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View Code? Open in Web Editor NEWCommon financial technical indicators implemented in Pandas.
License: GNU Lesser General Public License v3.0
Common financial technical indicators implemented in Pandas.
License: GNU Lesser General Public License v3.0
So I was experimenting with some data and it turned out that the VORTEX indicator does just have insane predictive performance. Except it does not. In finta/finta.py
line 987, the it reads:
VMP = pd.Series(ohlc["high"] - ohlc["low"].shift(-1).abs())
But actually the .shift(-1)
shifts values from 1 in the future to the present. Probably .shift(1) was intended, correct me if I'm wrong. Someone should go through the code and carefully review all ocurrences of .shift(-n)
Hello, first of all thanks for your awesome work!
Not a strong programmer myself so I've been struggling to convert this indicator into python code.
I think it will be an interesting addition to the indicator list, it deals with volatility mostly. I currently only have it in c++ code from prorealcode, which is the most understandable version of it.
Here is some information on the code and the discussions on it.
https://www.prorealcode.com/prorealtime-indicators/mark-whistlers-wave-pm/
http://fxcodebase.com/code/viewtopic.php?f=38&t=64011
https://www.prorealcode.com/topic/possible-to-code-this-in-prt/
I think the readme contains a slightly wrong and misleading section.
From the readme.md:
select only price column, resample by time period and return daily ohlc (you can choose different time period)
ohlc = df["close"].resample("24h").ohlc()
This does not work / make sense, since pd.Series
does not have a ohlc()
method.
I'm not sure what this tries to accomplish either, since utils.resample does require a dataframe, not a single series.
Lines 14 to 19 in 21a4242
standardize column names of your source
df.columns = ["date", 'close', 'volume']
I think this should include all ohlc[v] columns - to be in line with the comment 2 lines before that:
finta expects properly formated
ohlc
DataFrame, with column names inlowercase
:
["open", "high", "low", close"] and ["volume"] for indicators that expectohlcv
input.
I used period=5
for calculating a STOCH column and when I created a STOCHD column I realised that the library uses the default period=14
for calculating STOCH inside STOCHD which results in wrong calculations if we used a different period for STOCH e.g. period=5
.
Could we have an additional parameter for STOCH's period in the STOCHD signature?
Alternatively, from a quick google search I realised the STOCHD always uses period=3
, so maybe pass the period from STOCHD to STOCH and set the rolling window inside STOCHD to 3?
I am trying to do simple resampling with the resample
, like this:
resample(data, "5d")
Data contains just normal OHLC data, other calls like TA.ATR work on this data but resample
and resample_calendar
throw the following exception:
Traceback (most recent call last):
File "test.py", line 21, in <module>
data = resample(data, "5d")
File "/home/ppawel/.local/lib/python3.7/site-packages/finta/utils.py", line 19, in resample
return df.resample(interval).agg(d)
File "/home/ppawel/.local/lib/python3.7/site-packages/pandas/core/resample.py", line 281, in aggregate
result, how = self._aggregate(func, *args, **kwargs)
File "/home/ppawel/.local/lib/python3.7/site-packages/pandas/core/base.py", line 357, in _aggregate
raise SpecificationError("nested renamer is not supported")
pandas.core.base.SpecificationError: nested renamer is not supported
I am currently using pandas 1.0.3 in this project.
Any hints or is that perhaps incompatibility with pandas 1.0?
Edit: I can confirm that it works as expected with pandas 0.25.
df['SARs']=TA.SAR(df,0.02,0.02) tried with the the output value is too far.
Tested this TA.CCI(df) output comparing with tradingview.com(TV) chart inbuilt CCI ,the output is not matching .
In finta.py did a small change and the output CCI is almost closest to tradingview output but not sure how this is very close to the result shown in TV.
