"Final changes"

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/FinTech_app.iml

@@ -0,0 +1,36 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="FacetManager">
+    <facet type="django" name="Django">
+      <configuration>
+        <option name="rootFolder" value="$MODULE_DIR$" />
+        <option name="settingsModule" value="FinTech_app/settings.py" />
+        <option name="manageScript" value="$MODULE_DIR$/manage.py" />
+        <option name="environment" value="&lt;map/&gt;" />
+        <option name="doNotUseTestRunner" value="false" />
+        <option name="trackFilePattern" value="migrations" />
+      </configuration>
+    </facet>
+  </component>
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+      <excludeFolder url="file://$MODULE_DIR$/.idea/dataSources" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+    <orderEntry type="library" name="bootstrap" level="application" />
+    <orderEntry type="library" name="jquery-3.4.1.slim" level="application" />
+    <orderEntry type="library" name="popper.js" level="application" />
+    <orderEntry type="library" name="jquery-3.2.1.slim" level="application" />
+    <orderEntry type="library" name="chart.js" level="application" />
+  </component>
+  <component name="TemplatesService">
+    <option name="TEMPLATE_CONFIGURATION" value="Django" />
+    <option name="TEMPLATE_FOLDERS">
+      <list>
+        <option value="$MODULE_DIR$/../FinTech_app\templates" />
+      </list>
+    </option>
+  </component>
+</module>

.idea/dataSources.xml

@@ -0,0 +1,21 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name="db" uuid="683f61cd-d3b1-4d1e-bd0b-7b859cacda56">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:J:\Desktop\FinTech_app\db.sqlite3</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+    <data-source source="LOCAL" name="Django default" uuid="a50cdda5-8fe8-4a03-a372-70599e0e6924">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <imported>true</imported>
+      <remarks>$PROJECT_DIR$/FinTech_app/settings.py</remarks>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:J:\Desktop\FinTech_app\db.sqlite3</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+  </component>
+</project>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/jsLibraryMappings.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptLibraryMappings">
+    <file url="file://$PROJECT_DIR$" libraries="{bootstrap, chart.js, jquery-3.2.1.slim, jquery-3.4.1.slim, popper.js}" />
+  </component>
+</project>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (FinTech_app)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/FinTech_app.iml" filepath="$PROJECT_DIR$/.idea/FinTech_app.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

FinTech_app/settings.py

@@ -9,7 +9,7 @@ https://docs.djangoproject.com/en/4.1/topics/settings/
 For the full list of settings and their values, see
 https://docs.djangoproject.com/en/4.1/ref/settings/
 """
-
+import os
 from pathlib import Path
 
 # Build paths inside the project like this: BASE_DIR / 'subdir'.
@@ -38,7 +38,9 @@ INSTALLED_APPS = [
     'django.contrib.messages',
     'django.contrib.staticfiles',
     'bootstrap4',
-    'crispy_forms'
+    'crispy_forms',
+    'charts',
+    'matplotlib'
 
 ]
 
@@ -130,3 +132,7 @@ DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
 
 LOGIN_REDIRECT_URL = "/"
 LOGOUT_REDIRECT_URL = "/"
+
+STATICFILES_DIRS = [
+    os.path.join(BASE_DIR, 'static')
+]

FinTech_app/urls.py

@@ -16,8 +16,10 @@ Including another URLconf
 from django.contrib import admin
 from django.urls import path, include
 from django.views.generic.base import TemplateView
+
 from . import views
 
+
 urlpatterns = [
     path('admin/', admin.site.urls),
     path('', TemplateView.as_view(template_name='main.html'), name='home'),
@@ -25,4 +27,11 @@ urlpatterns = [
     path("login/",  views.login_request, name="login"),
     path("logout/", views.logout_request, name="logout"),
     path("main/", views.logout_request, name="main"),
+    path('chart/', include('charts.urls')),
+    path('view-pdf1/', views.pdf_view1, name='pdf_view1'),
+    path('view-pdf-rynki/', views.pdf_view_rynki, name='pdf_view_rynki'),
+    path('view-pdf-akcje/', views.pdf_view_akcje, name='pdf_view_akcje'),
+    path('plot/', views.plot, name='plot'),
+    path('wigplot/', views.plot_view, name='plot_view'),
+    path('oblplot/', views.ob_plot_view, name='ob_plot_view'),
 ]

FinTech_app/views.py

@@ -1,9 +1,21 @@
-from django.shortcuts import  render, redirect
+from django.http import HttpResponse
+from django.shortcuts import render, redirect
 from .forms import NewUserForm
 from django.contrib.auth import login, authenticate, logout
 from django.contrib import messages
 from django.contrib.auth.forms import AuthenticationForm
-
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.backends.backend_agg import FigureCanvasAgg
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from io import BytesIO
+import base64
+from datetime import datetime
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.model_selection import train_test_split
 
 def register_request(request):
     if request.method == "POST":
@@ -43,8 +55,108 @@ def logout_request(request):
     return redirect("/")
 
 
-def index(request):
+def pdf_view1(request):
-    if request.user.is_authenticated:
+    with open('FinTech_app/Czym jest portfel inwestycyjny.pdf', 'rb') as pdf:
-        print("Logged in")
+        response = HttpResponse(pdf.read(), content_type='application/pdf')
-    else:
+        response['Content-Disposition'] = 'inline;filename=mypdf.pdf'
-        print("Not logged in")
+        return response
+
+
+def pdf_view_rynki(request):
+    with open('FinTech_app/RYNKI.pdf', 'rb') as pdf:
+        response = HttpResponse(pdf.read(), content_type='application/pdf')
+        response['Content-Disposition'] = 'inline;filename=mypdf.pdf'
+        return response
+
+
+def pdf_view_akcje(request):
+    with open('FinTech_app/Akcje.pdf', 'rb') as pdf:
+        response = HttpResponse(pdf.read(), content_type='application/pdf')
+        response['Content-Disposition'] = 'inline;filename=mypdf.pdf'
+        return response
+
+
+def plot(request):
+    # Data for plotting
+    t = np.arange(0.0, 2.0, 0.01)
+    s = 1 + np.sin(2 * np.pi * t)
+
+    fig, ax = plt.subplots()
+    ax.plot(t, s)
+
+    ax.set(xlabel='czas', ylabel='kurs',
+           title='Przewidywany kurs dla ...')
+    ax.grid()
+
+    response = HttpResponse(content_type = 'image/png')
+    canvas = FigureCanvasAgg(fig)
+    canvas.print_png(response)
+    return response
+
+
+def my_view(request):
+    fig, ax = plt.subplots()
+    ax.plot([1, 2, 3, 4])
+    return render(request, 'my_template.html', {'fig': fig})
+
+
+def plot_view(request):
+    data = pd.read_csv("charts/static/akcjeWIG40.csv")
+    months = data['Data']
+    prices = data['Otwarcie']
+    months = np.array(months).reshape(-1, 1)
+    poly_feat = PolynomialFeatures(degree=2)
+    months_poly = poly_feat.fit_transform(months)
+    model = LinearRegression()
+    model.fit(months_poly, prices)
+    future_months = np.array([12, 13, 14, 15, 16, 17, 18]).reshape(-1, 1)
+    future_months_poly = poly_feat.fit_transform(future_months)
+    predictions = model.predict(future_months_poly)
+    plt.plot(months, prices, color='cyan')
+    plt.plot(future_months, predictions, color='pink')
+    plt.title('Wykres cen dla następnych 6 miesięcy dla akcji 40 najwięszych spółek WIG40')
+    plt.xlabel('Miesiące')
+    plt.ylabel('Cena')
+    plt.xticks(range(1, 19))
+    plt.grid()
+
+    #save the figure to a buffer
+    buf = BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    string = base64.b64encode(buf.read()).decode()
+    uri = 'data:image/png;base64,' + string
+    context = { 'uri': uri }
+    plt.clf()
+    return render(request, 'wigplot.html', context)
+
+
+def ob_plot_view(request):
+    data = pd.read_csv("charts/static/obligacjePL.csv")
+    months = data['Data']
+    prices = data['Otwarcie']
+    months = np.array(months).reshape(-1, 1)
+    poly_feat = PolynomialFeatures(degree=2)
+    months_poly = poly_feat.fit_transform(months)
+    model = LinearRegression()
+    model.fit(months_poly, prices)
+    future_months = np.array([12, 13, 14, 15, 16, 17, 18]).reshape(-1, 1)
+    future_months_poly = poly_feat.fit_transform(future_months)
+    predictions = model.predict(future_months_poly)
+    plt.plot(months, prices, color='cyan')
+    plt.plot(future_months, predictions, color='pink')
+    plt.title('Wykres cen dla następnych 6 miesięcy dla obligacji PL')
+    plt.xlabel('Miesiące')
+    plt.ylabel('Cena')
+    plt.xticks(range(1, 19))
+    plt.grid()
+
+    #save the figure to a buffer
+    buf = BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    string = base64.b64encode(buf.read()).decode()
+    uri = 'data:image/png;base64,' + string
+    context = { 'uri': uri }
+    plt.clf()
+    return render(request, 'obligacjePLplot.html', context)

