except KeyError: - target_reference = indicators[self.target_reference] - except KeyError: - pass - target_day_data = np.column_stack([target_base, target_reference]) - return target_day_data +# #create TARGET(Y) data +# def column_stack_target(self, bars, indicators) -> np.array: +# target_base = [] +# target_reference = [] +# try: +# try: +# target_base = bars[self.target] +# except KeyError: +# target_base = indicators[self.target] +# try: +# target_reference = bars[self.target_reference] +# except KeyError: +# target_reference = indicators[self.target_reference] +# except KeyError: +# pass +# target_day_data = np.column_stack([target_base, target_reference]) +# return target_day_data - def load_runners_as_list(self, runner_id_list = None, batch_id = None): - """Loads all runners data (bars, indicators) for given runners into list of dicts. +# def load_runners_as_list(self, runner_id_list = None, batch_id = None): +# """Loads all runners data (bars, indicators) for given runners into list of dicts. - List of runners/train_batch_id may be provided, or self.train_runner_ids/train_batch_id is taken instead. +# List of runners/train_batch_id may be provided, or self.train_runner_ids/train_batch_id is taken instead. - Returns: - tuple (barslist, indicatorslist,) - lists with dictionaries for each runner - """ - if runner_id_list is not None: - runner_ids = runner_id_list - print("loading runners for ",str(runner_id_list)) - elif batch_id is not None: - print("Loading runners for train_batch_id:", batch_id) - res, runner_ids = cs.get_archived_runnerslist_byBatchID(batch_id) - elif self.train_batch_id is not None: - print("Loading runners for TRAINING BATCH self.train_batch_id:", self.train_batch_id) - res, runner_ids = cs.get_archived_runnerslist_byBatchID(self.train_batch_id) - #pripadne bereme z listu runneru - else: - runner_ids = self.train_runner_ids - print("loading runners for TRAINING runners ",str(self.train_runner_ids)) +# Returns: +# tuple (barslist, indicatorslist,) - lists with dictionaries for each runner +# """ +# if runner_id_list is not None: +# runner_ids = runner_id_list +# print("loading runners for ",str(runner_id_list)) +# elif batch_id is not None: +# print("Loading runners for train_batch_id:", batch_id) +# res, runner_ids = cs.get_archived_runnerslist_byBatchID(batch_id) +# elif self.train_batch_id is not None: +# print("Loading runners for TRAINING BATCH self.train_batch_id:", self.train_batch_id) +# res, runner_ids = cs.get_archived_runnerslist_byBatchID(self.train_batch_id) +# #pripadne bereme z listu runneru +# else: +# runner_ids = self.train_runner_ids +# print("loading runners for TRAINING runners ",str(self.train_runner_ids)) - barslist = [] - indicatorslist = [] - ind_keys = None - for runner_id in runner_ids: - bars, indicators = mu.load_runner(runner_id) - print(f"runner:{runner_id}") - if self.use_bars: - barslist.append(bars) - print(f"bars keys {len(bars)} lng {len(bars[self.bar_features[0]])}") - indicatorslist.append(indicators) - print(f"indi keys {len(indicators)} lng {len(indicators[self.ind_features[0]])}") - if ind_keys is not None and ind_keys != len(indicators): - raise Exception("V runnerech musi byt stejny pocet indikatoru") - else: - ind_keys = len(indicators) +# barslist = [] +# indicatorslist = [] +# ind_keys = None +# for runner_id in runner_ids: +# bars, indicators = mu.load_runner(runner_id) +# print(f"runner:{runner_id}") +# if self.use_bars: +# barslist.append(bars) +# print(f"bars keys {len(bars)} lng {len(bars[self.bar_features[0]])}") +# indicatorslist.append(indicators) +# print(f"indi keys {len(indicators)} lng {len(indicators[self.ind_features[0]])}") +# if ind_keys is not None and ind_keys != len(indicators): +# raise Exception("V runnerech musi byt stejny pocet indikatoru") +# else: +# ind_keys = len(indicators) - return barslist, indicatorslist +# return barslist, indicatorslist - #toto nejspis rozdelit na TRAIN mod (kdy ma smysl si brat nataveni napr. remove cross) - def create_sequences(self, combined_data, target_data = None, remove_cross_sequences: bool = False, rows_in_day = None): - """Creates sequences of given length seq and optionally target N steps in the future. +# #toto nejspis rozdelit na TRAIN mod (kdy ma smysl si brat nataveni napr. remove cross) +# def create_sequences(self, combined_data, target_data = None, remove_cross_sequences: bool = False, rows_in_day = None): +# """Creates sequences of given length seq and optionally target N steps in the future. - Returns X(source) a Y(transformed target) - vrací take Y_untransformed - napr. referencni target column pro zobrazeni v grafu (napr. cenu) +# Returns X(source) a Y(transformed target) - vrací take Y_untransformed - napr. referencni target column pro zobrazeni v grafu (napr. cenu) - Volby pro transformaci targetu: - - KEEPVAL (keep value as is) - - KEEPVAL_MOVE(keep value, move target N steps in the future) +# Volby pro transformaci targetu: +# - KEEPVAL (keep value as is) +# - KEEPVAL_MOVE(keep value, move target N steps in the future) - další na zámysl (nejspíš ale data budu připravovat ve stratu a využívat jen KEEPy nahoře) - - BINARY_prefix - sloupec založený na podmínce, výsledek je 0,1 - - BINARY_TREND RISING - podmínka založena, že v target columnu stoupají/klesají po target N steps - (podvarianty BINARY TREND RISING(0-1), FALLING(0-1), BOTH(-1 - )) - - BINARY_READY - předpřipravený sloupec(vytvořený ve strategii jako indikator), stačí jen posunout o target step - - BINARY_READY_POSUNUTY - předpřipraveny sloupec (již posunutýo o target M) - stačí brát as is +# další na zámysl (nejspíš ale data budu připravovat ve stratu a využívat jen KEEPy nahoře) +# - BINARY_prefix - sloupec založený na podmínce, výsledek je 0,1 +# - BINARY_TREND RISING - podmínka založena, že v target columnu stoupají/klesají po target N steps +# (podvarianty BINARY TREND RISING(0-1), FALLING(0-1), BOTH(-1 - )) +# - BINARY_READY - předpřipravený sloupec(vytvořený ve strategii jako indikator), stačí jen posunout o target step +# - BINARY_READY_POSUNUTY - předpřipraveny sloupec (již posunutýo o target M) - stačí brát as is - Args: - combined_data: A list of combined data. - target_data: A list of target data (0-target,1-target ref.column) - remove_cross_sequences: If to remove crossday sequences - rows_in_day: helper dict to remove crossday sequences - return_untr: whether to return untransformed reference column +# Args: +# combined_data: A list of combined data. +# target_data: A list of target data (0-target,1-target ref.column) +# remove_cross_sequences: If to remove crossday sequences +# rows_in_day: helper dict to remove crossday sequences +# return_untr: whether to return untransformed reference column - Returns: - A list of X sequences and a list of y sequences. - """ +# Returns: +# A list of X sequences and a list of y sequences. +# """ - if remove_cross_sequences is True and rows_in_day is None: - raise Exception("To remove crossday sequences, rows_in_day param required.") +# if remove_cross_sequences is True and rows_in_day is None: +# raise Exception("To remove crossday sequences, rows_in_day param required.") - if target_data is not None and len(target_data) > 0: - target_data_untr = target_data[:,1] - target_data = target_data[:,0] - else: - target_data_untr = [] - target_data = [] +# if target_data is not None and len(target_data) > 0: +# target_data_untr = target_data[:,1] +# target_data = target_data[:,0] +# else: +# target_data_untr = [] +# target_data = [] - X_train = [] - y_train = [] - y_untr = [] - #comb data shape (4073, 13) - #target shape (4073, 1) - print("Start Sequencing") - #range sekvence podle toho jestli je pozadovan MOVE nebo NE - if self.train_target_transformation == TargetTRFM.KEEPVAL_MOVE: - right_offset = self.input_sequences + self.train_target_steps - else: - right_offset= self.input_sequences - for i in range(len(combined_data) - right_offset): +# X_train = [] +# y_train = [] +# y_untr = [] +# #comb data shape (4073, 13) +# #target shape (4073, 1) +# print("Start Sequencing") +# #range sekvence podle toho jestli je pozadovan MOVE nebo NE +# if self.train_target_transformation == TargetTRFM.KEEPVAL_MOVE: +# right_offset = self.input_sequences + self.train_target_steps +# else: +# right_offset= self.input_sequences +# for i in range(len(combined_data) - right_offset): - #take neresime cross sekvence kdyz neni vyplneni target nebo neni vyplnena rowsinaday - if remove_cross_sequences is True and not self.is_same_day(i,i + right_offset, rows_in_day): - print(f"sekvence vyrazena. NEW Zacatek {combined_data[i, 0]} konec {combined_data[i + right_offset, 0]}") - continue +# #take neresime cross sekvence kdyz neni vyplneni target nebo neni vyplnena rowsinaday +# if remove_cross_sequences is True and not self.is_same_day(i,i + right_offset, rows_in_day): +# print(f"sekvence vyrazena. NEW Zacatek {combined_data[i, 0]} konec {combined_data[i + right_offset, 0]}") +# continue - #pridame sekvenci - X_train.append(combined_data[i:i + self.input_sequences]) +# #pridame sekvenci +# X_train.append(combined_data[i:i + self.input_sequences]) - #target hodnotu bude ponecha (na radku mame jiz cilovy target) - #nebo vezme hodnotu z N(train_target_steps) baru vpredu a da jako target k radku - #je rizeno nastavenim right_offset vyse - if target_data is not None and len(target_data) > 0: - y_train.append(target_data[i + right_offset]) +# #target hodnotu bude ponecha (na radku mame jiz cilovy target) +# #nebo vezme hodnotu z N(train_target_steps) baru vpredu a da jako target k radku +# #je rizeno nastavenim right_offset vyse +# if target_data is not None and len(target_data) > 0: +# y_train.append(target_data[i + right_offset]) - #udela binary transformaci targetu - # elif self.target_transformation == TargetTRFM.BINARY_TREND_UP: - # #mini loop od 0 do počtu target steps - zda jsou successively rising - # #radeji budu resit vizualne conditional indikatorem pri priprave dat - # rising = False - # for step in range(0,self.train_target_steps): - # if target_data[i + self.input_sequences + step] < target_data[i + self.input_sequences + step + 1]: - # rising = True - # else: - # rising = False - # break - # y_train.append([1] if rising else [0]) - # #tato zakomentovana varianta porovnava jen cenu ted a cenu na target baru - # #y_train.append([1] if target_data[i + self.input_sequences] < target_data[i + self.input_sequences + self.train_target_steps] else [0]) - if target_data is not None and len(target_data) > 0: - y_untr.append(target_data_untr[i + self.input_sequences]) - return np.array(X_train), np.array(y_train), np.array(y_untr) +# #udela binary transformaci targetu +# # elif self.target_transformation == TargetTRFM.BINARY_TREND_UP: +# # #mini loop od 0 do počtu target steps - zda jsou successively rising +# # #radeji budu resit vizualne conditional indikatorem pri priprave dat +# # rising = False +# # for step in range(0,self.train_target_steps): +# # if target_data[i + self.input_sequences + step] < target_data[i + self.input_sequences + step + 1]: +# # rising = True +# # else: +# # rising = False +# # break +# # y_train.append([1] if rising else [0]) +# # #tato zakomentovana varianta porovnava jen cenu ted a cenu na target baru +# # #y_train.append([1] if target_data[i + self.input_sequences] < target_data[i + self.input_sequences + self.train_target_steps] else [0]) +# if target_data is not None and len(target_data) > 0: +# y_untr.append(target_data_untr[i + self.input_sequences]) +# return np.array(X_train), np.array(y_train), np.array(y_untr) - def is_same_day(self, idx_start, idx_end, rows_in_day): - """Helper for sequencing enables to recognize if the start/end index are from the same day. +# def is_same_day(self, idx_start, idx_end, rows_in_day): +# """Helper for sequencing enables to recognize if the start/end index are from the same day. - Used for sequences to remove cross runner(day) sequences. +# Used for sequences to remove cross runner(day) sequences. - Args: - idx_start: Start index - idx_end: End index - rows_in_day: 1D array containing number of rows(bars,inds) for each day. - Cumsumed defines edges where each day ends. [10,30,60] +# Args: +# idx_start: Start index +# idx_end: End index +# rows_in_day: 1D array containing number of rows(bars,inds) for each day. +# Cumsumed defines edges where each day ends. [10,30,60] - Returns: - A boolean +# Returns: +# A boolean - refactor to vectors if possible - i_b, i_e - podm_pole = i_b= pole - [10,30,60] - """ - for i in rows_in_day: - #jde o polozku na pomezi - vyhazujeme - if idx_start < i and idx_end >= i: - return False - if idx_start < i and idx_end < i: - return True - return None +# refactor to vectors if possible +# i_b, i_e +# podm_pole = i_b= pole +# [10,30,60] +# """ +# for i in rows_in_day: +# #jde o polozku na pomezi - vyhazujeme +# if idx_start < i and idx_end >= i: +# return False +# if idx_start < i and idx_end < i: +# return True +# return None - #vytvori X a Y data z nastaveni self - #pro vybrane runnery stahne data, vybere sloupce dle faature a target - #a vrátí jako sloupce v numpy poli - #zaroven vraci i rows_in_day pro nasledny sekvencing - def load_data(self, runners_ids: list = None, batch_id: list = None, source: Source = Source.RUNNERS): - """Service to load data for the model. Can be used for training or for vector prediction. +# #vytvori X a Y data z nastaveni self +# #pro vybrane runnery stahne data, vybere sloupce dle faature a target +# #a vrátí jako sloupce v numpy poli +# #zaroven vraci i rows_in_day pro nasledny sekvencing +# def load_data(self, runners_ids: list = None, batch_id: list = None, source: Source = Source.RUNNERS): +# """Service to load data for the model. Can be used for training or for vector prediction. - If input data are not provided, it will get the value from training model configuration (train_runners_ids, train_batch_id) +# If input data are not provided, it will get the value from training model configuration (train_runners_ids, train_batch_id) - Args: - runner_ids: - batch_id: - source: To load sample data. +# Args: +# runner_ids: +# batch_id: +# source: To load sample data. - Returns: - source_data,target_data,rows_in_day - """ - rows_in_day = [] - indicatorslist = [] - #bud natahneme samply - if source == Source.SAMPLES: - if self.use_bars: - bars = sample_bars - else: - bars = {} - indicators = sample_indicators - indicatorslist.append(indicators) - #nebo dotahneme pozadovane runnery - else: - #nalodujeme vsechny runnery jako listy (bud z runnerids nebo dle batchid) - barslist, indicatorslist = self.load_runners_as_list(runner_id_list=runners_ids, batch_id=batch_id) - #nerozumim - bl = deepcopy(barslist) - il = deepcopy(indicatorslist) - #a zmergujeme jejich data dohromady - bars = mu.merge_dicts(bl) - indicators = mu.merge_dicts(il) +# Returns: +# source_data,target_data,rows_in_day +# """ +# rows_in_day = [] +# indicatorslist = [] +# #bud natahneme samply +# if source == Source.SAMPLES: +# if self.use_bars: +# bars = sample_bars +# else: +# bars = {} +# indicators = sample_indicators +# indicatorslist.append(indicators) +# #nebo dotahneme pozadovane runnery +# else: +# #nalodujeme vsechny runnery jako listy (bud z runnerids nebo dle batchid) +# barslist, indicatorslist = self.load_runners_as_list(runner_id_list=runners_ids, batch_id=batch_id) +# #nerozumim +# bl = deepcopy(barslist) +# il = deepcopy(indicatorslist) +# #a zmergujeme jejich data dohromady +# bars = mu.merge_dicts(bl) +# indicators = mu.merge_dicts(il) - #zaroven vytvarime pomocny list, kde stale drzime pocet radku per day (pro nasledny sekvencing) - #zatim nad indikatory - v budoucnu zvazit, kdyby jelo neco jen nad barama - for i, val in enumerate(indicatorslist): - #pro prvni klic z indikatoru pocteme cnt - pocet = len(indicatorslist[i][self.ind_features[0]]) - print("pro runner vkladame pocet", pocet) - rows_in_day.append(pocet) +# #zaroven vytvarime pomocny list, kde stale drzime pocet radku per day (pro nasledny sekvencing) +# #zatim nad indikatory - v budoucnu zvazit, kdyby jelo neco jen nad barama +# for i, val in enumerate(indicatorslist): +# #pro prvni klic z indikatoru pocteme cnt +# pocet = len(indicatorslist[i][self.ind_features[0]]) +# print("pro runner vkladame pocet", pocet) +# rows_in_day.append(pocet) - rows_in_day = np.array(rows_in_day) - rows_in_day = np.cumsum(rows_in_day) - print("celkove pole rows_in_day(cumsum):", rows_in_day) +# rows_in_day = np.array(rows_in_day) +# rows_in_day = np.cumsum(rows_in_day) +# print("celkove pole rows_in_day(cumsum):", rows_in_day) - print("Data LOADED.") - print(f"number of indicators {len(indicators)}") - print(f"number of bar elements{len(bars)}") - print(f"ind list length {len(indicators['time'])}") - print(f"bar list length {len(bars['time'])}") +# print("Data LOADED.") +# print(f"number of indicators {len(indicators)}") +# print(f"number of bar elements{len(bars)}") +# print(f"ind list length {len(indicators['time'])}") +# print(f"bar list length {len(bars['time'])}") - self.validate_available_features(bars, indicators) +# self.validate_available_features(bars, indicators) - print("Preparing FEATURES") - source_data, target_data = self.stack_bars_indicators(bars, indicators) - return source_data, target_data, rows_in_day +# print("Preparing FEATURES") +# source_data, target_data = self.stack_bars_indicators(bars, indicators) +# return source_data, target_data, rows_in_day - def validate_available_features(self, bars, indicators): - for k in self.bar_features: - if not k in bars.keys(): - raise Exception(f"Missing bar feature {k}") +# def validate_available_features(self, bars, indicators): +# for k in self.bar_features: +# if not k in bars.keys(): +# raise Exception(f"Missing bar feature {k}") - for k in self.ind_features: - if not k in indicators.keys(): - raise Exception(f"Missing ind feature {k}") +# for k in self.ind_features: +# if not k in indicators.keys(): +# raise Exception(f"Missing ind feature {k}") - def stack_bars_indicators(self, bars, indicators): - print("Stacking dicts to numpy") - print("Source - X") - source_data = self.column_stack_source(bars, indicators) - print("shape", np.shape(source_data)) - print("Target - Y", self.target) - target_data = self.column_stack_target(bars, indicators) - print("shape", np.shape(target_data)) +# def stack_bars_indicators(self, bars, indicators): +# print("Stacking dicts to numpy") +# print("Source - X") +# source_data = self.column_stack_source(bars, indicators) +# print("shape", np.shape(source_data)) +# print("Target - Y", self.target) +# target_data = self.column_stack_target(bars, indicators) +# print("shape", np.shape(target_data)) - return source_data, target_data +# return source_data, target_data - #pomocna sluzba, ktera provede vsechny transformace a inverzni scaling a vyleze z nej predikce - #vstupem je standardni format ve strategii (state.bars, state.indicators) - #vystupem je jedna hodnota - def predict(self, bars, indicators) -> float: - #oriznuti podle seqence - pokud je nastaveno v modelu - lastNbars = slice_dict_lists(bars, self.input_sequences) - lastNindicators = slice_dict_lists(indicators, self.input_sequences) - # print("last5bars", lastNbars) - # print("last5indicators",lastNindicators) +# #pomocna sluzba, ktera provede vsechny transformace a inverzni scaling a vyleze z nej predikce +# #vstupem je standardni format ve strategii (state.bars, state.indicators) +# #vystupem je jedna hodnota +# def predict(self, bars, indicators) -> float: +# #oriznuti podle seqence - pokud je nastaveno v modelu +# lastNbars = slice_dict_lists(bars, self.input_sequences) +# lastNindicators = slice_dict_lists(indicators, self.input_sequences) +# # print("last5bars", lastNbars) +# # print("last5indicators",lastNindicators) - combined_live_data = self.column_stack_source(lastNbars, lastNindicators, verbose=0) - #print("combined_live_data",combined_live_data) - combined_live_data = self.scalerX.transform(combined_live_data) - combined_live_data = np.array(combined_live_data) - #print("last 5 values combined data shape", np.shape(combined_live_data)) +# combined_live_data = self.column_stack_source(lastNbars, lastNindicators, verbose=0) +# #print("combined_live_data",combined_live_data) +# combined_live_data = self.scalerX.transform(combined_live_data) +# combined_live_data = np.array(combined_live_data) +# #print("last 5 values combined data shape", np.shape(combined_live_data)) - #converts to 3D array - # 1 number of samples in the array. - # 2 represents the sequence length. - # 3 represents the number of features in the data. - combined_live_data = combined_live_data.reshape((1, self.input_sequences, combined_live_data.shape[1])) +# #converts to 3D array +# # 1 number of samples in the array. +# # 2 represents the sequence length. +# # 3 represents the number of features in the data. +# combined_live_data = combined_live_data.reshape((1, self.input_sequences, combined_live_data.shape[1])) - # Make a prediction - prediction = self.model(combined_live_data, training=False) - #prediction = prediction.reshape((1, 1)) - # Convert the prediction back to the original scale - prediction = self.scalerY.inverse_transform(prediction) - return float(prediction) +# # Make a prediction +# prediction = self.model(combined_live_data, training=False) +# #prediction = prediction.reshape((1, 1)) +# # Convert the prediction back to the original scale +# prediction = self.scalerY.inverse_transform(prediction) +# return float(prediction) diff --git a/v2realbot/ml/mlutils.py b/v2realbot/ml/mlutils.py index a8a561e..5207e69 100644 --- a/v2realbot/ml/mlutils.py +++ b/v2realbot/ml/mlutils.py @@ -1,5 +1,5 @@ import numpy as np -import v2realbot.controller.services as cs +# import v2realbot.controller.services as cs from joblib import load from v2realbot.config import DATA_DIR diff --git a/v2realbot/static/index.html b/v2realbot/static/index.html index 289c95f..a47ff46 100644 --- a/v2realbot/static/index.html +++ b/v2realbot/static/index.html @@ -17,8 +17,8 @@ - - + @@ -156,10 +156,10 @@
