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So I am trying to get multiple stock prices using pandas and panadas datareader. If I only try to import one ticker it will run fine, but if I use more than one then an error arises. The code is:
import pandas as pd
import pandas_datareader as web
import datetime as dt
stocks = ['BA', 'AMD']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d = web.DataReader(stocks, 'yahoo', start, end)
Though I get the error:
ValueError: Wrong number of items passed 2, placement implies 1
So how do I get around it only allowing to pass 1 stock.
So far I have tried using quandl and google instead, which dont work either. I also have tried pdr.get_data_yahoo but I get the same result. I have also tried yf.download() and still get the same issue. Does anyone have any ideas to get around this? Thank you.
EDIT: Full code:
import pandas as pd
import pandas_datareader as web
import datetime as dt
import yfinance as yf
import numpy as np
stocks = ['BA', 'AMD', 'AAPL']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d = web.DataReader(stocks, 'yahoo', start, end)
d['sma50'] = np.round(d['Close'].rolling(window=2).mean(), decimals=2)
d['sma200'] = np.round(d['Close'].rolling(window=14).mean(), decimals=2)
d['200-50'] = d['sma200'] - d['sma50']
_buy = -2
d['Crossover_Long'] = np.where(d['200-50'] < _buy, 1, 0)
d['Crossover_Long_Change']=d.Crossover_Long.diff()
d['buy'] = np.where(d['Crossover_Long_Change'] == 1, 'buy', 'n/a')
d['sell'] = np.where(d['Crossover_Long_Change'] == -1, 'sell', 'n/a')
pd.set_option('display.max_rows', 5093)
d.drop(['High', 'Low', 'Close', 'Volume', 'Open'], axis=1, inplace=True)
d.dropna(inplace=True)
#make 2 dataframe
d.set_index(d['Adj Close'], inplace=True)
buy_price = d.index[d['Crossover_Long_Change']==1]
sell_price = d.index[d['Crossover_Long_Change']==-1]
d['Crossover_Long_Change'].value_counts()
profit_loss = (sell_price - buy_price)*10
commision = buy_price*.01
position_value = (buy_price + commision)*10
percent_return = (profit_loss/position_value)*100
percent_rounded = np.round(percent_return, decimals=2)
prices = {
"Buy Price" : buy_price,
"Sell Price" : sell_price,
"P/L" : profit_loss,
"Return": percent_rounded
}
df = pd.DataFrame(prices)
print('The return was {}%, and profit or loss was ${} '.format(np.round(df['Return'].sum(), decimals=2),
np.round(df['P/L'].sum(), decimals=2)))
d
I tried 3 stocks in your code and it returns data for all 3, not sure I understood the problem you're facing?
import pandas as pd
import pandas_datareader as web
import datetime as dt
stocks = ['BA', 'AMD', 'AAPL']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d = web.DataReader(stocks, 'yahoo', start, end)
print(d)
Output:
Attributes Adj Close Close ... Open Volume
Symbols BA AMD AAPL BA AMD AAPL ... BA AMD AAPL BA AMD AAPL
Date ...