Modified Code:def CCI(cls... part
tp = cls.TP(ohlc)
return pd.Series(
(tp - tp.rolling(window=period,center=False).mean()) / (0.015 * tp.rolling(window=period,center=False).std()),
name="{0} period CCI".format(period),
)
Here is the calculation method ,TV is the standard worldwide proven indicator methods ,it would be good if we see your indicator output same as TV result. I have checked many source yours is very closest.Run the above code you will get only 20points differrence in the result.
In summary if we calculate the "Mean Deviation" correctly the output will be matching with global standards.
There are several steps involved in calculating the Commodity Channel Index.
The following example is for a typical 20 Period CCI:
CCI = (Typical Price - 20 Period SMA of TP) / (.015 x Mean Deviation)
Typical Price (TP) = (High + Low + Close)/3
Constant = .015
The Constant is set at .015 for scaling purposes.
By including the constant, the majority of CCI values will fall within the 100 to -100 range.
There are three steps to calculating the Mean Deviation.
I'm unable to resample a dataset from interval "1m" to "15m".
(dataset)
Code:
from finta import TA
from finta import utils
BTCUSDT = utils.resample(BTCUSDT, "15m")
BTCUSDT
Error message:
C:\Users\Kobe\anaconda3\lib\site-packages\finta\utils.py:19: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version.
For column-specific groupby renaming, use named aggregation
>>> df.groupby(...).agg(name=('column', aggfunc))
return df.resample(interval).agg(d)
Example:
import finta
print(df)
macd = finta.TA.EV_MACD(df)
Output(Partial)
open close high low volume
2018-03-19 264.293667 261.528421 265.308880 259.720376 109208442.0
2018-03-20 261.963513 261.973181 262.669327 261.228692 59757271.0
2018-03-21 261.924838 261.470409 264.216318 261.238361 78709578.0
2018-03-22 259.033899 254.934374 259.962094 254.634645 148785916.0
2018-03-23 255.417809 249.500570 255.775550 249.287859 183534751.0
[446 rows x 5 columns]
macd = finta.TA.EV_MACD(df)
File "/home/xxx/.local/lib/python3.6/site-packages/finta/finta.py", line 517, in EV_MACD
evwma_slow = cls.EVWMA(ohlcv, period_slow)
File "/home/xxx/.local/lib/python3.6/site-packages/finta/finta.py", line 317, in EVWMA
for x, y in zip(x.fillna(0).iteritems(), y.iteritems()):
UnboundLocalError: local variable 'x' referenced before assignment
Versions:
python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
Pandas 0.25.3
I know you have the indicators listed in the file at https://github.com/peerchemist/finta/blob/master/finta/finta.py but you should also put a simple list of them on your main page at https://github.com/peerchemist/finta .
def VORTEX(cls, ohlc, period=14):
VMP = pd.Series(ohlc['high'] - ohlc['low'].shift(-1).abs())
VMM = pd.Series(ohlc['low'] - ohlc['high'].shift(-1).abs())
VMPx = VMP.rolling(window=period).sum().tail(period)
VMMx = VMM.rolling(window=period).sum().tail(period)
VIp = pd.Series(VMPx / cls.TR(ohlc), name='VIp').interpolate(method='index')
VIm = pd.Series(VMMx / cls.TR(ohlc), name='VIm').interpolate(method='index')
return pd.concat([VIm, VIp], axis=1)
Why are you taking the tail() for VMPx and VMMx? This will just grab the last 14 values of that sum instead of taking the rolling sum of the whole array. Maybe I am missing something?
Implement regtests against widely used Ta-Lib.
Ta-Lib is not the most accurate library out there, but it is widely used and a good reference. Goal is the check if values produced by finta are in the same "ballpark" as the numbers produced by the Ta-Lib. Ta-Lib specifically has problems with length of the dataset, as explained here: https://www.quantopian.com/posts/ta-lib-rsi-using-something-other-than-close-price.
Regtests should enable use to gauge out which finta indicators are absolutely incorrect and should be refactored.