-
+
-
+
@@ -296,6 +296,7 @@ +
@@ -406,6 +407,7 @@ +
diff --git a/v2realbot/static/js/archivechart.js b/v2realbot/static/js/archivechart.js index ae0e331..3fd6cff 100644 --- a/v2realbot/static/js/archivechart.js +++ b/v2realbot/static/js/archivechart.js @@ -109,6 +109,43 @@ function transform_data(data) { transformed["bars"] = bars transformed["vwap"] = vwap transformed["volume"] = volume + var bars = [] + var volume = [] + var vwap = [] + + + if ((data.ext_data !== null) && (data.ext_data.dailyBars)) { + data.ext_data.dailyBars.time.forEach((element, index, array) => { + sbars = {}; + svolume = {}; + svwap = {}; + + sbars["time"] = element; + sbars["close"] = data.ext_data.dailyBars.close[index] + sbars["open"] = data.ext_data.dailyBars.open[index] + sbars["high"] = data.ext_data.dailyBars.high[index] + sbars["low"] = data.ext_data.dailyBars.low[index] + + + svwap["time"] = element + svwap["value"] = data.ext_data.dailyBars.vwap[index] + + svolume["time"] = element + svolume["value"] = data.ext_data.dailyBars.volume[index] + + bars.push(sbars) + vwap.push(svwap) + volume.push(svolume) + }); + transformed["dailyBars"] = {} + transformed["dailyBars"]["bars"] = bars + transformed["dailyBars"]["vwap"] = vwap + transformed["dailyBars"]["volume"] = volume + var bars = [] + var volume = [] + var vwap = [] + } + //get markers - avgp line for all buys var avgp_buy_line = [] @@ -561,6 +598,20 @@ function chart_indicators(data, visible, offset) { } }) } + + indList.sort((a, b) => { + const nameA = a.name.toUpperCase(); // ignore upper and lowercase + const nameB = b.name.toUpperCase(); // ignore upper and lowercase + if (nameA < nameB) { + return -1; + } + if (nameA > nameB) { + return 1; + } + // names must be equal + return 0; + + }); //vwap a volume zatim jen v detailnim zobrazeni if (!offset) { //display vwap and volume @@ -854,15 +905,26 @@ function chart_archived_run(archRecord, data, oneMinuteBars) { //console.log("native", native_resolution) //available intervals zatim jen 1m - var intervals = [data.native_resolution, '1m']; + var intervals = [data.native_resolution, '1m', '1d']; + + var dailyData = null + if (transformed_data["dailyBars"]) { + dailyData = transformed_data["dailyBars"]["bars"] + } + //zkusime daily data dat do minuty + //console.log("daily", dailyData) + nativeData = transformed_data["bars"] + //console.log("native") + //get one minute data //tbd prepare volume //console.log("oneMinuteData",oneMinuteBars) data["AllCandleSeriesesData"] = new Map([ [data.native_resolution, nativeData ], - ["1m", oneMinuteBars ], + ["1m", dailyData?dailyData.concat(oneMinuteBars):oneMinuteBars], + ["1d", dailyData ], ]); //dame si data do globalni, abychom je mohli pouzivat jinde (trochu prasarna, predelat pak) @@ -970,8 +1032,7 @@ function chart_archived_run(archRecord, data, oneMinuteBars) { $("#statusAccount").text(archRecord.account) $("#statusIlog").text("Logged:" + archRecord.ilog_save) $("#statusStratvars").text(((archRecord.strat_json)?archRecord.strat_json:archRecord.stratvars),null,2) - $("#statusSettings").text(JSON.stringify(archRecord.metrics,null,2) + " " + JSON.stringify(archRecord.settings,null,2)) - + $("#statusSettings").text(JSON.stringify(archRecord.metrics,null,2) + " " + JSON.stringify(archRecord.settings,null,2)+ JSON.stringify(data.ext_data,null,2)) //TBD other dynamically created indicators } diff --git a/v2realbot/static/js/archivetables.js b/v2realbot/static/js/archivetables.js index bfa6ec5..84d8193 100644 --- a/v2realbot/static/js/archivetables.js +++ b/v2realbot/static/js/archivetables.js @@ -43,6 +43,19 @@ function refresh_arch_and_callback(row, callback) { $(document).ready(function () { archiveRecords.ajax.reload(); + + //button select page + $('#button_selpage').click(function () { + if ($('#button_selpage').hasClass('active')) { + $('#button_selpage').removeClass('active'); + archiveRecords.rows().deselect(); + } + else { + $('#button_selpage').addClass('active'); + archiveRecords.rows( { page: 'current' } ).select(); + } + }); + //button clear log $('#button_clearlog').click(function () { $('#lines').empty(); @@ -454,7 +467,8 @@ $(document).ready(function () { if (record1.bt_to == "") {delete record1["bt_to"];} //mazeme, pouze rerunujeme single - record1["test_batch_id"]; + delete record1["test_batch_id"]; + delete record1["batch_id"]; const rec = new Object() rec.id2 = parseInt(stratData.id2); diff --git a/v2realbot/static/js/instantindicators.js b/v2realbot/static/js/instantindicators.js index 755c7be..a15ddef 100644 --- a/v2realbot/static/js/instantindicators.js +++ b/v2realbot/static/js/instantindicators.js @@ -28,7 +28,8 @@ $(document).ready(function () { obj = new Object() obj.runner_id = runner_id - obj.toml = TOML.parse(ind_editor.getValue()) + // obj.toml = TOML.parse(ind_editor.getValue()) + obj.toml = "" obj.name = indname jsonString = JSON.stringify(obj); //console.log("pred odeslanim",jsonString) diff --git a/v2realbot/static/js/mytables.js b/v2realbot/static/js/mytables.js index 3d181fc..c94142a 100644 --- a/v2realbot/static/js/mytables.js +++ b/v2realbot/static/js/mytables.js @@ -197,6 +197,23 @@ $(document).ready(function () { }); + //button filter selected strat + $('#button_filter_strat').click(function () { + if ($('#button_filter_strat').hasClass('active')) { + $('#button_filter_strat').removeClass('active'); + archiveRecords.columns().search("").draw(); + console.log("draw") + } + else { + row = stratinRecords.row('.selected').data(); + if (row) { + $('#button_filter_strat').addClass('active'); + archiveRecords.column(1).search(row.id).draw(); + console.log("filteredon",row.id) + } + } + }); + //button get historical trades $('#bt-trade').click(function () { event.preventDefault(); diff --git a/v2realbot/static/js/utils.js b/v2realbot/static/js/utils.js index 606fea7..07ae829 100644 --- a/v2realbot/static/js/utils.js +++ b/v2realbot/static/js/utils.js @@ -1,11 +1,82 @@ API_KEY = localStorage.getItem("api-key") var chart = null -// var colors = ["#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#00425A","#B5D5C5","#e61957","#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#00425A","#B5D5C5","#e61957"] -// var reset_colors = ["#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#00425A","#B5D5C5","#e61957","#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#00425A","#B5D5C5","#e61957"] -var colors = ["#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#B5D5C5","#e61957","#7B0E60","#9B2888","#BD38A0","#A30F68","#6E0B50","#CA2183","#E6319B","#A04C54","#643848","#CA7474","#E68D8D","#4F9C34","#3B7128","#73DF4D","#95EF65","#A857A4","#824690","#D087CC","#E2A1DF","#79711B","#635D17","#99912B","#B1A73D","#3779C9","#2B68B3","#5599ED","#77A9F7","#004C67","#00687D","#A1C6B5","#8CC6A5","#C9E6D5","#E4F6EA","#D2144A","#A60F3B","#FA2463","#FF3775"]; -var reset_colors = ["#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#B5D5C5","#e61957","#7B0E60","#9B2888","#BD38A0","#A30F68","#6E0B50","#CA2183","#E6319B","#A04C54","#643848","#CA7474","#E68D8D","#4F9C34","#3B7128","#73DF4D","#95EF65","#A857A4","#824690","#D087CC","#E2A1DF","#79711B","#635D17","#99912B","#B1A73D","#3779C9","#2B68B3","#5599ED","#77A9F7","#004C67","#00687D","#A1C6B5","#8CC6A5","#C9E6D5","#E4F6EA","#D2144A","#A60F3B","#FA2463","#FF3775"]; +//puvodni mene vyrazne barvy +// var colors = ["#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#B5D5C5","#e61957","#7B0E60","#9B2888","#BD38A0","#A30F68","#6E0B50","#CA2183","#E6319B","#A04C54","#643848","#CA7474","#E68D8D","#4F9C34","#3B7128","#73DF4D","#95EF65","#A857A4","#824690","#D087CC","#FF3775","#E2A1DF","#79711B","#635D17","#99912B","#B1A73D","#3779C9","#A60F3B","#2B68B3","#5599ED","#77A9F7","#004C67","#00687D","#A1C6B5","#8CC6A5","#C9E6D5","#E4F6EA","#D2144A","#FA2463","#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#B5D5C5","#e61957","#7B0E60","#9B2888","#BD38A0","#A30F68","#6E0B50","#CA2183","#E6319B","#A04C54","#643848","#CA7474","#E68D8D","#4F9C34","#3B7128","#73DF4D","#95EF65","#A857A4","#824690","#D087CC","#FF3775","#E2A1DF","#79711B","#635D17","#99912B","#B1A73D","#3779C9","#A60F3B","#2B68B3","#5599ED","#77A9F7","#004C67","#00687D","#A1C6B5","#8CC6A5","#C9E6D5","#E4F6EA","#D2144A","#FA2463"]; +// var reset_colors = ["#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#B5D5C5","#e61957","#7B0E60","#9B2888","#BD38A0","#A30F68","#6E0B50","#CA2183","#E6319B","#A04C54","#643848","#CA7474","#E68D8D","#4F9C34","#3B7128","#73DF4D","#95EF65","#A857A4","#824690","#D087CC","#FF3775","#E2A1DF","#79711B","#635D17","#99912B","#B1A73D","#3779C9","#A60F3B","#2B68B3","#5599ED","#77A9F7","#004C67","#00687D","#A1C6B5","#8CC6A5","#C9E6D5","#E4F6EA","#D2144A","#FA2463","#8B1874","#B71375","#B46060","#61c740","#BE6DB7","#898121","#4389d9","#B5D5C5","#e61957","#7B0E60","#9B2888","#BD38A0","#A30F68","#6E0B50","#CA2183","#E6319B","#A04C54","#643848","#CA7474","#E68D8D","#4F9C34","#3B7128","#73DF4D","#95EF65","#A857A4","#824690","#D087CC","#FF3775","#E2A1DF","#79711B","#635D17","#99912B","#B1A73D","#3779C9","#A60F3B","#2B68B3","#5599ED","#77A9F7","#004C67","#00687D","#A1C6B5","#8CC6A5","#C9E6D5","#E4F6EA","#D2144A","#FA2463"]; + +// function