2018-01-02 282.886383 10.980000 166.353714 296.839996 10.980000 172.259995 ... 295.750000 10.420000 170.160004 2978900.0 44146300.0 25555900.0
2018-01-03 283.801239 11.550000 166.324722 297.799988 11.550000 172.229996 ... 295.940002 11.610000 172.529999 3211200.0 154066700.0 29517900.0
2018-01-04 282.724396 12.120000 167.097290 296.670013 12.120000 173.029999 ... 297.940002 12.100000 172.539993 4171700.0 109503000.0 22434600.0
2018-01-05 294.322296 11.880000 168.999741 308.839996 11.880000 175.000000 ... 296.769989 12.190000 173.440002 6177700.0 63808900.0 23660000.0
2018-01-08 295.570740 12.280000 168.372040 310.149994 12.280000 174.350006 ... 308.660004 12.010000 174.350006 4124900.0 63346000.0 20567800.0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2019-12-24 331.030457 46.540001 282.831299 333.000000 46.540001 284.269989 ... 339.510010 46.099998 284.690002 4120100.0 44432200.0 12119700.0
2019-12-26 327.968689 46.630001 288.442780 329.920013 46.630001 289.910004 ... 332.700012 46.990002 284.820007 4593400.0 57562800.0 23280300.0
2019-12-27 328.187408 46.180000 288.333313 330.140015 46.180000 289.799988 ... 330.200012 46.849998 291.119995 4124000.0 36581300.0 36566500.0
2019-12-30 324.469513 45.520000 290.044617 326.399994 45.520000 291.519989 ... 330.500000 46.139999 289.459991 4525500.0 41149700.0 36028600.0
2019-12-31 323.833313 45.860001 292.163818 325.760010 45.860001 293.649994 ... 325.410004 45.070000 289.929993 4958800.0 31673200.0 25201400.0
I think the error comes from your moving average and the line
d['sma50'] = np.round(d['Close'].rolling(window=2).mean(), decimals=2)
because d represent 3 stocks, I think you have to separate each stock and compute the moving average separately
EDIT : I tried something for two stocks only (BA and AMD) but it is not the best solution because I'm always repeating myself for every line.
I'm just a beginner in Python but maybe this will help you to find a solution to your problem
PS : The last line doesn't work really well (which is the printing of the P&L and Return)
"
import pandas as pd
import pandas_datareader as web
import datetime as dt
stock1 = ['BA']
stock2=['AMD']
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2020, 1, 1)
d1 = web.DataReader(stock1, 'yahoo', start, end)
d2 = web.DataReader(stock2, 'yahoo', start, end)
d1['sma50'] = np.round(d1['Close'].rolling(window=2).mean(), decimals=2)
d2['sma50'] = np.round(d2['Close'].rolling(window=2).mean(), decimals=2)
d1['sma200'] = np.round(d1['Close'].rolling(window=14).mean(), decimals=2)
d2['sma200'] = np.round(d2['Close'].rolling(window=14).mean(), decimals=2)
d1['200-50'] = d1['sma200'] - d1['sma50']
d2['200-50'] = d2['sma200'] - d2['sma50']
_buy = -2
d1['Crossover_Long'] = np.where(d1['200-50'] < _buy, 1, 0)
d2['Crossover_Long'] = np.where(d2['200-50'] < _buy, 1, 0)
d1['Crossover_Long_Change']=d1.Crossover_Long.diff()
d2['Crossover_Long_Change']=d2.Crossover_Long.diff()
d1['buy'] = np.where(d1['Crossover_Long_Change'] == 1, 'buy', 'n/a')
d2['buy'] = np.where(d2['Crossover_Long_Change'] == 1, 'buy', 'n/a')
d1['sell_BA'] = np.where(d1['Crossover_Long_Change'] == -1, 'sell', 'n/a')
d2['sell_AMD'] = np.where(d2['Crossover_Long_Change'] == -1, 'sell', 'n/a')
pd.set_option('display.max_rows', 5093)
d1.drop(['High', 'Low', 'Close', 'Volume', 'Open'], axis=1, inplace=True)
d2.drop(['High', 'Low', 'Close', 'Volume', 'Open'], axis=1, inplace=True)
d2.dropna(inplace=True)
d1.dropna(inplace=True)
d1.set_index("Adj Close",inplace=True)
d2.set_index("Adj Close",inplace=True)
buy_price_BA = np.array(d1.index[d1['Crossover_Long_Change']==1])
buy_price_AMD = np.array(d2.index[d2['Crossover_Long_Change']==1])
sell_price_BA = np.array(d1.index[d1['Crossover_Long_Change']==-1])
sell_price_AMD = np.array(d2.index[d2['Crossover_Long_Change']==-1])
d1['Crossover_Long_Change'].value_counts()
d2['Crossover_Long_Change'].value_counts()
profit_loss_BA = (sell_price_BA - buy_price_BA)*10
profit_loss_AMD = (sell_price_AMD - buy_price_AMD)*10
