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Merged
merged 2 commits into from
Aug 5, 2021
Merged

Andrew #21

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06f7079
updating app file - mongodb connection
a-memme Aug 5, 2021
21ddda5
updating - database
a-memme Aug 5, 2021
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159 changes: 16 additions & 143 deletions app_stream.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,8 @@
import requests
import json
import datetime as dt

#st.set_page_config(layout="wide")
import pymongo
from pymongo import MongoClient

# Set ignore warning
st.set_option('deprecation.showPyplotGlobalUse', False)
Expand All @@ -25,7 +25,7 @@


#Forecast and allow to collect cache for optimized performance
@st.cache(allow_output_mutation=True)
#@st.cache(allow_output_mutation=True)

def forecast(selection):

Expand Down Expand Up @@ -64,25 +64,21 @@ def forecast(selection):

# Get database for cryptocurrency
def get_df(symbol, interval = '1m'):

# Initialize PyMongo to work with MongoDBs
conn = 'mongodb://localhost:27017'
client = pymongo.MongoClient(conn)

# Access API and reformat
root_url = 'https://api.binance.com/api/v1/klines'
url = root_url + '?symbol=' + symbol + '&interval=' + interval
data = json.loads(requests.get(url).text)
df = pd.DataFrame(data)
df.columns = ['open_time',
'o', 'h', 'l', 'c', 'v',
'close_time', 'qav', 'num_trades',
'taker_base_vol', 'taker_quote_vol', 'ignore']
df.index = [dt.datetime.fromtimestamp(x/1000.0) for x in df.close_time]
df = df.reset_index()
df = df.rename(columns={"index":"ds","open_time": "time", "o": "open", "h":"high", "l":"low", "c":"y", "v":"volume"})
#df_isolated = pd.DataFrame(df, columns = ['ds', 'y'])

#df_isolated.to_csv(r'coin_price_action.csv', mode = 'w', header = True, index=False)
#df1 = pd.read_csv("coin_price_action.csv")
#select database
db = client['crypto_db']
#select the collection within the database
df_data = db.items

return df
#convert entire collection to Pandas dataframe
df = pd.DataFrame(list(df_data.find()))
df = df.drop(columns = ['open_time','o', 'h', 'l', 'c', 'v', 'index', 'ignore', '_id', 'time'])

return df


def dfdata(selection):
Expand Down Expand Up @@ -196,129 +192,6 @@ def dfdata(selection):
Twitter_NLP_Final.wrrrdcloud()


# #Test

# base_chart = {
# "values": [40, 10, 10, 10, 10, 10, 10],
# "labels": ["-", "-1", "-0.75", "-0.25", "0.25", "0.75", "1"],
# "domain": {"x": [0, .48]},
# "marker": {
# "colors": [
# 'rgb(255, 255, 255)',
# 'rgb(255, 255, 255)',
# 'rgb(255, 255, 255)',
# 'rgb(255, 255, 255)',
# 'rgb(255, 255, 255)',
# 'rgb(255, 255, 255)',
# 'rgb(255, 255, 255)'
# ],
# "line": {
# "width": 1
# }
# },
# "name": "Gauge",
# "hole": .4,
# "type": "pie",
# "direction": "clockwise",
# "rotation": 108,
# "showlegend": False,
# "hoverinfo": "none",
# "textinfo": "label",
# "textposition": "outside"
# }

# meter_chart = {
# "values": [50, 10, 10, 10, 10, 10],
# "labels": ["Twitter Sentiment", "Negative", "", "Neutral", "", "Positive"],
# "marker": {
# 'colors': [
# 'rgb(255, 255, 255)',
# 'rgb(255, 84, 84)',
# 'rgb(255,141,84)',
# 'rgb(255,250,84)',
# 'rgb(216,255,84)',
# 'rgb(130,255,84)'
# ]
# },
# "domain": {"x": [0, 0.48]},
# "name": "Gauge",
# "hole": .3,
# "type": "pie",
# "direction": "clockwise",
# "rotation": 90,
# "showlegend": False,
# "textinfo": "label",
# "textposition": "inside",
# "hoverinfo": "none"
# }

# layout = {
# 'xaxis': {
# 'showticklabels': False,
# 'showgrid': False,
# 'zeroline': False,
# },
# 'yaxis': {
# 'showticklabels': False,
# 'showgrid': False,
# 'zeroline': False,
# },
# 'shapes': [
# {
# 'type': 'path',
# 'path': 'M 0.235 0.5 L 0.24 0.65 L 0.245 0.5 Z',
# 'fillcolor': 'rgba(44, 160, 101, 0.5)',
# 'line': {
# 'width': 0.75
# },
# 'xref': 'paper',
# 'yref': 'paper'
# }
# ],
# 'annotations': [
# {
# 'xref': 'paper',
# 'yref': 'paper',
# 'x': 0.23,
# 'y': 0.45,
# 'text': '100',
# 'showarrow': False
# }
# ]
# }