generateColorPalette(numColors) { +// const palette = []; +// let lastColor = null; +// for (let i = 0; i < numColors; i++) { +// let color = generateRandomColor(); +// while (isColorDark(color) || areColorsTooSimilar(color, lastColor)) { +// color = generateRandomColor(); +// } +// lastColor = color; +// palette.push(color); +// } +// return palette; +// } + +// function generateRandomColor() { +// const letters = '0123456789ABCDEF'; +// let color = '#'; +// for (let i = 0; i < 6; i++) { +// color += letters[Math.floor(Math.random() * 16)]; +// } +// return color; +// } + +// function areColorsTooSimilar(color1, color2) { +// if (!color1 || !color2) { +// return false; +// } +// // Calculate the color difference +// const diff = parseInt(color1.substring(1), 16) - parseInt(color2.substring(1), 16); +// // Define a threshold for color difference (you can adjust this value) +// const threshold = 500; +// return Math.abs(diff) < threshold; +// } + +// function isColorDark(color) { +// const hexColor = color.replace("#", ""); +// const r = parseInt(hexColor.substr(0, 2), 16); +// const g = parseInt(hexColor.substr(2, 2), 16); +// const b = parseInt(hexColor.substr(4, 2), 16); +// const brightness = (r * 299 + g * 587 + b * 114) / 1000; +// return brightness < 128 || brightness > 140; // You can adjust the threshold for what you consider 'dark' +// } + +// colors = generateColorPalette(255) +// reset_colors = colors + +// console.log(`"${colors.join("\", \"")}"`); + +// // pekne vygenrovane pomoci kodu vyse +var colors = ["#63AA57", "#8F8AB0", "#4CAA4E", "#E24AEE", "#D06AA6", "#7891BA", "#A39A34", "#8A94A2", "#8887A7", "#61BB2F", "#FD569D", "#1EB6E1", +"#379AC9", "#FD6F2E", "#8C9858", "#39A4A3", "#6D97F4", "#1ECB01", "#FA5B16", "#A6891C", "#48CF10", "#D27B26", "#D56B55", "#FE3AB8", "#E35C51", +"#EC4FE6", "#E250A3", "#BA618E", "#1BC074", "#C57784", "#888BC5", "#4FA452", "#80885C", "#B97272", "#33BF98", "#B7961D", "#A07284", "#02E54E", +"#AF7F35", "#F852EF", "#6D955B", "#E0676E", "#F73DEC", "#CE53FD", "#9773D3", "#649E81", "#D062CE", "#AB73E7", "#A4729C", "#E76A07", "#E85CCB", +"#A16FB1", "#4BB859", "#B25EE2", "#8580CE", "#A275EF", "#AC9245", "#4D988D", "#B672C9", "#4CA96E", "#C9873E", "#5BB147", "#10C783", "#D7647D", +"#CB893A", "#A586BA", "#28C0A2", "#61A755", "#0EB7C5", "#2DADBC", "#17BB71", "#2BC733", "#2BB890", "#F04EF8", "#699580", "#A88809", "#EB3FF6", +"#A75ED3", "#859171", "#BB6285", "#81A147", "#AD7CD2", "#65B630", "#C9616C", "#BD5EFA", "#7A9F30", "#2AB6AB", "#FC496A", "#687FC7", "#DB40E7", +"#07BCE9", "#509F63", "#EC4FDD", "#A079BE", "#C17297", "#E447C2", "#E95AD9", "#9FA01E", "#7E86CF", "#21E316", "#1CABF9", "#17C24F", "#9C9254", +"#C97994", "#4BA9DA", "#0DD595", "#13BEA8", "#C2855D", "#DF6C13", "#60B370", "#0FC3F6", "#C1830E", "#3AC917", "#0EBBB0", "#CC50B4", "#B768EC", +"#D47F49", "#B47BC5", "#38ADBD", "#05DC53", "#44CD4E", "#838E65", "#49D70F", "#2DADBE", "#2CB0C9", "#DA703E", "#06B5CA", "#7BAF3E", "#918E79", +"#2AA5E5", "#C37F5E", "#07B8C9", "#4CBA27", "#E752C6", "#7F93B2", "#4798CD", "#45AA4C", "#4DB666", "#7683A7", "#758685", "#4B9FAD", "#9280FD", +"#6682DD", "#42ACBE", "#C1609F", "#D850DB", "#649A62", "#54CC22", "#AD81C1", "#BF7A43", "#0FCEA5", "#D06DAF", "#87799B", "#4DA94E", "#2FD654", +"#07D587", "#21CF0C", "#03CF34", "#42C771", "#D563CD", "#6D9E9A", "#C76C59", "#68B368", "#11BCE5", "#0DCFB3", "#9266D8", "#BF67F6", "#88A04E", +"#73BE17", "#67B437", "#8586E4", "#9F8749", "#479CA5", "#CC777E", "#4FAF46", "#9D9836", "#918DAF", "#D167B8", "#6F9DA5", "#2BB167", "#16B8BC", +"#B4861F", "#A08487", "#67B357", "#5CAA5C", "#20CA49", "#D18813", "#15D63F", "#C8618F", "#887E92", "#21C457", "#4EA8CE", "#53BE49", "#5A86D5", +"#BD7E4E", "#27B0A1", "#33CF42", "#709083", "#38A8DE", "#4CA762", "#1EA4FF", "#DE3EE4", "#70A860", "#39A3C8", "#6BBB39", "#F053F4", "#8C7FB5", +"#969F21", "#B19841", "#E57148", "#C25DA7", "#6DA979", "#B27D73", "#7F9786", "#41AC99", "#C58848", "#948F9E", "#6BB620", "#81AB3B", "#09DE44", +"#43A9D2", "#41B0D7", "#20ACAA", "#649FCB", "#CD8345", "#A88669", "#3EA5E7", "#F36A19", "#E06B48", "#8388BD", "#EC6153", "#639082", "#52CA32", +"#878BAA", "#02BCDB", "#828FD9", "#3DC07F", "#29D46A", "#9C7CC1", "#EB7713", "#F95F6A", "#E25F4C", "#589994", "#D45AB7", "#DE66AB", "#B8715F", +"#E850F4", "#FB6420", "#C2832C", "#6383C5", "#D57A58", "#EF652C", "#02D71A", "#ED664D", "#60A526"] + +var reset_colors = colors.slice() var indList = [] var verticalSeries=null @@ -259,7 +330,7 @@ function initialize_chart() { //var chartOptions = { width: 1045, height: 600, leftPriceScale: {visible: true}} //TMAVY MOD - var chartOptions = { width: 1080, + var chartOptions = { width: 1280, height: 600, leftPriceScale: {visible: true}, layout: { @@ -483,6 +554,54 @@ function profitLineToggle() { } } +//togle go wide +function toggleWide() { + width = 2000; + const elem = document.getElementById("goWide"); + const msgContainer = document.getElementById("msgContainer"); + const msgContainerInner = document.getElementById("msgContainerInner"); + const clrButton = document.getElementById("clrButton"); + + if (elem.classList.contains("switcher-active-item")) { + width = 1080; + msgContainer.removeAttribute("style"); + msgContainerInner.removeAttribute("style"); + clrButton.removeAttribute("style"); + } else + { + msgContainer.style.display = "block" + msgContainerInner.style.display = "none" + clrButton.style.display = "none" + } + elem.classList.toggle("switcher-active-item"); + + if (chart) { + chart.applyOptions({ width: width}); + chart.timeScale().fitContent(); + } +} + +//togle profit line +function mrkLineToggle() { + vis = true; + const elem = document.getElementById("mrkLine"); + if (elem.classList.contains("switcher-active-item")) { + vis = false; + } + elem.classList.toggle("switcher-active-item"); + //v ifu kvuli workaroundu + if (markersLine) { + markersLine.applyOptions({ + visible: vis }); + } + if (slLine) { + slLine.forEach((series, index, array) => { + series.applyOptions({ + visible: vis }); + }) + } +} + //toggle indiktoru function onItemClickedToggle(index) { @@ -555,6 +674,46 @@ function populate_indicator_buttons(def) { itemEl.addEventListener('click', function(e) { profitLineToggle(); }); + buttonElement.appendChild(itemEl); + + //button pro toggle fullscreenu + var itemEl = document.createElement('button'); + itemEl.innerText = "wide" + itemEl.classList.add('switcher-item'); + itemEl.style.color = "#99912b" + itemEl.id = "goWide" + itemEl.addEventListener('click', function(e) { + toggleWide(); + }); + buttonElement.appendChild(itemEl); + + // //button pro toggle markeru nakupu/prodeju + var itemEl = document.createElement('button'); + itemEl.innerText = "mrk" + itemEl.classList.add('switcher-item'); + itemEl.classList.add('switcher-active-item'); + // if ((activatedButtons) && (!activatedButtons.includes("mrk"))) { + // } + // else { + + // } + + + itemEl.style.color = "#99912b" + itemEl.id = "mrkLine" + + // // Create an icon element + // const iconEl = document.createElement('i'); + // // Set the icon class + // iconEl.classList.add('bi'); + // iconEl.classList.add('bi-rainbow'); // Replace `icon-name` with the name of the icon you want to use + // // Append the icon element to the button element + // itemEl.appendChild(iconEl); + + itemEl.addEventListener('click', function(e) { + mrkLineToggle(); + }); + buttonElement.appendChild(itemEl); //create plus button to create new button diff --git a/v2realbot/static/main.css b/v2realbot/static/main.css index f25de77..1f2cf1f 100644 --- a/v2realbot/static/main.css +++ b/v2realbot/static/main.css @@ -290,7 +290,7 @@ html { display: inline-block; /* overflow: auto; */ height: 600px; - width: max-content; + width: 750px; } } @@ -364,6 +364,7 @@ pre { .headerItem { padding-right: 30px; + color: #33c1aa; } /* .highlighted { diff --git a/v2realbot/strategy/StrategyClassicSL.py b/v2realbot/strategy/StrategyClassicSL.py index ae88dba..fcd4c89 100644 --- a/v2realbot/strategy/StrategyClassicSL.py +++ b/v2realbot/strategy/StrategyClassicSL.py @@ -25,23 +25,45 @@ class StrategyClassicSL(Strategy): super().__init__(name, symbol, next, init, account, mode, stratvars, open_rush, close_rush, pe, se, runner_id, ilog_save) #zkontroluje zda aktualni profit/loss - nedosahnul limit a pokud ano tak vypne strategii + + ##TODO zestručnit a dát pryč opakovací kód async def stop_when_max_profit_loss(self): self.state.ilog(e="CHECK MAX PROFIT") max_sum_profit_to_quit = safe_get(self.state.vars, "max_sum_profit_to_quit", None) max_sum_loss_to_quit = safe_get(self.state.vars, "max_sum_loss_to_quit", None) + max_sum_profit_to_quit_rel = safe_get(self.state.vars, "max_sum_profit_to_quit_rel", None) + max_sum_loss_to_quit_rel = safe_get(self.state.vars, "max_sum_loss_to_quit_rel", None) + + if max_sum_profit_to_quit_rel is not None: + rel_profit = round(float(np.mean(self.state.rel_profit_cum)),5) + 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=}") + self.state.vars.pending = "max_sum_profit_to_quit_rel" + send_to_telegram(f"QUITTING MAX SUM REL PROFIT REACHED {max_sum_profit_to_quit_rel=} {self.state.profit=} {rel_profit=}") + self.se.set() + return True + if max_sum_loss_to_quit_rel is not None: + rel_profit = round(float(np.mean(self.state.rel_profit_cum)),5) + 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=}") + self.state.vars.pending = "max_sum_loss_to_quit_rel" + send_to_telegram(f"QUITTING MAX SUM REL LOSS REACHED {max_sum_loss_to_quit_rel=} {self.state.profit=} {rel_profit=}") + self.se.set() + return True + if max_sum_profit_to_quit is not None: if float(self.state.profit) >= float(max_sum_profit_to_quit): - self.state.ilog(e=f"QUITTING MAX SUM PROFIT REACHED {max_sum_profit_to_quit=} {self.state.profit=}") + self.state.ilog(e=f"QUITTING MAX SUM ABS PROFIT REACHED {max_sum_profit_to_quit=} {self.state.profit=} {rel_profit=}") self.state.vars.pending = "max_sum_profit_to_quit" - send_to_telegram(f"QUITTING MAX SUM PROFIT REACHED {max_sum_profit_to_quit=} {self.state.profit=}") + send_to_telegram(f"QUITTING