commision_BA = buy_price_BA*.01
commision_AMD = buy_price_AMD*.01
position_value_BA = (buy_price_BA + commision_BA)*10
position_value_AMD = (buy_price_AMD + commision_AMD)*10
percent_return_BA = np.round(((profit_loss_BA/position_value_BA)*100),decimals=2)
percent_return_AMD = np.round(((profit_loss_AMD/position_value_AMD)*100),decimals=2)
prices_BA = {
"Buy Price BA" : [buy_price_BA],
"Sell Price BA" : [sell_price_BA],
"P/L BA" : [profit_loss_BA],
"Return BA": [percent_return_BA]}
df = pd.DataFrame(prices_BA)
print('The return was {}%, and profit or loss was ${} '.format(np.round(df['Return BA'].sum(), decimals=2),
np.round(df['P/L BA'].sum(), decimals=2)))
prices_AMD = {
"Buy Price AMD" : [buy_price_AMD],
"Sell Price AMD" : [sell_price_AMD],
"P/L AMD" : [profit_loss_AMD],
"Return AMD": [percent_return_AMD]}
df = pd.DataFrame(prices_AMD)
print('The return was {}%, and profit or loss was ${} '.format(np.round(df['Return AMD'].sum(), decimals=2),
np.round(df['P/L AMD'].sum(), decimals=2)))
It seems like there's a bug in the pandas data reader. I work around it by initialising with one symbol and then setting the symbols property on the instantiated object. After doing that, it works fine to call read() on tmp below.
import pandas_datareader as pdr
all_symbols = ['ibb', 'xly', 'fb', 'exx1.de']
tmp = pdr.yahoo.daily.YahooDailyReader(symbols=all_symbols[0])
# this is a work-around, pdr is broken...
tmp.symbols = all_symbols
data = tmp.read()
The Bokeh DateRangeSlider widget requires int values for its step attribute which must be the time value in milliseconds. It works well when the step is set to seconds, minutes, hours, days or years. However I need a month resolution on the slider.
When the step is set to 31 days it works well for the start date until March when instead of 1 March I get 4 March. Then the shift from 1th of the month in the displayed value gets bigger and bigger.
I want to be able to set and get displayed the slider range on both sides always to be the 1th day of the month e.g. 1 March, 1 April, 1 May, 1 June etc... like it is in the DataFrame.
Considering the following code, what would be the best way to realize it (possibly using a JS callback) ?
import pandas as pd
from bokeh.plotting import show
from bokeh.models import DateRangeSlider
data = {'date_start': ['201812', '201901', '201902', '201903', '201904', '201905', '201906', '201907', '201908', '201909', '201910', '201911'],
'date_end': [ '201901', '201902', '201903', '201904', '201905', '201906', '201907', '201908', '201909', '201910', '201911', '201912'],
'values' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}
df = pd.DataFrame(data)
df['Start'] = pd.to_datetime(df['date_start'], format='%Y%m')
df['End'] = pd.to_datetime(df['date_end'], format='%Y%m')
start_date = df['Start'].min()
end_date = df['End'].max()
range_slider = DateRangeSlider(start=start_date, end=end_date, value=(start_date, end_date), step=31*24*60*60*1000, title="Date Range", callback_policy = 'mouseup', tooltips = False, width=600)
show(range_slider)import pandas as pd
from bokeh.plotting import show
from bokeh.models import DateRangeSlider
data = {'date_start': ['201812', '201901', '201902', '201903', '201904', '201905', '201906', '201907', '201908', '201909', '201910', '201911'],
'date_end': [ '201901', '201902', '201903', '201904', '201905', '201906', '201907', '201908', '201909', '201910', '201911', '201912'],
'values' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}
df = pd.DataFrame(data)
df['Start'] = pd.to_datetime(df['date_start'], format='%Y%m')
df['End'] = pd.to_datetime(df['date_end'], format='%Y%m')
start_date = df['Start'].min()
end_date = df['End'].max()
range_slider = DateRangeSlider(start=start_date, end=end_date, value=(start_date, end_date), step=31*24*60*60*1000, title="Date Range", callback_policy = 'mouseup', tooltips = False, width=600)
show(range_slider)