I've read the code on the ichimoku cloud, would it be possible to make variables (9,26,52,26) to be malleable so we can do:
TA.ichimoku(ohcl, 20,60,120,30)
in an easy way rather than modifying the code from finta, if this can be done already, please let me know
Thanks!
The Vortex Indicator is not correctly implemented.
This should be the correct implementation based on this rules https://www.investopedia.com/terms/v/vortex-indicator-vi.asp :
VMP = pd.Series((ohlc["high"] - ohlc["low"].shift(1)).abs())
VMM = pd.Series((ohlc["low"] - ohlc["high"].shift(1)).abs())
VMPx = VMP.rolling(window=period).sum()
VMMx = VMM.rolling(window=period).sum()
TR = cls.TR(ohlc).rolling(window=period).sum()
VIp = pd.Series(VMPx / TR, name="VIp").interpolate(method="index")
VIm = pd.Series(VMMx / TR, name="VIm").interpolate(method="index")
return pd.concat([VIm, VIp], axis=1)
Hi
Can you please check EMA calculation the output is close but not matching with Tv outputs.
Better to test this with TV and realign the code.
I have tried and tried to format my panda dataframe to make this library work on it, but, I keep getting 'close' problems. I'll copy the error here along with the printout of my dataframe.
The error comes up from this line in the code:
TA.SMA(dataTA, 42)
date open high low close volume
0 01/18/2020 17:00:00 0.000311 0.000311 0.000311 0.000311 1371.0
1 01/18/2020 17:01:00 0.000311 0.000311 0.000312 0.000312 13969.0
2 01/18/2020 17:02:00 0.000312 0.000312 0.000312 0.000312 2263.0
3 01/18/2020 17:03:00 0.000312 0.000311 0.000312 0.000312 8030.0
4 01/18/2020 17:04:00 0.000312 0.000311 0.000312 0.000311 11789.0
... ... ... ... ... ... ...
276820 07/29/2020 14:42:00 0.000636 0.000636 0.000638 0.000636 2347.7
276821 07/29/2020 14:43:00 0.000636 0.000636 0.000637 0.000636 1028.5
276822 07/29/2020 14:44:00 0.000637 0.000637 0.000638 0.000637 442.4
276823 07/29/2020 14:45:00 0.000637 0.000636 0.000637 0.000636 399.9
276824 07/29/2020 14:46:00 0.000636 0.000634 0.000636 0.000634 2370.4
[276825 rows x 6 columns]
Traceback (most recent call last):
File "/home/relskhan/anaconda3/envs/tesr/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2646, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'close'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "ohlcGraphing.py", line 65, in <module>
TA.SMA(dataTA, 42)
File "/home/relskhan/anaconda3/envs/tesr/lib/python3.7/site-packages/finta/finta.py", line 18, in SMA
ohlc[column].rolling(window=period).mean(),
File "/home/relskhan/anaconda3/envs/tesr/lib/python3.7/site-packages/pandas/core/frame.py", line 2800, in __getitem__
indexer = self.columns.get_loc(key)
File "/home/relskhan/anaconda3/envs/tesr/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2648, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'close'
The one thing I'm curious about is how to formate that 'date' column. The example only shows by the day, I'm trying to get all the info by the minute and then use built in library to resample. I keep thinking that the date column has to be causing a problem here ... even though it says the 'close' column?
I'm using Python 3.7.6 in Anaconda, I pulled finTA from here.
PSAR is both valuable and often used by traders. Can we implement PSAR?
I am trying using this fantastic library. But i have some issues. Is there any example code? I want to learn using pandas datasets and finta together.
Issue #61 addressed this but only the TR rolling issue was implemented.
VMP and VMM values need to be absolute values. like shown below.