MAX SUM ABS PROFIT REACHED {max_sum_profit_to_quit=} {self.state.profit=} {rel_profit=}") self.se.set() return True if max_sum_loss_to_quit is not None: 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 LOSS REACHED {max_sum_loss_to_quit=} {self.state.profit=}") + self.state.ilog(e=f"QUITTING MAX SUM ABS LOSS REACHED {max_sum_loss_to_quit=} {self.state.profit=} {rel_profit=}") self.state.vars.pending = "max_sum_loss_to_quit" - send_to_telegram(f"QUITTING MAX SUM LOSS REACHED {max_sum_loss_to_quit=} {self.state.profit=}") + send_to_telegram(f"QUITTING MAX SUM ABS LOSS REACHED {max_sum_loss_to_quit=} {self.state.profit=} {rel_profit=}") self.se.set() return True @@ -167,6 +189,10 @@ class StrategyClassicSL(Strategy): self.state.ilog(e="BUY: Jde o LONG nakuú nepocitame profit zatim") + if data.event == TradeEvent.FILL: + #zapisujeme last entry price + self.state.last_entry_price["long"] = data.price + #ic("vstupujeme do orderupdatebuy") print(data) #dostavame zde i celkové akutální množství - ukládáme @@ -279,6 +305,10 @@ class StrategyClassicSL(Strategy): self.state.ilog(e="SELL: Jde o SHORT nepocitame profit zatim") + if data.event == TradeEvent.FILL: + #zapisujeme last entry price + self.state.last_entry_price["short"] = data.price + #update pozic, v trade update je i pocet zbylych pozic old_avgp = self.state.avgp old_pos = self.state.positions diff --git a/v2realbot/strategy/base.py b/v2realbot/strategy/base.py index bc2130c..9a09329 100644 --- a/v2realbot/strategy/base.py +++ b/v2realbot/strategy/base.py @@ -25,6 +25,8 @@ from threading import Event, current_thread import json from uuid import UUID from rich import print as printnow +from collections import defaultdict + if PROFILING_NEXT_ENABLED: from pyinstrument import Profiler profiler = Profiler() @@ -662,6 +664,7 @@ class StrategyState: self.time = 0 #time of last trade processed self.last_trade_time = 0 + self.last_entry_price=dict(long=0,short=999) self.timeframe = None self.runner_id = runner_id self.bt = bt @@ -705,13 +708,14 @@ class StrategyState: self.sell_l = self.interface.sell_l self.cancel_pending_buys = None self.iter_log_list = [] + self.dailyBars = defaultdict(dict) #celkovy profit (prejmennovat na profit_cum) self.profit = 0 #celkovy relativni profit (obsahuje pole relativnich zisku, z jeho meanu se spocita celkovy rel_profit_cu,) self.rel_profit_cum = [] self.tradeList = [] #nova promenna pro externi data do ArchiveDetaili, napr. pro zobrazeni v grafu, je zde např. SL history - self.extData = {} + self.extData = defaultdict(dict) self.mode = None self.wait_for_fill = None diff --git a/v2realbot/strategyblocks/activetrade/close/eod_exit.py b/v2realbot/strategyblocks/activetrade/close/eod_exit.py new file mode 100644 index 0000000..faeca4e --- /dev/null +++ b/v2realbot/strategyblocks/activetrade/close/eod_exit.py @@ -0,0 +1,96 @@ +from v2realbot.strategy.base import StrategyState +from v2realbot.strategy.StrategyOrderLimitVykladaciNormalizedMYSELL import StrategyOrderLimitVykladaciNormalizedMYSELL +from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Followup +from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus +from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists +from v2realbot.utils.directive_utils import get_conditions_from_configuration +from v2realbot.ml.mlutils import load_model +from v2realbot.common.model import SLHistory +from v2realbot.config import KW +from uuid import uuid4 +from datetime import datetime +#import random +import json +import numpy as np +#from icecream import install, ic +from rich import print as printanyway +from threading import Event +import os +from traceback import format_exc +from v2realbot.strategyblocks.activetrade.helpers import insert_SL_history +from v2realbot.strategyblocks.activetrade.close.conditions import dontexit_protection_met, exit_conditions_met +from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_override_for_active_trade, keyword_conditions_met + + +def eod_exit_activated(state: StrategyState, data, direction: TradeDirection): + """ + Function responsible for end of day management + + V budoucnu bude obsahovat optimalizace pro uzaviraci okno + (obsahuje subokna - nejprve ceka na snizený profit, pak na minimálně breakeven a naposledy až forced exit) + + 1) zatim pouze - na breakeven(cele okno) + forced exit(posledni minuta) + + + do budoucna udelat interpolacni krivku s ubývajícím časem na snížování profit + tzn. mam např. 60minut, tak rozdělím 4:2 +poslední minuta + - 40 snižující profit (aktuální profit je např. 0.20ticků - tzn. 40 je 0.20, 0 je 0) - print(np.interp(atr10, [1, 10,11,12], [0, 1,100,1])) + - 19 waiting for breakeven + - 1 min forced immediate + """ + + directive_name = "forced_exit_window_start" + forced_exit_window_start = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, None)) + + if forced_exit_window_start is None: + state.ilog(lvl=0,e="Forced exit not required.") + return False + + + directive_name = "forced_exit_window_end" + forced_exit_window_end = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, 389)) + + if forced_exit_window_start>389: + state.ilog(lvl=0,e="Forced exit window end max is 389") + return False + + #TBD - mozna brat skutecny cas (state.time) - nez cas tradu? mozna do budoucna + if is_window_open(datetime.fromtimestamp(data['updated']).astimezone(zoneNY), forced_exit_window_start, forced_exit_window_end) is False: + state.ilog(lvl=1,e=f"Forced Exit Window CLOSED", msg=f"{forced_exit_window_start=} {forced_exit_window_end=} ", time=str(datetime.fromtimestamp(data['updated']).astimezone(zoneNY))) + return False + + # #dokdy konci okno snizujiciho se profitu (zbytek je breakeven a posledni minuta forced) - default pulka okna + # directive_name = "forced_exit_decreasing_profit_window_end" + # forced_exit_decreasing_profit_window_end = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, (forced_exit_window_end-forced_exit_window_end)/2)) + + # if forced_exit_decreasing_profit_window_end > forced_exit_window_end-1: + # state.ilog(lvl=0,e="Decreasing profit window must be less than window end -1.") + # return False + + #TODO v rámci profit optimalizace, udelat decreasing profit window direktivu jez kontroluje interpolovaný snizujici zisk až do 0 a pak až jede breakeven + #TODO v rámci tech optimalizace nevolat is_window_open dvakrat - volá se per tick + if is_window_open(datetime.fromtimestamp(data['updated']).astimezone(zoneNY), forced_exit_window_start, forced_exit_window_end-1) is True: + state.ilog(lvl=1,e=f"Forced Exit Window OPEN - breakeven check", msg=f"{forced_exit_window_start=} {forced_exit_window_end=} ", time=str(datetime.fromtimestamp(data['updated']).astimezone(zoneNY))) + + directive_name = "forced_exit_breakeven_period" + forced_exit_breakeven_period = get_override_for_active_trade(state, directive_name=directive_name, default_value=safe_get(state.vars, directive_name, True)) + + if forced_exit_breakeven_period is False: + return False + + #zatim krom posledni minuty cekame alespon na breakeven + curr_price = float(data['close']) + #short smer + if direction == TradeDirection.SHORT and curr_price<=float(state.avgp): + state.ilog(lvl=1,e=f"Forced Exit - price better than avgp, dir SHORT") + return True + + if direction == TradeDirection.LONG and curr_price>=float(state.avgp): + state.ilog(lvl=1,e=f"Forced Exit - price better than avgp, dir LONG") + return True + + return False + else: + state.ilog(lvl=1,e=f"Forced Exit - last minute - EXIT IMMEDIATE") + return True + diff --git a/v2realbot/strategyblocks/activetrade/close/evaluate_close.py b/v2realbot/strategyblocks/activetrade/close/evaluate_close.py index b5da188..93f360a 100644 --- a/v2realbot/strategyblocks/activetrade/close/evaluate_close.py +++ b/v2realbot/strategyblocks/activetrade/close/evaluate_close.py @@ -9,6 +9,7 @@ from rich import print as printanyway from threading import Event import os from traceback import format_exc +from v2realbot.strategyblocks.activetrade.close.eod_exit import eod_exit_activated from v2realbot.strategyblocks.activetrade.close.conditions import dontexit_protection_met, exit_conditions_met from v2realbot.strategyblocks.activetrade.helpers import get_max_profit_price, get_profit_target_price, get_override_for_active_trade, keyword_conditions_met @@ -26,12 +27,8 @@ def eval_close_position(state: StrategyState, data): #get TARGET PRICE pro dany smer a signal goal_price = get_profit_target_price(state, data, TradeDirection.SHORT) max_price = get_max_profit_price(state, data, TradeDirection.SHORT) - state.ilog(lvl=1,e=f"Goal price {str(TradeDirection.SHORT)} {goal_price} max price {max_price}") - - - #EOD EXIT - TBD - #FORCED EXIT PRI KONCI DNE - + state.ilog(lvl=1,e=f"Goal price {str(TradeDirection.SHORT)} {goal_price} max price {max_price}") + #SL - execution if curr_price > state.vars.activeTrade.stoploss_value: @@ -46,6 +43,7 @@ def eval_close_position(state: StrategyState, data): followup_action = None close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="SL REACHED", followup=followup_action) return + #REVERSE BASED ON REVERSE CONDITIONS if keyword_conditions_met(state, data, direction=TradeDirection.SHORT, keyword=KW.reverse): @@ -82,6 +80,12 @@ def eval_close_position(state: StrategyState, data): if max_price_signal or dontexit_protection_met(state=state, data=data,direction=TradeDirection.SHORT) is False: close_position(state=state, data=data, direction=TradeDirection.SHORT, reason=f"PROFIT or MAXPROFIT REACHED {max_price_signal=}") return + + #FORCED EXIT PRI KONCI DNE + if eod_exit_activated(state, data, TradeDirection.SHORT): + close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="EOD EXIT ACTIVATED") + return + #mame long elif int(state.positions) > 0: @@ -145,3 +149,8 @@ def eval_close_position(state: StrategyState, data): if max_price_signal or dontexit_protection_met(state, data, direction=TradeDirection.LONG) is False: close_position(state=state, data=data, direction=TradeDirection.LONG, reason=f"PROFIT or MAXPROFIT REACHED {max_price_signal=}") return + + #FORCED EXIT PRI KONCI DNE + if eod_exit_activated(state, data, TradeDirection.LONG): + close_position(state=state, data=data, direction=TradeDirection.LONG, reason="EOD EXIT ACTIVATED") + return \ No newline at end of file diff --git