After some struggling I came up with this JS callback which temporary changes the step to 1 day in order to be able to correct the date. It also changes temporary the range so that when the step is restored the slider handle remains on its position. Far from perfect but working:
import pandas as pd
from bokeh.plotting import show
from bokeh.models import CustomJS, DateRangeSlider
data = {'date_start': ['201812', '201901', '201902', '201903', '201904', '201905', '201906', '201907', '201908', '201909', '201910', '201911'],
'date_end': [ '201901', '201902', '201903', '201904', '201905', '201906', '201907', '201908', '201909', '201910', '201911', '201912'],
'values' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}
df = pd.DataFrame(data)
df['Start'] = pd.to_datetime(df['date_start'], format='%Y%m')
df['End'] = pd.to_datetime(df['date_end'], format='%Y%m')
start_date = df['Start'].min()
end_date = df['End'].max()
range_slider = DateRangeSlider(start=start_date, end=end_date, value=(start_date, end_date), step=31*24*60*60*1000, title="Date Range", callback_policy = 'mouseup', tooltips = False, width=600)
code = '''
console.log('start, end', cb_obj.start, cb_obj.end)
for (i in cb_obj.value) {
if (getDay(cb_obj.value[i]) != 1) {
correctDate(day, i)
}
}
function getDay(value) {
date = new Date(value)
str_date = date.toString()
day = str_date.split(' ')[2]
return Number(day)
}
function correctDate(day, side) {
if (day < 15) {
console.log('day < 15')
difference = day - 1
difference_milliseconds = -1 * difference*24*60*60*1000
}
else {
console.log('day >= 15')
difference = 0
new_day = -1
while(new_day != 1) {
difference_milliseconds = difference*24*60*60*1000
new_date = new Date(cb_obj.value[0] + difference_milliseconds)
new_day = Number(new_date.getDate())
difference += 1
}
}
cb_obj.step = 1*24*60*60*1000 // set slider step to 1 day to be able to correct
if (side == 0) {
cb_obj.start = cb_obj.start + difference_milliseconds
cb_obj.value = [cb_obj.value[0] + difference_milliseconds, cb_obj.value[1]]
}
else if (side == 1) {
cb_obj.end = cb_obj.end + difference_milliseconds + 4*24*60*60*1000
cb_obj.value = [cb_obj.value[0], cb_obj.value[1] + difference_milliseconds]
}
setTimeout(resetStep, 50, cb_obj) // reset step to 31 days
}
function resetStep(cb_obj) {
cb_obj.step = 31*24*60*60*1000
}
'''
range_slider.js_on_change('value_throttled', CustomJS(args = {'end_date': end_date}, code=code))
show(range_slider)
Or maybe the best option is not to use the DateRangeSlider at all fo the month step. The solution below uses a RangeSlider in combination with a Div to realize the same functionality which looks much nicer:
import pandas as pd
from bokeh.plotting import show
from bokeh.models import RangeSlider, Div, Column, CustomJS
data = {'date_start': ['2018-12', '2019-01', '2019-02', '2019-03', '2019-04', '2019-05', '2019-06', '2019-07', '2019-08', '2019-09', '2019-10', '2019-11'],
'date_end': [ '2019-01', '2019-02', '2019-03', '2019-04', '2019-05', '2019-06', '2019-07', '2019-08', '2019-09', '2019-10', '2019-11', '2019-12'],
'values' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}
df = pd.DataFrame(data)
df['Start'] = pd.to_datetime(df['date_start'], format='%Y-%m')
df['End'] = pd.to_datetime(df['date_end'], format='%Y-%m')
number_dates = len(list(df.date_start.unique()))
start_dates = df.date_start.to_list()
end_dates = df.date_end.to_list()
range_slider = RangeSlider(start=0, end=number_dates, value=(0, number_dates), step=1, title="", callback_policy = 'mouseup', tooltips = False, width=600, show_value = False)
div = Div(text = "Date Range: <b>" + str(start_dates[range_slider.value[0]]) + ' . . . ' + str(end_dates[range_slider.value[1]-1]) + '</b>', render_as_text = False, width = 575)
code = '''
range = Math.round(Number(cb_obj.value[1] - cb_obj.value[0]), 10)
range = range < 10 ? '0' + range : range
div.text = "Date Range: <b>" + start_dates[Math.round(cb_obj.value[0], 10)] + ' . . . ' + end_dates[Math.round(cb_obj.value[1], 10) + -1] + '</b>'
'''
range_slider.js_on_change('value_throttled', CustomJS(args = {'div': div, 'start_dates': start_dates, 'end_dates': end_dates}, code=code))