# # we don't want the boundary now
# base_chart['marker']['line']['width'] = 0

# gauge2 = {"data": [base_chart, meter_chart],
# "layout": layout}

# st.plotly_chart(gauge2)

# #py.iplot(fig, filename='gauge-meter-chart')


# # Average Sentiment Score
# gauge_chart = go.Figure(go.Indicator(
# mode='gauge+number+delta',
# value = Twitter_NLP_Final.scrape_sentiment_score(),
# title = {'text': 'Twitter Sentiment (-1.0 Negative to 1.0 Positive)'},
# gauge = {
# 'axis': {'range': [-1, 1]},
# 'bar': {'color':'lightgrey'},
# 'steps': [
# {'range': [-1, -0.75], 'color': 'rgb(255, 84, 84)'},
# {'range': [-0.75, -0.25], 'color': 'rgb(255,141,84)'},
# {'range': [-0.25, 0.25], 'color': 'rgb(255,250,84)'},
# {'range': [0.25, 0.75], 'color': 'rgb(216,255,84)'},
# {'range': [0.75, 1], 'color': 'rgb(130,255,84)'}
# ]
# }
# ))

# st.plotly_chart(gauge_chart)





114 changes: 66 additions & 48 deletions prophet_remake.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,9 @@

import streamlit as st

import pymongo
from pymongo import MongoClient

def get_bars(symbol, interval = '1m'):

# Access API and reformat
Expand All @@ -43,63 +46,78 @@ def get_bars(symbol, interval = '1m'):
m = Prophet(changepoint_prior_scale=0.01, daily_seasonality=True).fit(df_isolated)
future = m.make_future_dataframe(periods=30, freq='1min')
fcst = m.predict(future)

# Initialize PyMongo to work with MongoDBs
conn = 'mongodb://localhost:27017'
client = pymongo.MongoClient(conn)

# Create traces for interactive plotly graph
trace = go.Scatter(
name = 'Actual price',
mode = 'markers',
x = list(fcst['ds']),
y = list(df_isolated['y']),
marker=dict(
color='#FFBAD2',
line=dict(width=1)
))
# Define database and collection
# Name & create mongo database (i.e nhl_db in this case)
# Create variable for collection within your mongo database
db = client.crypto_db
collection = db.items

trace1 = go.Scatter(
name = 'predict',
mode = 'lines',
x = list(fcst['ds']),
y = list(fcst['yhat']),
marker=dict(
color='red',
line=dict(width=3)
))
#Reformat
mongo_df = df.to_dict('records')

upper_band = go.Scatter(
name = 'upper band',
mode = 'lines',
x = list(fcst['ds']),
y = list(fcst['yhat_upper']),
line= dict(color='#57b88f'),
fill = 'tonexty'
)
#Load dictionary into mongo db
collection.insert_many(mongo_df)

# Create graph
figure = plot_plotly(m, fcst)
return figure

lower_band = go.Scatter(
name= 'lower band',
mode = 'lines',
x = list(fcst['ds']),
y = list(fcst['yhat_lower']),
line= dict(color='#1705ff'))
# # Create traces for interactive plotly graph
# trace = go.Scatter(
# name = 'Actual price',
# mode = 'markers',
# x = list(fcst['ds']),
# y = list(df_isolated['y']),
# marker=dict(
# color='#FFBAD2',
# line=dict(width=1)
# ))

tracex = go.Scatter(
name = 'Actual price',
mode = 'markers',
x = list(df1['ds']),
y = list(df1['y']),
marker=dict(
color='black',
line=dict(width=2)
))
# trace1 = go.Scatter(
# name = 'predict',
# mode = 'lines',
# x = list(fcst['ds']),
# y = list(fcst['yhat']),
# marker=dict(
# color='red',
# line=dict(width=3)
# ))

data = [tracex, trace1, lower_band, upper_band, trace]
# upper_band = go.Scatter(
# name = 'upper band',
# mode = 'lines',
# x = list(fcst['ds']),
# y = list(fcst['yhat_upper']),
# line= dict(color='#57b88f'),
# fill = 'tonexty'
# )

layout = dict(title='Price Prediction Using Prophet',
xaxis=dict(title = 'Dates', ticklen=2, zeroline=True))

# lower_band = go.Scatter(
# name= 'lower band',
# mode = 'lines',
# x = list(fcst['ds']),
# y = list(fcst['yhat_lower']),
# line= dict(color='#1705ff'))

figure = plot_plotly(m, fcst)
# tracex = go.Scatter(
# name = 'Actual price',
# mode = 'markers',
# x = list(df1['ds']),
# y = list(df1['y']),
# marker=dict(
# color='black',
# line=dict(width=2)
# ))

return figure
# data = [tracex, trace1, lower_band, upper_band, trace]

# layout = dict(title='Price Prediction Using Prophet',
# xaxis=dict(title = 'Dates', ticklen=2, zeroline=True))

#figure=dict(data=data,layout=layout)
#plt.savefig('btc03.png')
Expand Down