balik zmen uff

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"]
+

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')
 
+
+
+

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]))

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}")

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()
+
+

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()
+
+

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"]
+

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)

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

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)

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()

v2realbot/controller/services.py

@@ -277,16 +277,18 @@ 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)
     
+    #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 += "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 += "\nREASON:" + str(reason)
                 #i.history += str(runner.__dict__)+"<BR>"
                 db.save()
  
@@ -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__)+"<BR>"
+            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)
@@ -1241,8 +1260,7 @@ 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]
         

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)

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)

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

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

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 and i_s >= 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 and i_s >= 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)

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
 

v2realbot/static/index.html

@@ -17,8 +17,8 @@
 
         <!-- <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha3/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-KK94CHFLLe+nY2dmCWGMq91rCGa5gtU4mk92HdvYe+M/SXH301p5ILy+dN9+nJOZ" crossorigin="anonymous"> -->
         <link href="/static/js/libs/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
-
-        
+        <!-- <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap-icons@1.11.1/font/bootstrap-icons.css">
+         -->
         <!-- <link rel="stylesheet" href="https://cdn.datatables.net/1.13.4/css/dataTables.bootstrap5.min.css"> -->
         <link rel="stylesheet" href="/static/js/libs/dataTables.bootstrap5.min.css">
 
@@ -156,10 +156,10 @@
                     <div id="chart" style="display: None; float: left; "></div>
                     <div class="legend" id="legend"></div>
                     <div id="msgContainer">
-                        <div class="clearbutton">
+                        <div class="clearbutton" id="clrButton">
                             <button id="button_clearlog" class="btn btn-outline-success btn-sm" style="display:none;">Clear</button>
                         </div>
-                        <div class="msgContainerInner">
+                        <div class="msgContainerInner" id="msgContainerInner">
                             <div id="lines">
                             </div>
                         </div>
@@ -296,6 +296,7 @@
                         <button id="button_refresh" class="refresh btn btn-outline-success btn-sm">Refresh</button>
                         <button id="button_compare_arch" class="refresh btn btn-outline-success btn-sm">Compare</button>
                         <button id="button_runagain_arch" class="refresh btn btn-outline-success btn-sm">Run Again(r)</button>
+                        <button id="button_selpage" class="btn btn-outline-success btn-sm">Select all</button>
                         <!-- <button id="button_stopall" class="btn btn-outline-success btn-sm">Stop All</button>
                         <button id="button_refresh" class="btn btn-outline-success btn-sm">Refresh</button> -->
                     </div>
@@ -406,6 +407,7 @@
                         <button id="button_compare" class="btn btn-outline-success btn-sm">Compare</button>
                         <button id="button_run" class="btn btn-outline-success btn-sm">Run Strategy(y)</button>
                         <button id="button_refresh" class="refresh btn btn-outline-success btn-sm">Refresh</button>
+                        <button id="button_filter_strat" class="btn btn-outline-success btn-sm">Filter backtests</button>
                     </div>
                     <table id="stratinTable" class="table-striped table dataTable" style="width:100%; border-color: #dce1dc;">
                         <thead>

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
 
 }

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);

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)

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();

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

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 {

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

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
     

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
+

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
 
@@ -28,10 +29,6 @@ def eval_close_position(state: StrategyState, data):
             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
-
             #SL - execution
             if curr_price > state.vars.activeTrade.stoploss_value:
 
@@ -47,6 +44,7 @@ def eval_close_position(state: StrategyState, data):
                 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):
                     close_position(state=state, data=data, direction=TradeDirection.SHORT, reason="REVERSE COND MET", followup=Followup.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      

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

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:

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
 

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()

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"
 

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
 

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

v2realbot/strategyblocks/indicators/custom/divergence.py

@@ -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
-
-
-
-
-
-
-

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

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):
+def expression(state: StrategyState, params, name):
+    try:
         funcName = "expression"
         #indicator name
-    operation = safe_get(params, "expression", None)
+        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)
+        state.ilog(lvl=0,e=f"BEFORE {name}:{funcName} {operation=}", **params)
         
         #pro zacatek eval
-    val = eval(operation, {'state': state, 'np': np}, state.ind_mapping)
+        val = eval(operation, {'state': state, 'np': np, 'utls': utls}, state.ind_mapping)
+
+        #printanyway(val)
 
         if not np.isfinite(val):
             val = 0
         #val = ne.evaluate(operation, state.ind_mapping)
 
-    state.ilog(lvl=1,e=f"IND {funcName} {operation=} res:{val}", **params)
+        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
 
 

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)
+
+    if not np.isfinite(ma_value[-1]):
+        val = defval
+    else:
         val = round(ma_value[-1],4)
 
-    state.ilog(lvl=1,e=f"INSIDE {funcName} {val} {type=} {source=} {lookback=}", **params)
+    if val == 0:
+        val = defval
+
+    state.ilog(lvl=1,e=f"INSIDE {name}:{funcName} {val} {type=} {source=} {lookback=}", **params)
     return 0, val

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
 
 

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"

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)

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
 

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

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

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]
 

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):
+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

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

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) 

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])

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
+

v2realbot/utils/utils.py

@@ -152,12 +152,12 @@ 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
@@ -165,6 +165,7 @@ def is_pivot(source: list, leg_number: int, type: str = "A"):
         print("Unknown type")
         return False
     
+
 def crossed_up(threshold, list):
     """check if threshold has crossed up last thresholdue in list"""
     try:
@@ -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),