show(Column(div, range_slider))
I am trying to replicate the following code which work smoothly and add a parameter for date to the function and run the function with different date in a loop:
FUNCTION　V1:
def getOHLCV(currencies):
c_price = []
data = {}
try:
url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/ohlcv/historical'
parameters = {
'symbol': ",".join(currencies),
#'time_start': ",".join(start_dates),
'count':'91',
'interval':'daily',
'convert':'JPY',
}
headers = {
'Accepts': 'application/json',
'X-CMC_PRO_API_KEY': 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
}
session = Session()
session.headers.update(headers)
response = session.get(url, params=parameters)
data = json.loads(response.text)
for currency in data['data']:
used_list = [
item['quote']['JPY']
for item in data['data'][currency]['quotes']
]
price = pd.DataFrame.from_records(used_list)
price['timestamp'] = pd.to_datetime(price['timestamp'])
price['timestamp'] = price['timestamp'].astype(str).str[:-15]
price_c = price.set_index('timestamp').close
c_price.append(price_c.rename(currency))
except Exception as e:
print (data)
return c_price
c_price = []
c_price.extend(getOHLCV(available[:61]))
c_price.extend(getOHLCV(available[61:]))
c_price = pd.concat(c_price, axis=1, sort=True)
pd.set_option('display.max_columns', 200)
c_price = c_price.transpose()
c_price.index.name = 'currency'
c_price.sort_index(axis=0, ascending=True, inplace=True)
OUTPUT:
2019-07-25 2019-07-26 2019-07-27 2019-07-28 2019-07-29 \
currency
1WO 2.604104 2.502526 2.392313 2.418967 2.517868
ABX 1.015568 0.957774 0.913224 0.922612 1.037273
ADH 0.244782 0.282976 0.309931 0.287933 0.309613
... ... ... ... ... ...
XTX 0.156103 0.156009 0.156009 0.165103 0.156498
ZCO 0.685255 0.661324 0.703521 0.654763 0.616204
ZPR 0.214395 0.204968 0.181529 0.178460 0.177596
FUNCTION　V2:
The V2 function add a parameter start_dates and loop the function with this new parameter. The issue is I got an empty dataframe from it. I assume that there is an issue with the date but I don't know where. Any help is appreciated.
def getOHLCV(currencies, start_dates):
...
'symbol': ",".join(currencies),
'time_start': ",".join(start_dates),
...
date_list = [(date.today() - timedelta(days= x * 91)) for x in range(3)][1:]
one = []
for i in date_list:
c_price = []
c_price.extend(getOHLCV(available[:61], i))
c_price.extend(getOHLCV(available[61:], i))
c_price = pd.concat(c_price, axis=1, sort=True)
one = pd.concat(c_price, axis=1, sort=True)
pd.set_option('display.max_columns', 200)
The array you are extending you are clearing at each iteration of the foor loop, it can be fixed like so
date_list = [(date.today() - timedelta(days= x * 91)) for x in range(3)][1:]
one = []
c_price = []
for i in date_list:
c_price.extend(getOHLCV(available[:61], i))
c_price.extend(getOHLCV(available[61:], i))
c_price = pd.concat(c_price, axis=1, sort=True)
one = pd.concat(c_price, axis=1, sort=True)
pd.set_option('display.max_columns', 200)
Hope that works for you
EDIT 1
So we need to fix the error : "time_start" must be a valid ISO 8601 timestamp or unix time value'
This is because the return from this
date_list = [(date.today() - timedelta(days= x * 91)) for x in range(3)][1:]
Is this
[datetime.date(2019, 7, 24), datetime.date(2019, 4, 24)]
So we need to convert the list from datetime objects to something that the API will understand, we can do it the following way
date_list = list(map(date.isoformat, date_list))
And we get the following output
['2019-07-24', '2019-04-24']
Edit 2
The error happens when we try to call join on something that isnt a list, so we can fix it by doing
'time_start': start_dates
Instead of doing
'time_start': ",".join(start_dates),
I am trying to loop over 2 lists to get all combinations possible in the loop below. I have some difficulties to understand why the first part works and the second does not. Basically it query the same data but with all pattern from the lists. Any help would be very appreciated.