VMP = pd.Series((ohlc["high"] - ohlc["low"].shift(1)).abs())
VMM = pd.Series((ohlc["low"] - ohlc["high"].shift(1)).abs())
VMPx = VMP.rolling(window=period).sum()
VMMx = VMM.rolling(window=period).sum()
TR = cls.TR(ohlc).rolling(window=period).sum()
VIp = pd.Series(VMPx / TR, name="VIp").interpolate(method="index")
VIm = pd.Series(VMMx / TR, name="VIm").interpolate(method="index")
return pd.concat([VIm, VIp], axis=1)
The code for VORTEX currently has this (no parenthesis around columns being subtracted and calling abs() on that):
VMP = pd.Series(ohlc["high"] - ohlc["low"].shift().abs())
VMM = pd.Series(ohlc["low"] - ohlc["high"].shift().abs())
In RSI I don't thin we need [1:]
in
delta = ohlc['close'].diff()[1:]
.
It causes the returned series to have one less row. If we just write
delta = ohlc['close'].diff()
then it's the correct shape with nan in the, previously missing, first row.
Hi,
First, thank you for a great library.
I have been trying to apply TA.HMA
to a dataframe structured like this:
open high low close volume
date symbol
2019-09-15 00:00:00+00:00 BTC 10363.2000 10380.0000 10270.1000 10311.3000 1190.540645
ETC 6.3660 6.4490 6.3010 6.3230 3848.955337
XTZ 1.0000 1.0194 0.9863 1.0062 110247.320200
XMR 75.9400 76.4300 74.0100 75.7000 976.875002
BCH 305.0000 307.6000 302.1000 303.0000 1771.432299
2019-09-16 00:00:00+00:00 BTC 10311.3000 10374.7000 10311.2000 10329.0000 353.164112
ETC 6.3230 6.4490 6.3230 6.4150 1027.900751
EOS 4.0707 4.1461 4.0707 4.0948 16762.763057
XMR 75.7000 76.2500 75.4800 75.9300 353.116738
XTZ 1.0062 1.0077 1.0020 1.0039 5323.913412
Where date
and symbol
are used as indices. But when running:
df['hma'] = df.groupby('symbol').apply(lambda x:TA.HMA(x))
I get:
TypeError: incompatible index of inserted column with frame index
(full error output attached: error.txt)
Any ideas as to what I'm doing wrong or do you have any examples of how to do this?
Thank you,
Luis
Hi,
shouldn't the formula for STOCH be (ohlc['close'] - lowest_low) / (highest_high - lowest_low)
instead of (highest_high - ohlc['close']) / (highest_high - lowest_low) ?
And another question,
is there a reason we have min_periods=period - 1
in SMA and some other indicators?
Seems weird to get the first average over period-1 instead of period (like the rest).
In addition, it seems inconsistent for SMA to produce one more row than WMA.
Thanks!
The ZLEMA function seems to only return the transformed data (before handling it to the ema method) but doesn't actually perform the EMA on the data.
The method code is:
lag = (period - 1) / 2
return pd.Series(
(ohlc["close"] + (ohlc["close"].diff(lag))),
name="{0} period ZLEMA.".format(period),
)
Which doesn't include any EMA i.e. it is missing the last step in the ZLEMA pseudocode: https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
may be you can double check..
but i think this line https://github.com/peerchemist/finta/blob/master/finta/finta.py#L798
should be
ohlc["down_move"] = -ohlc["low"].diff()
Check for standardness of the output.
Hi. Think I might have spotted an issue with the bbands coding.. the window isn't rolling so it gives a single value. Switching it to ohlc["close"].rolling(window=period).std() fixed it for me.
Apologies if I'm wrong. Quite new to python. Cheers.
How implementing MOBO indicator (Momentum breakout)?
