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• Announcements
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  1.9.75.123:

  Adding extra day before dtcmp calc, as otherwise the extradays
  have no effect (#388) Fixing the issue with TWS API Bust events (err code 10225) (#396) Add support for ASK quotes for CASH assets (#395) plus fixes Remove duplicated note (#386) Fixing time.clock for python>=3.8 (#394) Changed file initiation for WriterFile to make it work under
  multi-process optimization (#397) plus fixes Fixed backend loading if a backend is loaded (Google Collab) and
  backend to use on MacOSX Fix: crumb in feeds.YahooFinanceData (#400) Fix color assignments, ticks line widths and some pep-8 improvements Fix timeframe/compression detection when plotting Fix default value for ticks display format on X-axis Sample with ta-lib SAR test Generic support of multiple "text/*" content types for Yahoo
• Blog

  Blog posts

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  It seems the link in the original topic is no longer valid. Here is the updated link: https://www.backtrader.com/blog/2019-05-20-momentum-strategy/momentum-strategy/

• General Discussion

  Feedback, comments and anything in general

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  S

  The more I look into it, the more it becomes obvious that the Binance RSI is no RSI at all, but some other undisclosed type of measurement. I see it being at odds at every value with both talib and my implementation, but what strikes most is that the Binance (so called) "RSI" is much more stable for some reasons.
  For evidence, let's look at this:
  alt text

  In the marked section from 15:55 to 16:45 the Binance RSI(12) peaks exactly at the high 15:55 candle with just about 60, then declines gradually in a linear fashion.(with the exception of that one tiny uptick at the exact only green candle in this decline) The RSI reaches its lowest point exactly at the price local lowest point.

  Now in contrast talib RSI:
  alt text
  These measurements are intentionally offset by +5min since they are meant to be fed into decision making for the current candle, but you can see a similar high at 16:00 (which is the 15:55 RSI) of 59.65. Some critical difference with it not reaching 60, but so far so good. What is most striking however, is that the talib RSI (and mine) actually take up during the price decline.
  This is caused by the moving average "forgetting" reference to the extreme negative candles to the far left, while still holding on to the powerful green uptick in the middle left, so the slow decline after the 15:55 peak is actually not judged as being weighty enough.

  That of course is a stark contrast to the, again, linear decline of Binances so called "RSI" that snuggles its ups and downs completely around the general price trend. Thus, the conventional wisdom RSI and what Binance calls RSI cannot be the same thing, since the RSI formula would have to be changed in ways that completely distance it from the original purpose to achieve this effect.

  If anything, this reminds me more of the Exponentially Weighted Average from machine learning, where you would essentially just take a percentage of the last, say, RSI, and then add the remaining percentage of the current RSI to get some slight but not overshadowing shift.
  Like this (0<=Beta<=1):
  alt text

  The RSI right would be the known formula, but the displayed "RSI", the Binance-RSI, would be a moving average, which would probably work in this snuggle and flattening effect.

  What do you people say? Is this known?

• Indicators/Strategies/Analyzers
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  @ab_trader Thanks for the explanation. I have a feeling that this is somehow different to what happens on a MT4 terminal, right?

• General Code/Help
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  L

  @ab_trader Thanks for answering.

  Here is the solution I came up with, in case it's useful to someone in the future.

  If anyone knows of a better solution I'd love to hear about it.

  from collections import defaultdict import backtrader as bt class MultiDataFeedStrategy(bt.Strategy): """ A base class for stratagies that use multiple data feeds It allows iteration over only those data lines that have valid data. usage: from MultiDataFeedStrategy import MultiDataFeedStrategy class TestStrategy(MultiDataFeedStrategy): def __init__(self): super().__init__() def next(self): for d in self.valid_datas(): print('%s %s %d' % (d.datetime.date(0), d._name, len(d))) """ # params = () def __init__(self): super().__init__() # Define a dict that stores the previous len(d) # off all the datalines. self.previous_len_d = defaultdict(lambda: 0) self.max_param = self.get_max_numeric_param() def prenext(self): self.next() def next(self): # Overide me pass def valid_datas(self): feeds = [d for d in self.datas if self.feed_available(d) and len(d) >= self.max_param] return feeds def feed_available(self, d): # if the previous len(d) equals the current len(d) # it means either the data hasn't started yet or it has ended if self.previous_len_d[d._name] == len(d): return False else: self.previous_len_d[d._name] = len(d) return True def get_max_numeric_param(self): numeric_params = [] for param in self.params._getvalues(): # just in case a param can't convert to a number. try: numeric_params.append(float(param)) except ValueError: pass return round(max(numeric_params))

  Then:

  from MultiDataFeedStrategy import MultiDataFeedStrategy class TestStrategy(MultiDataFeedStrategy): def __init__(self): super().__init__() def next(self): for d in self.valid_datas(): print('%s %s %d' % (d.datetime.date(0), d._name, len(d)))

  Seems to work so far.

• bta-lib
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  H

  @run-out thanks for your reply. Actually I'm using bta-lib on a data stream so I'm reindexing the dataframe on each incoming message, so any ideas for a better solution ?