def _main():
"""
Predicts which loans will perform the best and then places orders on those if
there is enough cash and if the loan has not already been ordered.
"""
count = 0
df = generate_completed_df()
# import pdb; pdb.set_trace()
if three_years_only:
df = df[df.term == 36]
good_ids = set(df.ix[(df.estimated_roi > roi_floor)
& (df.default_prob < default_floor)].index)
already_ordered = set()
for row in c.execute('SELECT * FROM orders'):
already_ordered.add(row[1])
good_ids = good_ids - already_ordered
logging.debug("Placing orders for {} loans \n{} already ordered".format(
len(set((good_ids)) - set(already_ordered)), len(already_ordered))
) # TODO Consider making a new_orders thing with the set.
order_ids = []
pending_amount = 0
for id in good_ids:
funds_available = has_enough_cash(
pending_amount) # Called once to avoid dirty logs
if id not in already_ordered and funds_available:
count += 1
pending_amount += amount
order_ids.append(id)
elif not funds_available:
logging.debug("No Cash left")
break
elif id in already_ordered:
logging.info("{} Was previously ordered".format(id))
place_order(order_ids)
logging.debug("Finished Ordering ordered {} notes \n".format(count))
def _main():
"""
Predicts which loans will perform the best and then places orders on those if
there is enough cash and if the loan has not already been ordered.
"""
count = 0
df = generate_completed_df()
# import pdb; pdb.set_trace()
if three_years_only:
df = df[df.term == 36]
good_ids = set(df.ix[(df.estimated_roi > roi_floor) &
(df.default_prob < default_floor)].index)
already_ordered = set()
for row in c.execute('SELECT * FROM orders'):
already_ordered.add(row[1])
good_ids = good_ids - already_ordered
logging.debug("Placing orders for {} loans \n{} already ordered"
.format(len(set((good_ids)) - set(already_ordered)), len(already_ordered))) # TODO Consider making a new_orders thing with the set.
order_ids = []
pending_amount = 0
for id in good_ids:
funds_available = has_enough_cash(pending_amount) # Called once to avoid dirty logs
if id not in already_ordered and funds_available:
count += 1
pending_amount += amount
order_ids.append(id)
elif not funds_available:
logging.debug("No Cash left")
break
elif id in already_ordered:
logging.info("{} Was previously ordered".format(id))
place_order(order_ids)
logging.debug("Finished Ordering ordered {} notes \n".format(count))
def _main():
"""
Predicts which loans will perform the best and then places orders on those if
there is enough cash and if the loan has not already been ordered.
"""
df = generate_completed_df()
good_ids = df.id[(df.estimated_roi > roi_floor) & (df.default_prob < default_floor)]
already_ordered = []
for row in c.execute('SELECT * FROM orders'):
already_ordered.append(row[1])
logging.debug("Placing orders for {} loans \n{} already ordered"
.format(len(set((good_ids)) - set(already_ordered)), len(already_ordered)))
for id in good_ids:
funds_available = has_enough_cash() # Called once to avoid dirty logs
if id not in already_ordered and funds_available:
place_order(id)
elif not funds_available:
logging.debug("No Cash left")
break
logging.debug("Finished Ordering")
<h1>{{name}}</h1>
{{data | safe}}
{% endblock %}
"""
import os
import pandas as pd
from flask import Flask, render_template, request
# sys.path.append('..')
from process_api import generate_completed_df
app = Flask(__name__)
df = generate_completed_df()
df_html = df.to_html().replace('class="dataframe"',
'class="table table-striped table-bordered table-condensed')
loans_available = 0
estimated_roi = 0
last_checked = 0
last_submitted = 0
# @app.route('/')
# def dashboard():
# return render_template('dashboard.html', roi_floor=roi_floor,
# loans_available=loans_available,
# estimated_roi=estimated_roi,
def test_dtypes_and_shape():
df = generate_completed_df()
return df
"""
import py.test
import pandas as pd
import engarde.decorators as ed
import numpy as np
import sys
from hypothesis import given, assume, example, strategies as st
sys.path.append('../app')
from process_api import generate_completed_df
TEST_API = True
BAD_DATA = True
if TEST_API:
test_df = generate_completed_df()
elif BAD_DATA:
test_df = pd.read_csv('bad_data.csv')
else:
test_df = pd.read_csv('good_data.csv')
dtypes = dict(estimated_roi=float,
default_prob=float,
loan_amnt=float,
int_rate=float,
grade='category',
installment=float,
emp_length=float,
home_ownership='category',
purpose='category',
revol_bal=float,
def test_dtypes_and_shape():
df = generate_completed_df()
return df