It's a derivative of the a Bollinger band but:
"MOBO bands are based on a zone of 0.80 standard deviation with a 10 period look-back"
If the price breaks out of the MOBO band it can signify a trend move or price spike
Explanation around 3:58 of video
https://www.youtube.com/watch?v=Y6q06t4mp2c
One thinkscript implementation that might help:
#Bollinger Bands MOBO(MOmentum BreakOut) [Thinkscript]
#============================================
#Explanation of how this works. +/- 0.8 std deviation Bollinger Bands are the criteris for bullish/bearish plots. When the close rises above the upper band the signal is bullish and stays bullish until the ose moves below the lower band when the plot turns to bearish and remains bearish until the close rises above the upper band.
input MOBO_length = 10;#hint MOBO_length:The agg-bars used in the standard deviation(SD) calculation to define the upper and lower bands.
input Num_Dev_Dn = -0.8;#hint Num_Dev_Dn:The SD of the lower band. Similar to the 2,0 SD used in the Bollinger Bands
input Num_Dev_up = 0.8;#hint Num_Dev_up:The SD of the upper band. Similar to the 2,0 SD used in the Bollinger Bands
def sDev = StDev(data = c, length = MOBO_length);
def Midmobo = Average(c, length = MOBO_length);
def Lowermobo = Midmobo + Num_Dev_Dn * sDev;
def Uppermobo = Midmobo + Num_Dev_up * sDev;
help wanted on how to pull an indicator value from all time-frame at one shot?
on TradingView, i pull out these values from an indicator
['GBPUSD', 'M: -53.00460785710888', 'W: -53.00460785710888', 'D: -18.48497324001634', 'H4: -7.875243664717016', 'H1: -33.94957983193184', 'M30: -32.47422680412316', 'M15: -22.485207100590525', 'M5: -31.232876712327133', 'M2: -41.45454545454205']
['GBPUSD']
let's take RSI as a sample,
TA.RSI(ohlc).tail(10) will pull out for Daily and the past 10 days. i would like to see current data.
no idea how to get these info hence asking for help
pandas 1.03
python 3.7.6
Test against some older more polished code, for example ta-lib, and compare results to spot the regressions and bugs.
There is python wrapper here: https://github.com/mrjbq7/ta-lib
finta.py:1180: RuntimeWarning: divide by zero encountered in log
return pd.Series((np.log((1 + smooth) / (1 - smooth))).ewm(span=3).mean()
Hi,
first of all thank you for your amazing job.
I would like to ask you if do you know how to calculate ssma?
I saw this indicator on iqoption site but I can't found how to calc this.
I found SMA but not SSMA
The STC indicator should returns nan, -inf, and values -/+ x thousands every time I use it.
The STC indicator should return values between 0 and 100.
I feed the function with dataframe from Binance which is compatible with the ohlc format you required.
` time open high low close volume date
0 1.593015e+12 42.41 42.54 42.41 42.54 404.91309 2020-06-24 16:02:00
1 1.593015e+12 42.54 42.56 42.48 42.54 891.49911 2020-06-24 16:03:00
2 1.593015e+12 42.55 42.57 42.50 42.53 273.67014 2020-06-24 16:04:00
3 1.593015e+12 42.53 42.56 42.51 42.55 1502.95409 2020-06-24 16:05:00
4 1.593015e+12 42.54 42.54 42.35 42.37 7764.08264 2020-06-24 16:06:00
.. ... ... ... ... ... ... ...
`
Thanks for your work and your time.
First of all, many thanks for this nice python library. I am using the version 0.4.4.
I computed the RSI on data fetched using ccxt
, and compared it with the RSI shown on cryptowat.ch.
Unfortunately, the RSI calculated by finta did not match the RSI calculated by cryptowat.ch.
After investigation, I realized that the way the exponential moving average (EMA) is calculated is different.
In finta, the EMA is computed with alpha = 2 / (1 + period)
(i.e. the parameter span
in https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html )
In cryptowat.ch, the EMA seems computed with alpha = 1 / period
.
As an example, I took the BTC/USDT symbol from binance.
https://cryptowat.ch/charts/BINANCE:BTC-USDT?period=1h
To test my assumption, I wrote a script for plotting different RSI values (requires ccxt, finta, mplfinance and pandas).
#!/usr/bin/env python3
import ccxt
from finta import TA
from datetime import datetime
import mplfinance as mpf
import pandas as pd
# https://cryptowat.ch/charts/BINANCE:BTC-USDT?period=1h
exchange_id = "binance"
symbol_id = "BTC/USDT"
timeframe = "1h"
period = 14
def fetch(exchange_id, symbol_id, timeframe):
exchange = getattr(ccxt, exchange_id)()
data = exchange.fetch_ohlcv(symbol_id, timeframe=timeframe)
columns = ["date", "open", "high", "low", "close", "volume"]
df = pd.DataFrame.from_records(data, index=["date"], columns=columns)
df.index = pd.to_datetime(df.index, unit="ms")
return df
def my_RSI(df, period):
delta = df["close"].diff()
up, down = delta.copy(), delta.copy()
up[up < 0] = 0
down[down > 0] = 0
_gain = up.ewm(alpha=1.0 / period).mean()
_loss = down.abs().ewm(alpha=1.0 / period).mean()
rs = _gain / _loss
return pd.Series(100.0 - (100.0 / (1.0 + rs)), name=f"{period} period RSI")
def plot(df):
plots = [
mpf.make_addplot(df["TA_RSI_1"], color="r", panel="lower"),
mpf.make_addplot(df["TA_RSI_2"], color="g", panel="lower"),
mpf.make_addplot(df["my_RSI"], color="b", panel="lower"),
]
mpf.plot(df, type="candle", addplot=plots)
df = fetch(exchange_id, symbol_id, timeframe)
df["TA_RSI_1"] = TA.RSI(df, period)
df["TA_RSI_2"] = TA.RSI(df, 2 * period - 1)
df["my_RSI"] = my_RSI(df, period)
print(df.tail(20))
plot(df.tail(100))
The script above outputs the OHLC columns, plus:
TA_RSI_1
: the RSI calculated by finta with span = period
.TA_RSI_2
: the RSI calculated by finta with span = 2 * period - 1
(should be equivalent to setting alpha = 1/period
).my_RSI
: the RSI calculated by my function my_RSI
that computes the EMA with alpha = 1/period
.Here is the resulting table:
open high low close volume TA_RSI_1 TA_RSI_2 my_RSI
date
...
2020-07-11 20:00:00 9205.10 9217.24 9201.55 9216.86 635.630615 45.234820 46.166655 46.166655
2020-07-11 21:00:00 9216.86 9224.68 9207.74 9220.21 436.291969 47.639032 47.260102 47.260102
2020-07-11 22:00:00 9220.19 9236.11 9210.36 9218.41 835.240810 46.376785 46.711094 46.711094
2020-07-11 23:00:00 9218.42 9253.00 9218.02 9234.03 718.692534 57.620173 51.929694 51.929694
2020-07-12 00:00:00 9234.02 9268.52 9230.61 9250.99 994.539305 66.436767 56.868698 56.868698
2020-07-12 01:00:00 9250.99 9294.00 9243.48 9268.56 1859.797089 73.120963 61.304329 61.304329
2020-07-12 02:00:00 9268.56 9280.01 9260.09 9270.16 761.173114 73.671899 61.690696 61.690696
2020-07-12 03:00:00 9270.36 9284.15 9266.51 9272.47 858.523274 74.541190 62.276332 62.276332
2020-07-12 04:00:00 9272.47 9286.69 9261.22 9269.11 788.583932 70.627428 60.819928 60.819928
2020-07-12 05:00:00 9269.17 9277.21 9253.48 9264.84 344.009624 65.578534 58.933693 58.933693
The chart on cryptowat.ch shows the following:
The bottom panel contains the curve of the RSI as computed by cryptowat.ch.
The chart shown by my python script is the following:
The RSI is shown in the bottom panel.
The red curve is the RSI computed by finta (column TA_RSI_1
), the blue curve is the RSI computed by my_RSI
. We can see that the TA_RSI_1
in red has some peaks far above 75, whereas my_RSI
stays below 75 (sometimes reaches nearly 75), the latter matches strongly the behavior of the cryptowat.ch curve. So it supports the fact that cryptowat.ch uses alpha = 1/period
.
