other changes

testy/domfreq.py

@@ -0,0 +1,62 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.fft import fft
+
+# Define the sampling frequency and time vector
+fs = 500  # Sampling frequency
+t = np.arange(0, 1, 1/fs)  # Time vector
+
+# Define the frequencies
+f1 = 5    # Frequency that occurs most often but with lower amplitude
+f2 = 20   # Frequency with the highest amplitude
+
+# Creating the individual signals
+signal_f1 = 0.5 * np.sin(2 * np.pi * f1 * t)  # Signal with frequency f1
+signal_f2 = 2 * np.sin(2 * np.pi * f2 * t)    # Signal with frequency f2
+
+# Composite signal
+signal = signal_f1 + signal_f2
+
+# Performing a Fourier Transform
+freq = np.fft.fftfreq(len(t), 1/fs)
+fft_values = fft(signal)
+
+# Plotting all the signals and the frequency spectrum
+plt.figure(figsize=(14, 10))
+
+# Plot 1: Composite Signal
+plt.subplot(4, 1, 1)
+plt.plot(t, signal)
+plt.title('Composite Signal (f1 + f2)')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 2: Frequency f1 Signal
+plt.subplot(4, 1, 2)
+plt.plot(t, signal_f1)
+plt.title('Individual Frequency f1 Signal')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 3: Frequency f2 Signal
+plt.subplot(4, 1, 3)
+plt.plot(t, signal_f2)
+plt.title('Individual Frequency f2 Signal')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 4: Frequency Spectrum
+plt.subplot(4, 1, 4)
+plt.plot(freq, np.abs(fft_values))
+plt.title('Frequency Spectrum of Composite Signal')
+plt.xlabel('Frequency [Hz]')
+plt.ylabel('Amplitude')
+plt.xlim([0, 30])
+
+# Highlighting the dominant frequencies in the spectrum
+plt.axvline(x=f1, color='green', linestyle='--', label='Frequency f1')
+plt.axvline(x=f2, color='red', linestyle='--', label='Frequency f2')
+
+plt.legend()
+plt.tight_layout()
+plt.show()

testy/getlastcalendardate.py

@@ -0,0 +1,28 @@
+from alpaca.data.historical import CryptoHistoricalDataClient, StockHistoricalDataClient
+from alpaca.data.requests import CryptoLatestTradeRequest, StockLatestTradeRequest, StockLatestBarRequest, StockTradesRequest, StockBarsRequest
+from alpaca.data.enums import DataFeed
+from config import API_KEY, SECRET_KEY, MAX_BATCH_SIZE
+import datetime
+import time
+from alpaca.data import Quote, Trade, Snapshot, Bar
+from alpaca.data.models import BarSet, QuoteSet, TradeSet
+from alpaca.data.timeframe import TimeFrame
+# import mplfinance as mpf
+import pandas as pd
+from rich import print
+from v2realbot.utils.utils import zoneNY
+from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY
+from alpaca.trading.requests import GetCalendarRequest
+from alpaca.trading.client import TradingClient
+
+parametry = {}
+
+clientTrading = TradingClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=False)
+
+#get previous days bar
+
+datetime_object_from = datetime.datetime(2023, 10, 11, 4, 0, 00, tzinfo=datetime.timezone.utc)
+datetime_object_to = datetime.datetime(2023, 10, 16, 16, 1, 00, tzinfo=datetime.timezone.utc)
+calendar_request = GetCalendarRequest(start=datetime_object_from,end=today)
+cal_dates = clientTrading.get_calendar(calendar_request)
+print(cal_dates)

testy/histogramnumpy.py

@@ -0,0 +1,34 @@
+import numpy as np
+
+data = np.array([1,2,3,4,3,2,4,7,8,4,3,0,0,0,0,9,9,9,11,23,2,3,4,29,23])
+
+counts, bin_edges = np.histogram(data, bins=4)
+# returns a tuple containing two arrays:
+# counts: An array containing the number of data points in each bin.
+# bin_edges: An array containing the edges of each bin.
+#(array([10,  6,  0,  1]), array([ 1. ,  6.5, 12. , 17.5, 23. ]))
+print(counts, bin_edges)
+
+edge_from = bin_edges[3]
+edge_to = bin_edges[4]
+print(edge_from)
+print(edge_to)
+print("test where", data[np.where((edge_from<data) & (data<edge_to))])
+
+ctvrty_bin = [datum for datum in data if edge_from <= datum <= edge_to]
+
+print(np.mean(ctvrty_bin))
+
+#print(histo[0][-2])
+
+bins = 4
+mean_of_4th_bin = np.mean(data[np.where(np.histogram(data, bins)[1][3] <= data)[0]])
+# print(mean_of_4th_bin)
+# print(mean_of_fourth_bucket)
+
+
+
+
+# Print the data from the 3rd bin using a list comprehension
+
+#print([datum for datum in data if bin_edges[2] <= datum < bin_edges[3]])