a/v2realbot/strategyblocks/activetrade/helpers.py b/v2realbot/strategyblocks/activetrade/helpers.py index 0715831..48b5570 100644 --- a/v2realbot/strategyblocks/activetrade/helpers.py +++ b/v2realbot/strategyblocks/activetrade/helpers.py @@ -9,6 +9,7 @@ from v2realbot.common.model import SLHistory from v2realbot.config import KW from uuid import uuid4 from datetime import datetime +from v2realbot.strategyblocks.indicators.helpers import value_or_indicator #import random import json import numpy as np diff --git a/v2realbot/strategyblocks/indicators/RSI.py b/v2realbot/strategyblocks/indicators/RSI.py index df992df..50ad22e 100644 --- a/v2realbot/strategyblocks/indicators/RSI.py +++ b/v2realbot/strategyblocks/indicators/RSI.py @@ -3,7 +3,7 @@ from v2realbot.strategy.base import StrategyState from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.indicators.oscillators import rsi from traceback import format_exc -from v2realbot.strategyblocks.indicators.helpers import get_source_series +from v2realbot.strategyblocks.indicators.helpers import get_source_series, value_or_indicator #RSI INDICATOR # type = RSI, source = [close, vwap, hlcc4], rsi_length = [14], MA_length = int (optional), on_confirmed_only = [true, false] @@ -22,10 +22,11 @@ def populate_dynamic_RSI_indicator(data, state: StrategyState, name): #poustet kazdy tick nebo jenom na confirmed baru (on_confirmed_only = true) on_confirmed_only = safe_get(options, 'on_confirmed_only', False) req_source = safe_get(options, 'source', 'vwap') - rsi_length = int(safe_get(options, "length",14)) + rsi_length = safe_get(options, "length",14) rsi_MA_length = safe_get(options, "MA_length", None) start = safe_get(options, "start","linear") #linear/sharp + rsi_length = int(value_or_indicator(state, rsi_length)) if on_confirmed_only is False or (on_confirmed_only is True and data['confirmed']==1): try: diff --git a/v2realbot/strategyblocks/indicators/custom/_upscaled_rsi_wip.py b/v2realbot/strategyblocks/indicators/custom/_upscaled_rsi_wip.py index a93d6f5..3fcc2a2 100644 --- a/v2realbot/strategyblocks/indicators/custom/_upscaled_rsi_wip.py +++ b/v2realbot/strategyblocks/indicators/custom/_upscaled_rsi_wip.py @@ -4,7 +4,6 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict diff --git a/v2realbot/strategyblocks/indicators/custom/barparams.py b/v2realbot/strategyblocks/indicators/custom/barparams.py index d31609b..0e44fb2 100644 --- a/v2realbot/strategyblocks/indicators/custom/barparams.py +++ b/v2realbot/strategyblocks/indicators/custom/barparams.py @@ -5,19 +5,20 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict #indicator allowing to be based on any bar parameter (index, high,open,close,trades,volume, etc.) -def barparams(state, params): +def barparams(state, params, name): funcName = "barparams" if params is None: return -2, "params required" source = safe_get(params, "source", None) + lookback = safe_get(params, "lookback", 1) if source is None: return -2, "source required" try: - return 0, state.bars[source][-1] + return 0, get_source_series(state, source)[-lookback] + #return 0, state.bars[source][-1] except Exception as e: return -2, str(e)+format_exc() diff --git a/v2realbot/strategyblocks/indicators/custom/basestats.py b/v2realbot/strategyblocks/indicators/custom/basestats.py index 309ad19..dedfea6 100644 --- a/v2realbot/strategyblocks/indicators/custom/basestats.py +++ b/v2realbot/strategyblocks/indicators/custom/basestats.py @@ -4,29 +4,36 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict from scipy.stats import linregress +from scipy.fft import fft +from v2realbot.strategyblocks.indicators.helpers import value_or_indicator #vstupem je bud indicator nebo bar parametr #na tomto vstupu dokaze provest zakladni statisticke funkce pro subpole X hodnot zpatky #podporovane functions: min, max, mean -def basestats(state, params): +def basestats(state, params, name): funcName = "basestats" #name of indicator or source = safe_get(params, "source", None) lookback = safe_get(params, "lookback", None) func = safe_get(params, "function", None) + returns = safe_get(params, "returns", None) source_dict = defaultdict(list) source_dict[source] = get_source_series(state, source) + self = state.indicators[name] if lookback is None: source_array = source_dict[source] + else: + lookback = int(value_or_indicator(state, lookback)) + try: source_array = source_dict[source][-lookback-1:] + self = self[-lookback-1:] except IndexError: source_array = source_dict[source] @@ -69,7 +76,7 @@ def basestats(state, params): try: np.seterr(all="raise") val, _, _, _, _ = linregress(np.arange(len(source_array)), source_array) - val = round(val, 4) + val = val*1000 except FloatingPointError: return -2, "FloatingPointError" #zatim takto, dokud nebudou podporovany indikatory s vice vystupnimi @@ -82,6 +89,60 @@ def basestats(state, params): val = round(val, 4) except FloatingPointError: return -2, "FloatingPointError" + elif func == "fourier": + time_series = np.array(source_array) + n = len(time_series) + + # Compute the Fourier transform + yf = fft(time_series) + xf = np.linspace(0.0, 1.0/(2.0), n//2) + + dominant_frequencies = xf[np.argsort(np.abs(yf[:n//2]))[-3:]] + state.ilog(lvl=1,e=f"IND {name}:{funcName} 3 dominant freq are {str(dominant_frequencies)}", **params) + + if returns is not None: + #vracime druhou + if returns == "second": + if len(dominant_frequencies) > 1: + val = dominant_frequencies[-2] + else: + val = 0 + else: + #vracime most dominant + val = float(np.max(dominant_frequencies)) + return 0, val + + elif func == "maxima": + if len(source_array) < 3: + return 0, state.bars["high"] + + if len(self) == 0: + self_max = 0 + else: + #nejvyssi dosavadni maxima za lookback + #self_max = float(np.max(self)) + #zkusim zatim takto, a dalsi indikator z toho pak bude delat lajny? + self_max = self[-2] + + state.ilog(lvl=1,e=f"IND {name}:{funcName} {str(self_max)}", **params) + + # 3 .. 2 nahoru + if source_array[-2] > source_array[-3]: + # 2 .. 1 dolu - mame pivot + if source_array[-2] > source_array[-1]: + ##jsme max za obdobi + if source_array[-2] > self_max: + return 0, source_array[-2] + else: + return 0, self_max + # 2 .. 1 nahoru - drzime puvodni -do otocky + else: + return 0, self_max + + # 3 ..2 dolu drzime max + else: + return 0, self_max + else: return -2, "wrong function" diff --git a/v2realbot/strategyblocks/indicators/custom/conditional.py b/v2realbot/strategyblocks/indicators/custom/conditional.py index 4c022b0..605a0a0 100644 --- a/v2realbot/strategyblocks/indicators/custom/conditional.py +++ b/v2realbot/strategyblocks/indicators/custom/conditional.py @@ -4,7 +4,6 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series, evaluate_directive_conditions from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict @@ -24,7 +23,7 @@ from collections import defaultdict #novy podminkovy indikator, muze obsahovat az N podminek ve stejne syntaxy jako u signalu #u kazde podminky je hodnota, ktera se vraci pokud je true #hodi se pro vytvareni binarnich targetu pro ML -def conditional(state, params): +def conditional(state, params, name): funcName = "conditional" if params is None: return -2, "params required" @@ -42,13 +41,13 @@ def conditional(state, params): #zde je pripavena podminka, kterou jen evaluujeme cond_dict = condsettings["cond_dict"] result, conditions_met = evaluate_directive_conditions(state, cond_dict, "OR") - state.ilog(lvl=1,e=f"IND PODMINKA {condname} =OR= {result}", **conditions_met, cond_dict=cond_dict) + state.ilog(lvl=1,e=f"IND PODMINKA {name}:{condname} =OR= {result}", **conditions_met, cond_dict=cond_dict) if result: return 0, true_val #OR neprosly testujeme AND result, conditions_met = evaluate_directive_conditions(state, cond_dict, "AND") - state.ilog(lvl=1,e=f"IND PODMINKA {condname} =AND= {result}", **conditions_met, cond_dict=cond_dict) + state.ilog(lvl=1,e=f"IND PODMINKA {name}:{condname} =AND= {result}", **conditions_met, cond_dict=cond_dict) if result: return 0, true_val diff --git a/v2realbot/strategyblocks/indicators/custom/delta.py b/v2realbot/strategyblocks/indicators/custom/delta.py index 10ee045..abaea9c 100644 --- a/v2realbot/strategyblocks/indicators/custom/delta.py +++ b/v2realbot/strategyblocks/indicators/custom/delta.py @@ -4,13 +4,12 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict #strength, absolute change of parameter between current value and lookback value (n-past) #used for example to measure unusual peaks -def delta(state, params): +def delta(state, params, name): funcName = "delta" source = safe_get(params, "source", None) lookback = safe_get(params, "lookback",1) @@ -20,5 +19,5 @@ def delta(state, params): currval = source_series[-1] delta = currval - lookbackval - state.ilog(lvl=1,e=f"INSIDE {funcName} {delta} {source=} {lookback=}", currval=currval, lookbackval=lookbackval, **params) + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {delta} {source=} {lookback=}", currval=currval, lookbackval=lookbackval, **params) return 0, delta diff --git a/v2realbot/strategyblocks/indicators/custom/divergence.py b/v2realbot/strategyblocks/indicators/custom/divergence.py deleted file mode 100644 index ec9e06b..0000000 --- a/v2realbot/strategyblocks/indicators/custom/divergence.py +++ /dev/null @@ -1,43 +0,0 @@ -from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists -from v2realbot.strategy.base import StrategyState -from v2realbot.indicators.indicators import ema, natr, roc -from v2realbot.strategyblocks.indicators.helpers import get_source_series -from rich import print as printanyway -from traceback import format_exc -from v2realbot.ml.ml import ModelML -import numpy as np -from collections import defaultdict - -#abs/rel divergence of two indicators -def divergence(state, params): - funcName = "indicatorDivergence" - source1 = safe_get(params, "source1", None) - source1_series = get_source_series(state, source1) - source2 = safe_get(params, "source2", None) - source2_series = get_source_series(state, source2) - mode = safe_get(params, "type") - state.ilog(lvl=0,e=f"INSIDE {funcName} {source1=} {source2=} {mode=}", **params) - val = 0 - if mode == "abs": - val = round(abs(float(source1_series[-1]) - float(source2_series[-1])),4) - elif mode == "absn": - val = round((abs(float(source1_series[-1]) - float(source2_series[-1])))/float(source1_series[-1]),4) - elif