I wonder if there is a standard "RSI". This website ( https://www.macroption.com/rsi-calculation/#wilders-smoothing-method ) states that the inventor of the RSI computed the EMA with alpha = 1/period
, that seems to be the one adopted by cryptowat.ch.
Therefore, if it makes sense, my suggestion would be to replace these lines:
Lines 589 to 591 in 8642dd8
by:
# EMAs of ups and downs
_gain = up.ewm(alpha=1.0/period, adjust=adjust).mean()
_loss = down.abs().ewm(alpha=1.0/period, adjust=adjust).mean()
By the way, I think it would also be nice to add a parameter such as "column" to choose on which column of the dataframe the RSI is computed.
Many thanks.
RSI calc does not match Tradingview RSI calculation. Please match calculations. Thanks.
https://learn.tradimo.com/technical-analysis-how-to-work-with-indicators/fractals-indicator
https://www.tradingview.com/script/Uyv9vQc2-Williams-Fractals-Tutorial-Template/
https://www.prorealcode.com/prorealtime-indicators/bill-williams-fractals/
This indicator is hot and widely used, I think it would be a very valuable addition to Finta. As plus, is not available in TA-lib or others. Note that I have edited the title because it's not FRAMA but, "Williams Fractal" which is different.
Hi!
Here is my program that uses Pandas and Finta and MatPlotLib to chart the dataframe.: https://gist.github.com/Industrial/597379bd4ba5168c26e49a9be8817a91/a195a385dd170fa81ca597240418eb178f3ff5dc
I am trying to add Bollinger Bands, and later a Keltner Channel and after that create my own indicator "Squeeze" which uses a BB and a KC and implement it in te same fashion as Finta methods. I hope to at some point land at BUY/SELL signals in a column and one final "Equity" which would model out what would happen to your equity over time running this"algorithm". Does that sound like a correct approach?
What is currently not working is this:
gr,
Tom
Hello, nice lib!
However, there are two minor exceptions:
File ".../finta.py", line 554, in STOCHD
return pd.Series(cls.STOCHK(ohlc).rolling(center=False, window=period, min_periods=period-1).mean(), name="STOCH %D")
AttributeError: type object 'TA' has no attribute 'STOCHK
File ".../finta.py", line 108, in VIDYA
raise NotImplemetedError
NameError: name 'NotImplemetedError' is not defined
Hello,
I am using finta library from Julia. I noticed I must pass price as Pandas DataFrame, and this adds extra function calls and overhead through PyCall. Is there a way I can use finta functions using Numpy Arrays instead of Pandas DataFrames? A lower level API maybe?!
Thank you!
Thank you for the good library!
I want to ask a question that I am not sure if it is my fault or a library issue.
I calculated MACD with 100rows of 1 minute ohlcv historical data.
First, I calculated it with whole 100 rows, and then I calculated it with last 80 rows.
And I saw that result of MACD and SIGNAL of same row (ex. index 99) of each calculation is different.
For example,
df = pd.read_csv("my_ohlcv.csv") #size of my_ohlcv is 100rows
result1 = TA.MACD(df)
df = df.iloc[-80:]
result2 = TA.MACD(df)
print(result1['MACD'].iloc[-1], result2['MACD']iloc[-1])
I expected result1.iloc[-1] and result2.iloc[-1] would be same, but the value was a little bit different.
But when I calculated CCI with same way I did with MACD, this problem doesn't happened and 2 results was totally identical.
Why these things happens with MACD? Do I need bigger size of data for accurate MACD calculation? I also tested it with more than 200rows, but result was not identical either.
I'm using python3.6.9
After creating and adding the mass index indicator in the following format
from finta import TA
dataframe['mi']=TA.MI(dataframe, 9)
i'm only getting the last 9 values of mass index.
Any help would be greatly appreciated.