testy/numpylistmutability.py

@@ -0,0 +1,14 @@
+import numpy as np
+from array import array
+
+# Original list
+puvodni = array('i', [1, 2, 3, 4])
+
+# Create a NumPy array using the original list
+numpied = np.array(puvodni)
+
+# Now, if puvodni changes, numpied will be updated as well
+puvodni.append(5)
+
+# Check the updated numpied array
+print(numpied)

testy/picklequeue.py

@@ -0,0 +1,25 @@
+import queue
+import msgpack
+# Creating the original queue
+original_queue = queue.Queue()
+new_queue = queue.Queue()
+
+# Adding elements to the original queue
+original_queue.put(5)
+original_queue.put(10)
+original_queue.put(15)
+
+# Pickling the queue
+pickled_queue = msgpack.packb(original_queue)
+
+# Unpickling the queue
+unpickled_queue = msgpack.unpackb(pickled_queue)
+# Pickling the queue
+new_queue.queue = unpickled_queue.queue
+
+
+print(new_queue)
+
+# Checking the contents of the new queue
+while not new_queue.empty():
+    print(new_queue.get())

v2realbot/ENTRY_ClassicSL_v01.py

@@ -16,6 +16,10 @@ from v2realbot.strategyblocks.newtrade.signals import signal_search
 from v2realbot.strategyblocks.activetrade.activetrade_hub import manage_active_trade
 from v2realbot.strategyblocks.inits.init_indicators import initialize_dynamic_indicators
 from v2realbot.strategyblocks.inits.init_directives import intialize_directive_conditions
+from alpaca.trading.requests import GetCalendarRequest
+from alpaca.trading.client import TradingClient
+from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR, OFFLINE_MODE
+from alpaca.trading.models import Calendar
 
 print(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 """"
@@ -141,11 +145,42 @@ def init(state: StrategyState):
 
 
     #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)
+    #get 30 days (history_datetime_from musí být alespoň -2 aby to bralo i vcerejsek)
+    #history_datetime_from = time_to - timedelta(days=40)
+    #get previous market day
+    #time_to = time_to - timedelta(days=1)
+
+    #time_to = time_to.date()
+
+    #vypocet posledniho market dne - do samostatne funkce get_previous_market_day(today)
+    #time_to = time_to.date()
+
+    today = time_to.date()
+    several_days_ago = today - timedelta(days=40)
+    printanyway(f"{today=}",f"{several_days_ago=}")
+    clientTrading = TradingClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=False)
+    #get all market days from here to 40days ago
+    calendar_request = GetCalendarRequest(start=several_days_ago,end=today)
+    cal_dates = clientTrading.get_calendar(calendar_request)
+
+    #find the first market day - 40days ago
+    #history_datetime_from = zoneNY.localize(cal_dates[0].open)
+    history_datetime_from = cal_dates[0].open
+
+    # Find the previous market day
+    history_datetime_to = None
+    for session in reversed(cal_dates):
+        if session.date < today:
+            #history_datetime_to = zoneNY.localize(session.close)
+            history_datetime_to = session.close
+            break
+    printanyway("Previous Market Day Close:", history_datetime_to)
+    printanyway("Market day 40days ago Open:", history_datetime_from)
+
+    printanyway(history_datetime_from, history_datetime_to)
+    #az do predchziho market dne dne
+    state.dailyBars = get_historical_bars(state.symbol, history_datetime_from, history_datetime_to, TimeFrame.Day)
+    printanyway("daily bars FILLED", state.dailyBars)
     #zatim ukladame do extData - pro instant indicatory a gui
     state.extData["dailyBars"] = state.dailyBars
 

v2realbot/loader/cacher.py

@@ -31,13 +31,14 @@ import threading
 class Cacher:  
     def __init__(self,
                  
-                 rectype: RecordType = RecordType.BAR,
+                 rectype: RecordType,
                  resolution: 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)

v2realbot/static/index.html

@@ -297,6 +297,8 @@
                         <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_export_xml" class="btn btn-outline-success btn-sm">Export xml</button>
+                        <button id="button_export_csv" class="btn btn-outline-success btn-sm">Export csv</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>

v2realbot/static/js/archivetables.js

@@ -40,9 +40,101 @@ function refresh_arch_and_callback(row, callback) {
     });
 }
 