THE CODE:
base = ['BTC', 'ETH']
quoted = ['USDT', 'AUD','USD']
def daily_volume_historical(symbol, comparison_symbol, all_data=False, limit=90, aggregate=1, exchange=''):
url = 'https://min-api.cryptocompare.com/data/histoday?fsym={}&tsym={}&limit={}&aggregate={}'\
.format(symbol.upper(), comparison_symbol.upper(), limit, aggregate)
if exchange:
url += '&e={}'.format(exchange)
if all_data:
url += '&allData=true'
page = requests.get(url)
data = page.json()['Data']
df = pd.DataFrame(data)
df.drop(df.index[-1], inplace=True)
df['timestamp'] = [datetime.datetime.fromtimestamp(d) for d in df.time]
df.set_index('timestamp')
return df
## THIS CODE GIVES SOME DATA ##
volu = daily_volume_historical('BTC', 'USD', 'CCCAGG').set_index('timestamp').volumefrom
## THIS CODE GIVES EMPTY DATA FRAME ##
d_volu = []
for a,b in [(a,b) for a in base for b in quoted]:
volu = daily_volume_historical(a, b, exchange= 'CCCAGG').volumefrom
d_volu.append
d_volu = pd.concat(d_volu, axis=1)
volu output sample:
timestamp
2010-07-17 09:00:00 20.00
2010-07-18 09:00:00 75.01
2010-07-19 09:00:00 574.00
2010-07-20 09:00:00 262.00
2010-07-21 09:00:00 575.00
2010-07-22 09:00:00 2160.00
2010-07-23 09:00:00 2402.50
2010-07-24 09:00:00 496.32
import itertools
base = ['BTC', 'ETH']
quoted = ['USDT', 'AUD','USD']
combinations = list(itertools.product(base, quoted))
def daily_volume_historical(symbol, comparison_symbol, all_data=False, limit=90, aggregate=1, exchange=''):
url = 'https://min-api.cryptocompare.com/data/histoday?fsym={}&tsym={}&limit={}&aggregate={}'\
.format(symbol.upper(), comparison_symbol.upper(), limit, aggregate)
if exchange:
url += '&e={}'.format(exchange)
if all_data:
url += '&allData=true'
page = requests.get(url)
data = page.json()['Data']
df = pd.DataFrame(data)
df.drop(df.index[-1], inplace=True)
df['timestamp'] = [datetime.datetime.fromtimestamp(d) for d in df.time]
df.set_index('timestamp')
return df
## THIS CODE GIVES SOME DATA ##
volu = daily_volume_historical('BTC', 'USD', 'CCCAGG').set_index('timestamp').volumefrom
## THIS CODE GIVES EMPTY DATA FRAME ##
d_volu = []
for a,b in combinations:
volu = daily_volume_historical(a, b, exchange= 'CCCAGG').volumefrom
d_volu.append
d_volu = pd.concat(d_volu, axis=1)
I retrieve data from quandl and load it to a pandas DF object.
Afterwards I calculate SMA values (SMA21, SMA55) based on "Last Price".
Adding those SMA values as a column do my DF object.
I iterate through DF to catch a buy signal.