Values
date open high low close volume mi deltawma vama10 buy sell
...
6782 2019-10-10 14:00:00+00:00 8482.39 8510.00 8456.68 8489.98 1635.848476 NaN 8027.641851 8547.883619 0 0
6783 2019-10-10 15:00:00+00:00 8489.74 8555.40 8472.80 8540.69 1716.237902 NaN 8038.541924 8548.552000 0 0
6784 2019-10-10 16:00:00+00:00 8540.73 8573.93 8509.06 8526.18 1649.275602 NaN 8050.143981 8547.282667 0 0
6785 2019-10-10 17:00:00+00:00 8525.90 8542.15 8488.14 8489.87 850.972090 NaN 8063.350510 8545.511810 0 0
6786 2019-10-10 18:00:00+00:00 8489.87 8531.08 8482.20 8519.47 1089.157289 NaN 8076.693470 8539.016286 0 0
6787 2019-10-10 19:00:00+00:00 8519.98 8556.00 8518.24 8554.13 996.493605 NaN 8086.414588 8535.643619 1 0
6788 2019-10-10 20:00:00+00:00 8554.96 8579.96 8530.26 8552.30 1570.403528 23.473755 8096.960645 8527.076381 0 0
6789 2019-10-10 21:00:00+00:00 8551.87 8589.81 8520.00 8546.76 1239.594837 23.446802 8107.979870 8514.458000 0 0
6790 2019-10-10 22:00:00+00:00 8546.00 8570.01 8528.11 8561.00 852.013904 23.413048 8124.975436 8509.463143 0 0
6791 2019-10-10 23:00:00+00:00 8561.16 8568.00 8530.00 8558.03 735.032822 23.348156 8140.008588 8510.675238 0 0
6792 2019-10-11 00:00:00+00:00 8557.82 8572.91 8491.38 8502.41 1609.400042 23.454690 8154.769788 8511.984762 0 0
6793 2019-10-11 01:00:00+00:00 8502.41 8528.81 8475.00 8525.32 1125.748941 23.593241 8169.234850 8511.672000 1 0
6794 2019-10-11 02:00:00+00:00 8525.35 8527.98 8483.93 8508.85 1038.794517 23.736796 8186.737581 8520.223524 0 0
6795 2019-10-11 03:00:00+00:00 8509.16 8557.00 8499.41 8553.35 986.502991 23.870550 8204.404730 8526.969619 1 0
6796 2019-10-11 04:00:00+00:00 8552.47 8711.11 8530.82 8698.00 4005.058525 24.326677 8223.514110 8534.366190 0 0
Hi, first of all THANK YOU FOR THIS AWESOME LIBRARY
A question:
how can we set custom series name? At the moment it seems not possible setting a custom series name, we must rename it after return. Do you think could be useful to pass an optional parameter to set a custom series name?
Line 1199 in 643e1cf
Hi,
I'd just like to enquire as to why you decided to put this project under the GPL license, instead of something less restrictive.
Robert
Hi,
Thanks a lot for this very useful project, hoping to be helpful I'm submitting this issue about WMA function.
Seems that there are a couple of problem in the WMA calculation
The first one is that the series reversing is not needed
(line 283 finta.py) _weights = pd.Series(np.arange(1, period + 1))
(line 284 finta.py) weights = _weights.iloc[::-1] # reverse the series
(new line) weights = pd.Series(np.arange(1, period + 1))
The second is on line 292:
(line 292 finta.py) close_ = ohlc["close"].rolling(period, min_periods=period)
the fixed value "close" doesn't work if for example the WMA function is called by HMA one, the HMA use to calculate on custom field (not on close)
(NEW line 292 finta.py) close_ = ohlc[column].rolling(period, min_periods=period)
Thanks
Please let me know if this would be possible:
'cloud_green': df['senkou_span_a'] > df['senkou_span_b'],
'cloud_red': df['senkou_span_b'] > df['senkou_span_a']
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