mode == "rel": - val = round(float(source1_series[-1]) - float(source2_series[-1]),4) - elif mode == "reln": - val = round((float(source1_series[-1]) - float(source2_series[-1]))/float(source1_series[-1]),4) - elif mode == "pctabs": - val = pct_diff(num1=float(source1_series[-1]),num2=float(source2_series[-1]), absolute=True) - elif mode == "pct": - val = pct_diff(num1=float(source1_series[-1]),num2=float(source2_series[-1])) - return 0, val - -#model - naloadovana instance modelu -#seq - sekvence pro vstup - - - - - - - diff --git a/v2realbot/strategyblocks/indicators/custom/ema.py b/v2realbot/strategyblocks/indicators/custom/ema.py index d0a8693..e76a370 100644 --- a/v2realbot/strategyblocks/indicators/custom/ema.py +++ b/v2realbot/strategyblocks/indicators/custom/ema.py @@ -4,13 +4,12 @@ from v2realbot.indicators.indicators import ema as ext_ema from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict from v2realbot.strategyblocks.indicators.helpers import value_or_indicator #strength, absolute change of parameter between current value and lookback value (n-past) #used for example to measure unusual peaks -def ema(state, params): +def ema(state, params, name): funcName = "ema" source = safe_get(params, "source", None) lookback = safe_get(params, "lookback",14) @@ -22,5 +21,5 @@ def ema(state, params): ema_value = ext_ema(source_series, lookback) val = round(ema_value[-1],4) - state.ilog(lvl=1,e=f"INSIDE {funcName} {val} {source=} {lookback=}", **params) + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {val} {source=} {lookback=}", **params) return 0, val \ No newline at end of file diff --git a/v2realbot/strategyblocks/indicators/custom/expression.py b/v2realbot/strategyblocks/indicators/custom/expression.py index d1ec064..d6e7b77 100644 --- a/v2realbot/strategyblocks/indicators/custom/expression.py +++ b/v2realbot/strategyblocks/indicators/custom/expression.py @@ -1,6 +1,10 @@ -from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists from v2realbot.strategy.base import StrategyState import numpy as np +from rich import print as printanyway +from traceback import format_exc +import v2realbot.utils.utils as utls +# from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists + #allows executing a expression - pozor neni sanitized a zatim se spousti i v globalni scopu #v pripade jineho nez soukromeho uziti zabezpecit @@ -9,24 +13,30 @@ import numpy as np #eval nyni umi i user-defined function, string operation and control statements #teroeticky se dá pouzit i SYMPY - kde se daji vytvorit jednotlive symboly s urcitou funkcni -def expression(state: StrategyState, params): - funcName = "expression" - #indicator name - operation = safe_get(params, "expression", None) +def expression(state: StrategyState, params, name): + try: + funcName = "expression" + #indicator name + operation = utls.safe_get(params, "expression", None) - if operation is None : - return -2, "required param missing" - - state.ilog(lvl=0,e=f"BEFORE {funcName} {operation=}", **params) - - #pro zacatek eval - val = eval(operation, {'state': state, 'np': np}, state.ind_mapping) + if operation is None : + return -2, "required param missing" + + state.ilog(lvl=0,e=f"BEFORE {name}:{funcName} {operation=}", **params) + + #pro zacatek eval + val = eval(operation, {'state': state, 'np': np, 'utls': utls}, state.ind_mapping) - if not np.isfinite(val): - val = 0 - #val = ne.evaluate(operation, state.ind_mapping) + #printanyway(val) - state.ilog(lvl=1,e=f"IND {funcName} {operation=} res:{val}", **params) + if not np.isfinite(val): + val = 0 + #val = ne.evaluate(operation, state.ind_mapping) + + state.ilog(lvl=1,e=f"IND {name}:{funcName} {operation=} res:{val}", **params) + except Exception as e: + printanyway(name + str(e) + format_exc()) + raise e return 0, val diff --git a/v2realbot/strategyblocks/indicators/custom/ma.py b/v2realbot/strategyblocks/indicators/custom/ma.py index faca166..fd90f3f 100644 --- a/v2realbot/strategyblocks/indicators/custom/ma.py +++ b/v2realbot/strategyblocks/indicators/custom/ma.py @@ -4,22 +4,23 @@ import v2realbot.indicators.moving_averages as mi from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict from v2realbot.strategyblocks.indicators.helpers import value_or_indicator +# from talib import BBANDS, MACD, RSI, MA_Type #IMPLEMENTS different types of moving averages in package v2realbot.indicators.moving_averages -def ma(state, params): +def ma(state, params, name): funcName = "ma" type = safe_get(params, "type", "ema") source = safe_get(params, "source", None) lookback = safe_get(params, "lookback",14) start = safe_get(params, "start","linear") #linear/sharp - + defval = safe_get(params, "defval",0) #lookback muze byt odkaz na indikator, pak berem jeho hodnotu lookback = int(value_or_indicator(state, lookback)) + defval = int(value_or_indicator(state, defval)) source_series = get_source_series(state, source) @@ -34,7 +35,14 @@ def ma(state, params): ma_function = eval(type) ma_value = ma_function(source_series, lookback) - val = round(ma_value[-1],4) - state.ilog(lvl=1,e=f"INSIDE {funcName} {val} {type=} {source=} {lookback=}", **params) + if not np.isfinite(ma_value[-1]): + val = defval + else: + val = round(ma_value[-1],4) + + if val == 0: + val = defval + + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {val} {type=} {source=} {lookback=}", **params) return 0, val \ No newline at end of file diff --git a/v2realbot/strategyblocks/indicators/custom/mathop.py b/v2realbot/strategyblocks/indicators/custom/mathop.py index 1828aae..853a358 100644 --- a/v2realbot/strategyblocks/indicators/custom/mathop.py +++ b/v2realbot/strategyblocks/indicators/custom/mathop.py @@ -3,7 +3,8 @@ from v2realbot.strategy.base import StrategyState from v2realbot.strategyblocks.indicators.helpers import get_source_series, value_or_indicator #allows basic mathematical operators to one or more indicators (add two indicator, add value to a indicator etc.) -def mathop(state, params): +#REPLACED by EXPRESSION +def mathop(state, params, name): funcName = "mathop" #indicator name source1 = safe_get(params, "source1", None) @@ -24,7 +25,7 @@ def mathop(state, params): val = round(float(source1_series[-1] * druhy),4) else: return -2, "unknow operator" - state.ilog(lvl=1,e=f"INSIDE {funcName} {source1=} {source2=} {val} {druhy=}", **params) + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {source1=} {source2=} {val} {druhy=}", **params) return 0, val diff --git a/v2realbot/strategyblocks/indicators/custom/model.py b/v2realbot/strategyblocks/indicators/custom/model.py index 184eea7..334519e 100644 --- a/v2realbot/strategyblocks/indicators/custom/model.py +++ b/v2realbot/strategyblocks/indicators/custom/model.py @@ -4,11 +4,10 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict -def model(state, params): +def model(state, params, ind_name): funcName = "model" if params is None: return -2, "params required" diff --git a/v2realbot/strategyblocks/indicators/custom/opengap.py b/v2realbot/strategyblocks/indicators/custom/opengap.py index 005cd3c..2b6e66b 100644 --- a/v2realbot/strategyblocks/indicators/custom/opengap.py +++ b/v2realbot/strategyblocks/indicators/custom/opengap.py @@ -4,17 +4,16 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict #WIP - #testing custom indicator CODE -def opengap(state, params): +def opengap(state, params, name): funcName = "opengap" param1 = safe_get(params, "param1") param2 = safe_get(params, "param2") - state.ilog(lvl=0,e=f"INSIDE {funcName} {param1=} {param2=}", **params) + state.ilog(lvl=0,e=f"INSIDE {name}:{funcName} {param1=} {param2=}", **params) last_close = 28.45 today_open = 29.45 val = pct_diff(last_close, today_open) diff --git a/v2realbot/strategyblocks/indicators/custom/sameprice.py b/v2realbot/strategyblocks/indicators/custom/sameprice.py index 696060d..66c75bd 100644 --- a/v2realbot/strategyblocks/indicators/custom/sameprice.py +++ b/v2realbot/strategyblocks/indicators/custom/sameprice.py @@ -4,14 +4,13 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict import bisect #strength, absolute change of parameter between current value and lookback value (n-past) #used for example to measure unusual peaks -def sameprice(state, params): +def sameprice(state, params, name): funcName = "sameprice" typ = safe_get(params, "type", None) @@ -40,13 +39,13 @@ def sameprice(state, params): #jde o daily high if pozice_prvniho_vetsiho == -1: - state.ilog(lvl=1,e=f"INSIDE {funcName} {typ} {pozice_prvniho_vetsiho=} vracime 1") + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {typ} {pozice_prvniho_vetsiho=} vracime 1") return 0, celkova_delka delka_k_predchozmu = celkova_delka - pozice_prvniho_vetsiho normalizovano = delka_k_predchozmu/celkova_delka - state.ilog(lvl=1,e=f"INSIDE {funcName} {typ} {pozice_prvniho_vetsiho=} {celkova_delka=} {delka_k_predchozmu=} {normalizovano=}", pozice_prvniho_vetsiho=pozice_prvniho_vetsiho, celkova_delka=celkova_delka, delka_k_predchozmu=delka_k_predchozmu, **params) + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {typ} {pozice_prvniho_vetsiho=} {celkova_delka=} {delka_k_predchozmu=} {normalizovano=}", pozice_prvniho_vetsiho=pozice_prvniho_vetsiho, celkova_delka=celkova_delka, delka_k_predchozmu=delka_k_predchozmu, **params) return 0, delka_k_predchozmu diff --git a/v2realbot/strategyblocks/indicators/custom/slope.py b/v2realbot/strategyblocks/indicators/custom/slope.py index 841078b..d3bcd54 100644 --- a/v2realbot/strategyblocks/indicators/custom/slope.py +++ b/v2realbot/strategyblocks/indicators/custom/slope.py @@ -4,12 +4,11 @@ from v2realbot.indicators.indicators import ema, natr, roc from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict #rate of change - last value of source indicator vs lookback value of lookback_priceline indicator -def slope(state, params): +def slope(state, params, name): funcName = "slope" source = safe_get(params, "source", None) source_series = get_source_series(state, source) @@ -31,5 +30,5 @@ def slope(state, params): slope = ((currval - lookbackprice)/abs(lookbackprice))*100 #slope = round(slope, 4) - state.ilog(lvl=1,e=f"INSIDE {funcName} {slope} {source=} {lookback=}", currval_source=currval, lookbackprice=lookbackprice, lookbacktime=lookbacktime, **params) + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {slope} {source=} {lookback=}", currval_source=currval, lookbackprice=lookbackprice, lookbacktime=lookbacktime, **params) return 