I know the buy condition is holding true for some dates but my code does not printing anything out. I am expecting to print the buy condition at the very least.
as below you can see the following condition:
kitem['SMA21'] >= kitem['Last']
My code:
import requests
import pandas as pd
import json
class URL_Params:
def __init__ (self, endPoint, symboll, startDate, endDate, apiKey):
self.endPoint = endPoint
self.symboll = symboll
self.startDate = startDate
self.endDate = endDate
self.apiKey = apiKey
def createURL (self):
return self.endPoint + self.symboll + '?start_date=' + self.startDate + '&end_date=' + self.endDate + '&api_key=' + self.apiKey
def add_url(self, _url):
self.url_list
my_portfolio = {'BTC':1.0, 'XRP':0, 'DSH':0, 'XMR':0, 'TotalBTCValue':1.0}
_endPoint = 'https://www.quandl.com/api/v3/datasets/BITFINEX/'
_symbolls = ['BTCEUR','XRPBTC','DSHBTC','IOTBTC','XMRBTC']
_startDate = '2017-01-01'
_endDate = '2019-03-01'
_apiKey = '' #needs to be set for quandl
my_data = {}
my_conns = {}
my_col_names = ['Date', 'High', 'Low', 'Mid', 'Last', 'Bid', 'Ask', 'Volume']
orderbook = []
#create connection and load data for each pair/market.
#load them in a dict for later use
for idx_symbol in _symbolls:
my_url_params = URL_Params(_endPoint,idx_symbol,_startDate,_endDate,_apiKey)
response = requests.get(my_url_params.createURL())
my_data[idx_symbol] = json.loads(response.text)
#Prepare Data
my_raw_data_df_xrpbtc = pd.DataFrame(my_data['XRPBTC']['dataset']['data'], columns= my_data['XRPBTC']['dataset']['column_names'])
#Set Index to Date Column and Sort
my_raw_data_df_xrpbtc['Date'] = pd.to_datetime(my_raw_data_df_xrpbtc['Date'])
my_raw_data_df_xrpbtc.index = my_raw_data_df_xrpbtc['Date']
my_raw_data_df_xrpbtc = my_raw_data_df_xrpbtc.sort_index()
#Drop unrelated columns
my_raw_data_df_xrpbtc.drop(['Date'], axis=1, inplace=True)
my_raw_data_df_xrpbtc.drop(['Ask'], axis=1, inplace=True)
my_raw_data_df_xrpbtc.drop(['Bid'], axis=1, inplace=True)
my_raw_data_df_xrpbtc.drop(['Low'], axis=1, inplace=True)
my_raw_data_df_xrpbtc.drop(['High'], axis=1, inplace=True)
my_raw_data_df_xrpbtc.drop(['Mid'], axis=1, inplace=True)
#Calculate SMA values to create buy-sell signal
my_raw_data_df_xrpbtc['SMA21'] = my_raw_data_df_xrpbtc['Last'].rolling(21).mean()
my_raw_data_df_xrpbtc['SMA55'] = my_raw_data_df_xrpbtc['Last'].rolling(55).mean()
my_raw_data_df_xrpbtc['SMA200'] = my_raw_data_df_xrpbtc['Last'].rolling(200).mean()
#Check for each day if buy signal holds BUY if sell signal holds SELL
for idx,kitem in my_raw_data_df_xrpbtc.iterrows():
if (kitem['SMA21'] >= kitem['Last']) is True: #buy signal
print("buy0")
if my_portfolio['BTC'] > 0 is True:
print("buy1")
if (kitem['Last'] * my_portfolio['XRP']) >= (my_portfolio['BTC'] * 1.05) is True: #sell signal
print("sell0")
if my_portfolio['XRP'] > 0 is True:
print("sell1")
I know that there are lots of rows that holds true but my code never enters this path of code so it does not print out what I expect.
Could anyone please help/comment what might be wrong?
The reason is that your comparison is wrong. The result of kitem['SMA21'] >= kitem['Last'] will be a numpy.bool_. When you use is to compare it to True this will fail as it is not the same object.
If you change the comparison to == it will work as expected:
if (kitem['SMA21'] >= kitem['Last']) == True: