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mport pandas as pd
import numpy as np
def detecter_tendance(data):
# Code pour détecter la tendance
pass
def identifier_niveaux(data):
# Code pour identifier les niveaux de support et de résistance
pass
def calculer_stop_loss(tendance, support, resistance):
# Code pour calculer les stop loss
pass
def calculer_profils(tendance, support, resistance):
# Code pour calculer les profils mport pandas as pd
import numpy as np
def detecter_tendance(data):
# Code pour détecter la tendance
pass
def identifier_niveaux(data):
# Code pour identifier les niveaux de support et de résistance
pass
def calculer_stop_loss(tendance, support, resistance):
# Code pour calculer les stop loss
pass
def calculer_profils(tendance, support, resistance):
# Code pour calculer les profils import pandas as pd
import numpy as np
def detecter_tendance(data):
# Code pour détecter la tendance
pass
def identifier_niveaux(data):
# Code pour identifier les niveaux de support et de résistance
pass
def calculer_stop_loss(tendance, support, resistance):
# Code pour calculer les stop loss
pass
def calculer_profils(tendance, support, resistance):
# Code pour calculer les profils from flask import Flask, render_template, request
from .models import detecter_tendance, identifier_niveaux, calculer_stop_loss, calculer_profils
app = Flask(__name__)
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
data = request.files['data']
data = pd.read_csv(data)
tendance = detecter_tendance(data)
support, resistance = identifier_niveaux(data)
stop_loss = calculer_stop_loss(tendance, support, resistance)
profils = calculer_profils(tendance, support, resistance)
return render_template('index.html', tendance=tendance, support=support, resistance=resistance, stop_loss=stop_loss, profils=profils)
return render_template('index.html
pass
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