+//type ("text/csv","application/xml"), filetype (csv), filename
+function downloadFile(type, filetype, filename, content) {
+    var blob = new Blob([content], { type: type });
+    var url = window.URL.createObjectURL(blob);
+    var link = document.createElement("a");
+    link.href = url;
+    link.download = filename +"."+filetype;
+    link.click();
+}
+
+// Function to convert a JavaScript object to XML
+function convertToXml(data) {
+    var xml = '<?xml version="1.0" encoding="UTF-8"?>\n<trades>\n';
+    data.forEach(function (item) {
+        xml += '  <trade>\n';
+        Object.keys(item).forEach(function (key) {
+            xml += '    <' + key + '>' + item[key] + '</' + key + '>\n';
+        });
+        xml += '  </trade>\n';
+    });
+    xml += '</trades>';
+    return xml;
+}
+
+// Function to convert a JavaScript object to CSV
+function convertToCsv(data) {
+    var csv = '';
+    // Get the headers
+    var headers = Object.keys(data[0]);
+    csv += headers.join(',') + '\n';
+
+    // Iterate over the data
+    data.forEach(function (item) {
+        var row = headers.map(function (header) {
+            return item[header];
+        });
+        csv += row.join(',') + '\n';
+    });
+
+    return csv;
+}
+
+function prepare_export() {
+    rows = archiveRecords.rows('.selected');
+    var trdList = []
+    if(rows.data().length > 0 ) {
+        //console.log(rows.data())
+        // Loop through the selected rows and display an alert with each row's ID
+        rows.every(function (rowIdx, tableLoop, rowLoop ) {
+            var data = this.data()
+            data.metrics.prescr_trades.forEach((trade) => {
+                new_obj = {}
+                new_obj["entry_time"] = (trade.entry_time) ? new Date(trade.entry_time * 1000) : null
+                new_obj["entry_time"] = (new_obj["entry_time"]) ? new_obj["entry_time"].toLocaleString('cs-CZ', {
+                    timeZone: 'America/New_York',
+                  }) : null
+                new_obj["exit_time"] = (trade.exit_time) ? new Date(trade.exit_time * 1000):null
+                new_obj["exit_time"] = (new_obj["exit_time"]) ? new_obj["exit_time"].toLocaleString('cs-CZ', {
+                    timeZone: 'America/New_York',
+                  }) : null
+                new_obj["direction"] = trade.direction
+                new_obj["profit"] = trade.profit
+                new_obj["rel_profit"] = trade.rel_profit
+                trdList.push(new_obj)
+            })
+        });
+    }
+    return trdList
+}
+
 $(document).ready(function () {
     archiveRecords.ajax.reload();
 
+    //button export
+    $('#button_export_xml').click(function () {
+            xmled = convertToXml(prepare_export())
+            console.log(xmled)
+            //csved = convertToCsv(trdList)
+            //console.log(csved)
+            //type ("text/csv","application/xml"), filetype (csv), filename, content
+            //downloadFile("text/csv","csv", "trades", csved)
+            downloadFile("application/xml","xml", "trades", xmled)
+            //console.log(jsonobj)
+            //downloadCsv(jsonobj); 
+    });
+
+    //button export
+    $('#button_export_csv').click(function () {
+        csved = convertToCsv(prepare_export())
+        console.log(csved)
+        //type ("text/csv","application/xml"), filetype (csv), filename, content
+        downloadFile("text/csv","csv", "trades", csved)
+        //console.log(jsonobj)
+        //downloadCsv(jsonobj); 
+});
     
     //button select page
     $('#button_selpage').click(function () {

v2realbot/strategyblocks/activetrade/helpers.py

@@ -161,23 +161,32 @@ def get_profit_target_price(state, data, direction: TradeDirection):
 
     #mame v direktivve ticky
     if isinstance(def_profit, (float, int)):
-        normalized_def_profit = normalize_tick(state, data, float(def_profit))
-
-        state.ilog(lvl=0,e=f"PROFIT {def_profit=} {normalized_def_profit=}")
-
-        base_price = state.avgp if state.avgp != 0 else data["close"]
-
-        to_return = price2dec(float(base_price)+normalized_def_profit,3) if direction == TradeDirection.LONG else price2dec(float(base_price)-normalized_def_profit,3)
+        to_return = get_normalized_profitprice_from_tick(state, data, def_profit, direction)
     #mame v direktive indikator
     elif isinstance(def_profit, str):
         to_return = float(value_or_indicator(state, def_profit))
 
-        if direction == TradeDirection.LONG and to_return < data['close'] or direction == TradeDirection.SHORT and to_return > data['close']:
-            state.ilog(lvl=1,e=f"SPATNA HODOTA DOTAZENEHO PROFITU z ind {def_profit} {to_return=} {smer} {data['close']}")
-            raise Exception(f"SPATNA HODOTA DOTAZENEHO PROFITU z ind{def_profit} {to_return=} {smer} {data['close']}")
-        state.ilog(lvl=1,e=f"DOTAZENY PROFIT z indikatoru {def_profit} {to_return=}")
+        #min profit (ochrana extremnich hodnot indikatoru)
+        directive_name = 'profit_min_ind_tick_value'
+        profit_min_ind_tick_value = get_override_for_active_trade(state, directive_name=directive_name, default_value=def_profit_both_directions)
+        profit_min_ind_price_value = get_normalized_profitprice_from_tick(state, data, profit_min_ind_tick_value, direction)
+
+        #ochrana pri nastaveni profitu prilis nizko
+        if direction == TradeDirection.LONG and to_return < profit_min_ind_price_value or direction == TradeDirection.SHORT and to_return > profit_min_ind_price_value:
+            state.ilog(lvl=1,e=f"SPATNA HODOTA DOTAZENEHO PROFITU z ind {def_profit} {to_return=} MINIMUM:{profit_min_ind_price_value} {smer} {data['close']}")
+            #fallback na profit_min_ind_price_value
+            to_return = profit_min_ind_price_value
+        state.ilog(lvl=1,e=f"PROFIT z indikatoru {def_profit} {to_return=}")
     return to_return
 
+##based on tick a direction, returns normalized prfoit price (LONG = avgp(nebo currprice)+norm.tick, SHORT=avgp(or currprice)-norm.tick)
+def get_normalized_profitprice_from_tick(state, data, tick, direction: TradeDirection):
+        normalized_tick = normalize_tick(state, data, float(tick))
+        base_price = state.avgp if state.avgp != 0 else data["close"]
+        returned_price = price2dec(float(base_price)+normalized_tick,3) if direction == TradeDirection.LONG else price2dec(float(base_price)-normalized_tick,3)
+        state.ilog(lvl=0,e=f"NORMALIZED TICK {tick=} {normalized_tick=} NORM.PRICE {returned_price}")
+        return returned_price
+
 def get_max_profit_price(state, data, direction: TradeDirection):
     if direction == TradeDirection.LONG:
         smer = "long"

v2realbot/strategyblocks/indicators/RSI.py

@@ -4,6 +4,7 @@ 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, value_or_indicator
+import numpy as np
 
 #RSI INDICATOR
 # type = RSI, source = [close, vwap, hlcc4], rsi_length = [14], MA_length = int (optional), on_confirmed_only = [true, false]
@@ -39,7 +40,8 @@ def populate_dynamic_RSI_indicator(data, state: StrategyState, name):
                     rsi_length = delka
 
                 rsi_res = rsi(source, rsi_length)
-                rsi_value = round(rsi_res[-1],4)
+                val =  rsi_res[-1] if np.isfinite(rsi_res[-1]) else 0
+                rsi_value = round(val,4)
                 state.indicators[name][-1]=rsi_value
                 state.ilog(lvl=0,e=f"IND {name} RSI {rsi_value}")
 