0, slope diff --git a/v2realbot/strategyblocks/indicators/custom/vwma.py b/v2realbot/strategyblocks/indicators/custom/vwma.py index 9b33367..c584b99 100644 --- a/v2realbot/strategyblocks/indicators/custom/vwma.py +++ b/v2realbot/strategyblocks/indicators/custom/vwma.py @@ -5,13 +5,12 @@ from v2realbot.indicators.moving_averages import vwma as ext_vwma from v2realbot.strategyblocks.indicators.helpers import get_source_series from rich import print as printanyway from traceback import format_exc -from v2realbot.ml.ml import ModelML import numpy as np from collections import defaultdict from v2realbot.strategyblocks.indicators.helpers import value_or_indicator # Volume(or reference_source) Weighted moving Average -def vwma(state, params): +def vwma(state, params, name): funcName = "vwma" source = safe_get(params, "source", None) ref_source = safe_get(params, "ref_source", "volume") @@ -34,5 +33,5 @@ def vwma(state, params): vwma_value = ext_vwma(source_series, ref_source_series, lookback) val = round(vwma_value[-1],4) - state.ilog(lvl=1,e=f"INSIDE {funcName} {val} {source=} {ref_source=} {lookback=}", **params) + state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {val} {source=} {ref_source=} {lookback=}", **params) return 0, val \ No newline at end of file diff --git a/v2realbot/strategyblocks/indicators/custom_hub.py b/v2realbot/strategyblocks/indicators/custom_hub.py index 7daf8b6..d9a3b8f 100644 --- a/v2realbot/strategyblocks/indicators/custom_hub.py +++ b/v2realbot/strategyblocks/indicators/custom_hub.py @@ -133,7 +133,7 @@ def populate_dynamic_custom_indicator(data, state: StrategyState, name): subtype = "ci."+subtype+"."+subtype custom_function = eval(subtype) - res_code, new_val = custom_function(state, custom_params) + res_code, new_val = custom_function(state, custom_params, name) if res_code == 0: state.indicators[name][-1-save_to_past]=new_val state.ilog(lvl=1,e=f"IND {name} {subtype} VAL FROM FUNCTION: {new_val}", lastruntime=state.vars.indicators[name]["last_run_time"], lastrunindex=state.vars.indicators[name]["last_run_index"], save_to_past=save_to_past) @@ -159,7 +159,7 @@ def populate_dynamic_custom_indicator(data, state: StrategyState, name): else: state.ilog(lvl=0,e=f"IND {name} {subtype} COND NOT READY: {msg}") - #not time to run + #not time to run - copy last value if len(state.indicators[name]) >= 2: state.indicators[name][-1]=state.indicators[name][-2] diff --git a/v2realbot/strategyblocks/indicators/helpers.py b/v2realbot/strategyblocks/indicators/helpers.py index 27aec3b..170120c 100644 --- a/v2realbot/strategyblocks/indicators/helpers.py +++ b/v2realbot/strategyblocks/indicators/helpers.py @@ -71,11 +71,22 @@ def get_source_or_MA(state, indicator): except KeyError: return state.bars[indicator] -def get_source_series(state, source): - try: - return state.bars[source] - except KeyError: - return state.indicators[source] +def get_source_series(state: StrategyState, source: str): + """ + Podporujeme krome klice v bar a indikatoru a dalsi doplnujici, oddelene _ napr. dailyBars_close + vezme serii static.dailyBars[close] + """ + + split_index = source.find("|") + if split_index == -1: + try: + return state.bars[source] + except KeyError: + return state.indicators[source] + else: + dict_name = source[:split_index] + key = source[split_index + 1:] + return getattr(state, dict_name)[key] #TYTO NEJSPIS DAT do util #vrati true pokud dany indikator prekrocil threshold dolu diff --git a/v2realbot/strategyblocks/inits/init_directives.py b/v2realbot/strategyblocks/inits/init_directives.py index 6b34224..0097af8 100644 --- a/v2realbot/strategyblocks/inits/init_directives.py +++ b/v2realbot/strategyblocks/inits/init_directives.py @@ -4,7 +4,6 @@ from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, Foll from v2realbot.common.PrescribedTradeModel import Trade, TradeDirection, TradeStatus from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, print, safe_get, is_still, is_window_open, eval_cond_dict, crossed_down, crossed_up, crossed, is_pivot, json_serial, pct_diff, create_new_bars, slice_dict_lists from v2realbot.utils.directive_utils import get_conditions_from_configuration -from v2realbot.ml.mlutils import load_model from v2realbot.common.model import SLHistory from v2realbot.config import KW from uuid import uuid4 diff --git a/v2realbot/strategyblocks/newtrade/conditions.py b/v2realbot/strategyblocks/newtrade/conditions.py index 963dde8..8e71377 100644 --- a/v2realbot/strategyblocks/newtrade/conditions.py +++ b/v2realbot/strategyblocks/newtrade/conditions.py @@ -9,6 +9,7 @@ from v2realbot.common.model import SLHistory from v2realbot.config import KW from uuid import uuid4 from datetime import datetime +from v2realbot.strategyblocks.indicators.helpers import value_or_indicator #import random import json import numpy as np @@ -106,6 +107,8 @@ def common_go_preconditions_check(state, data, signalname: str, options: dict): return False next_signal_offset = safe_get(options, "next_signal_offset_from_last_exit",safe_get(state.vars, "next_signal_offset_from_last_exit",0)) + #muze byt i indikator + next_signal_offset = int(value_or_indicator(state, next_signal_offset)) if state.vars.last_exit_index is not None: index_to_compare = int(state.vars.last_exit_index)+int(next_signal_offset) diff --git a/v2realbot/utils/historicals.py b/v2realbot/utils/historicals.py new file mode 100644 index 0000000..2ea146a --- /dev/null +++ b/v2realbot/utils/historicals.py @@ -0,0 +1,92 @@ +from alpaca.data.historical import StockHistoricalDataClient, CryptoHistoricalDataClient +from alpaca.data.requests import StockLatestQuoteRequest, StockBarsRequest, StockTradesRequest, StockSnapshotRequest +from alpaca.data import Quote, Trade, Snapshot, Bar +from alpaca.data.models import BarSet, QuoteSet, TradeSet +from alpaca.data.timeframe import TimeFrame, TimeFrameUnit +from v2realbot.utils.utils import zoneNY +from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY +from alpaca.data.enums import DataFeed +from datetime import datetime, timedelta +import pandas as pd +from rich import print +from collections import defaultdict +from pandas import to_datetime +from msgpack.ext import Timestamp + +def convert_daily_bars(daily_bars): + """Converts a list of daily bars into a dictionary with the specified keys. + + Args: + daily_bars: A list of daily bars, where each bar is a dictionary with the + following keys: + * c: Close price + * h: High price + * l: Low price + * n: Number of trades + * o: Open price + * t: Time in UTC (ISO 8601 format) + * v: Volume + * vw: VWAP + + Returns: + A dictionary with the following keys: + * high: A list of high prices + * low: A list of low prices + * volume: A list of volumes + * close: A list of close prices + * hlcc4: A list of HLCC4 indicators + * open: A list of open prices + * time: A list of times in UTC (ISO 8601 format) + * trades: A list of number of trades + * resolution: A list of resolutions (all set to 'D') + * confirmed: A list of booleans (all set to True) + * vwap: A list of VWAP indicator + * updated: A list of booleans (all set to True) + * index: A list of integers (from 0 to the length of the list of daily bars) + """ + + bars = defaultdict(list) + for i in range(len(daily_bars)): + bar = daily_bars[i] + + # Calculate the HLCC4 indicator + hlcc4 = (bar['h'] + bar['l'] + bar['c'] + bar['o']) / 4 + datum = to_datetime(bar['t'], utc=True) + + #nebo pripadna homogenizace s online streamem + #datum = Timestamp.from_unix(datum.timestamp()) + + # Add the bar to the dictionary + bars['high'].append(bar['h']) + bars['low'].append(bar['l']) + bars['volume'].append(bar['v']) + bars['close'].append(bar['c']) + bars['hlcc4'].append(hlcc4) + bars['open'].append(bar['o']) + bars['time'].append(datum) + bars['trades'].append(bar['n']) + bars['resolution'].append('D') + bars['confirmed'].append(1) + bars['vwap'].append(bar['vw']) + bars['updated'].append(datum) + bars['index'].append(i) + + return bars + +def get_last_close(): + pass + +def get_todays_open(): + pass + +##vrati historicke bary v nasem formatu +def get_historical_bars(symbol: str, time_from: datetime, time_to: datetime, timeframe: TimeFrame): + stock_client = StockHistoricalDataClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=True) + # snapshotRequest = StockSnapshotRequest(symbol_or_symbols=[symbol], feed=DataFeed.SIP) + # snapshotResponse = stock_client.get_stock_snapshot(snapshotRequest) + # print("snapshot", snapshotResponse) + + bar_request = StockBarsRequest(symbol_or_symbols=symbol,timeframe=timeframe, start=time_from, end=time_to, feed=DataFeed.SIP) + bars: BarSet = stock_client.get_stock_bars(bar_request) + ##print("puvodni bars", bars["BAC"]) + return convert_daily_bars(bars[symbol]) diff --git a/v2realbot/utils/profitloss.py b/v2realbot/utils/profitloss.py new file mode 100644 index 0000000..21f5eda --- /dev/null +++ b/v2realbot/utils/profitloss.py @@ -0,0 +1,16 @@ + + +def calculate_relative_profit_loss(entry_price, exit_price): + """Calculates the relative profit/loss in percents. + + Args: + entry_price: The entry price. + exit_price: The exit price. + + Returns: + The relative profit/loss in percents. + """ + + relative_profit_loss = (exit_price - entry_price) / entry_price * 100 + return relative_profit_loss + diff --git a/v2realbot/utils/utils.py b/v2realbot/utils/utils.py index a0a2dbd..25ac1c6 100644 --- a/v2realbot/utils/utils.py +++ b/v2realbot/utils/utils.py @@ -152,18 +152,19 @@ def is_pivot(source: list, leg_number: int, type: str = "A"): right_leg = source[-leg_number:] if type == "A": - if isrising(left_leg) and isfalling(right_leg): + if isrisingc(left_leg) and isfallingc(right_leg): return True else: return False elif type == "V": - if isfalling(left_leg) and isrising(right_leg): + if isfallingc(left_leg) and isrisingc(right_leg): return True else: return False else: print("Unknown type") return False + def crossed_up(threshold, list): """check if threshold has crossed up last thresholdue in list""" @@ -324,6 +325,7 @@ def json_serial(obj): """ type_map = { + pd.Timestamp: lambda obj: obj.timestamp(), datetime: lambda obj: obj.timestamp(), UUID: lambda obj: str(obj), Enum: lambda obj: str(obj),