v2realbot/strategyblocks/indicators/atr.py

@@ -3,7 +3,8 @@ from v2realbot.indicators.oscillators import rsi
 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 traceback import format_exc
-
+from v2realbot.strategyblocks.indicators.helpers import get_source_series
+from collections import defaultdict
 #TODO ATR INDICATOR -  predelat na CUSTOM a udelat scitani a odecteni od close (atru, atrd)
 # type = ATR, ĺength = [14], on_confirmed_only = [true, false]
 def populate_dynamic_atr_indicator(data, state: StrategyState, name):
@@ -16,17 +17,28 @@ def populate_dynamic_atr_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)
     atr_length = int(safe_get(options, "length",5))
+
+    # priceline with high/low/close (bars/daily bars)
+
+    #TODO tady jsem skoncil - dodelat
+    source = safe_get(options, "source", "bars")
+    source_dict = eval(f"state.{source}")
+
     if on_confirmed_only is False or (on_confirmed_only is True and data['confirmed']==1):
         try:
-            source_high = state.bars["high"][-atr_length:]
-            source_low = state.bars["low"][-atr_length:]
-            source_close = state.bars["close"][-atr_length:]
+            delka_close = len(source_dict["close"])
+            if atr_length > delka_close:
+                atr_length = delka_close
+
+            source_high = source_dict["high"][-atr_length:]
+            source_low = source_dict["low"][-atr_length:]
+            source_close = source_dict["close"][-atr_length:]
             #if len(source) > ema_length:
             atr_value = atr(source_high, source_low, source_close, atr_length)
             val = round(atr_value[-1],4)
             state.indicators[name][-1]= val
             #state.indicators[name][-1]= round2five(val)
-            state.ilog(lvl=0,e=f"IND {name} ATR {val} {atr_length=}")
+            state.ilog(lvl=0,e=f"IND {name} on {source} ATR {val} {atr_length=}")
             #else:
             #    state.ilog(lvl=0,e=f"IND {name} EMA necháváme 0", message="not enough source data", source=source, ema_length=ema_length)
         except Exception as e:

v2realbot/strategyblocks/indicators/custom/basestats.py

@@ -112,6 +112,16 @@ def basestats(state, params, name):
             val = float(np.max(dominant_frequencies))
             return 0, val
         
+    elif func == "histogram":
+        #takes only first N - items
+        dt = np.array(source_array)
+        #creates 4 buckets
+        bins = 4
+        mean_of_4th_bin = np.mean(dt[np.where(np.histogram(dt, bins)[1][3] <= dt)[0]])
+        if not np.isfinite(mean_of_4th_bin):
+            mean_of_4th_bin = 0
+        return 0, float(mean_of_4th_bin)
+
     elif func == "maxima":
         if len(source_array) < 3:
             return 0, state.bars["high"]

v2realbot/strategyblocks/indicators/custom/expression.py

@@ -3,6 +3,7 @@ import numpy as np
 from rich import print as printanyway
 from traceback import format_exc
 import v2realbot.utils.utils as utls
+from copy import deepcopy
 # 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
 
 
@@ -22,15 +23,39 @@ def expression(state: StrategyState, params, name):
         if operation is None :
             return -2, "required param missing"
         
+        #list of indicators that should be converted beforehands
+        convertToNumpy = utls.safe_get(params, "convertToNumpy", [])
+
         state.ilog(lvl=0,e=f"BEFORE {name}:{funcName} {operation=}", **params)
 
+        #nyni vytvarime kazdou iteraci nove numpy pole
+        #pro optimalizaci by slo pouzit array.array ktery umi
+        #sdilet s numpy pamet a nevytvari se pak kopie pole
+        #nevyhoda: neumi comprehensions a dalsi
+        #viz https://chat.openai.com/c/03bb0c1d-450e-4f0e-8036-d338692c1082
+
+        #opt by chatGPT
+        temp_ind_mapping = {k: np.array(v) if k in convertToNumpy else v for k, v in state.ind_mapping.items()}
+     
+        # temp_ind_mapping = {}
+        # if len(convertToNumpy) > 0:
+        #     #mozna msgpack ext in
+        #     temp_ind_mapping = deepcopy(state.ind_mapping)
+        #     for key in convertToNumpy:
+        #         try:
+        #             temp_ind_mapping[key] = np.array(state.ind_mapping[key])
+        #             print(f"numpyed {key}")
+        #         except Exception:
+        #             pass
+        
+        # if len(temp_ind_mapping) == 0:
+        #     temp_ind_mapping = state.ind_mapping
+
         #pro zacatek eval
-        val = eval(operation, {'state': state, 'np': np, 'utls': utls}, state.ind_mapping)
+        val = eval(operation, {'state': state, 'np': np, 'utls': utls}, temp_ind_mapping)
 
         #printanyway(val)
-
-        if not np.isfinite(val):
-            val = 0
+        val = 0 if not np.isfinite(val) else val
         #val = ne.evaluate(operation, state.ind_mapping)
 
         state.ilog(lvl=1,e=f"IND {name}:{funcName} {operation=} res:{val}", **params)

v2realbot/utils/historicals.py

@@ -88,5 +88,5 @@ def get_historical_bars(symbol: str, time_from: datetime, time_to: datetime, tim
 
     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("puvodni bars", bars["BAC"])
     return convert_daily_bars(bars[symbol])