charts/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

charts/algorithm.py

@@ -0,0 +1,48 @@
+import csv
+import numpy as np
+from sklearn.svm import SVR
+import matplotlib.pyplot as plt
+
+dates = []
+prices = []
+
+
+def get_data(filename):
+    with open(filename, 'r') as csvfile:
+        csvFileReader = csv.reader(csvfile)
+        next(csvFileReader)
+        for row in csvFileReader:
+            dates.append(int(row[0].split('-')[1]))
+            prices.append(float(row[1]))
+    return
+
+
+def predict_prices(dates, prices, x):
+    dates = np.reshape(dates, (len(dates), 1))
+
+    svr_lin = SVR(kernel='linear', C=1e3)
+    svr_poly = SVR(kernel='poly', C=1e3, degree = 2)
+    svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
+    svr_lin.fit(dates, prices)
+    svr_poly.fit(dates, prices)
+    svr_rbf.fit(dates, prices)
+
+    plt.scatter(dates, prices, color='black', label='Data')
+    plt.plot(dates, svr_lin.predict(dates), color='green', label='linear model')
+    plt.plot(dates, svr_poly.predict(dates), color='blue', label='Polynomial model')
+    plt.plot(dates, svr_rbf.predict(dates), color='red', label='RBF model')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.title('Support Vector Regression')
+    plt.legend()
+    plt.show()
+
+    return svr_lin.predict(x)[0], svr_poly.predict(x)[0], svr_rbf.predict(x)[0]
+
+
+get_data('static/akcjeWIG40.csv')
+print(dates)
+print(prices)
+predicted_price = predict_prices(dates, prices, 29)
+
+print(predicted_price)

charts/algorithm_2.py

@@ -0,0 +1,56 @@
+import numpy as np
+from sklearn.svm import SVR
+import matplotlib.pyplot as plt
+
+
+X = np.sort(5 * np.random.rand(40, 1), axis=0)
+y = np.sin(X).ravel()
+
+# add noise to targets
+y[::5] += 3 * (0.5 - np.random.rand(8))
+svr_rbf = SVR(kernel="rbf", C=100, gamma=0.1, epsilon=0.1)
+svr_lin = SVR(kernel="linear", C=100, gamma="auto")
+svr_poly = SVR(kernel="poly", C=100, gamma="auto", degree=3, epsilon=0.1, coef0=1)
+lw = 2
+
+svrs = [svr_rbf, svr_lin, svr_poly]
+kernel_label = ["RBF", "Linear", "Polynomial"]
+model_color = ["m", "c", "g"]
+
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)
+for ix, svr in enumerate(svrs):
+    axes[ix].plot(
+        X,
+        svr.fit(X, y).predict(X),
+        color=model_color[ix],
+        lw=lw,
+        label="{} model".format(kernel_label[ix]),
+    )
+    axes[ix].scatter(
+        X[svr.support_],
+        y[svr.support_],
+        facecolor="none",
+        edgecolor=model_color[ix],
+        s=50,
+        label="{} support vectors".format(kernel_label[ix]),
+    )
+    axes[ix].scatter(
+        X[np.setdiff1d(np.arange(len(X)), svr.support_)],
+        y[np.setdiff1d(np.arange(len(X)), svr.support_)],
+        facecolor="none",
+        edgecolor="k",
+        s=50,
+        label="other training data",
+    )
+    axes[ix].legend(
+        loc="upper center",
+        bbox_to_anchor=(0.5, 1.1),
+        ncol=1,
+        fancybox=True,
+        shadow=True,
+    )
+
+fig.text(0.5, 0.04, "data", ha="center", va="center")
+fig.text(0.06, 0.5, "target", ha="center", va="center", rotation="vertical")
+fig.suptitle("Support Vector Regression", fontsize=14)
+plt.show()

charts/algorithm_3.py

@@ -0,0 +1,57 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.pylab import rcParams
+from keras.models import Sequential
+from keras.layers import LSTM, Dropout, Dense
+from sklearn.preprocessing import MinMaxScaler
+
+df = pd.read_csv('static/HistoricalData_APL.csv')
+df = df[['Date', 'Close/Last']]
+df = df.replace({'\$': ''}, regex=True)
+print(df.head())
+df = df.astype({"Close/Last": float})
+df["Date"] = pd.to_datetime(df.Date, format="%m/%d/%Y")
+print(df.dtypes)
+df.index = df['Date']
+plt.plot(df["Close/Last"], label='AAPL Close Price history')
+plt.show()
+df = df.sort_index(ascending=True,axis=0)
+data = pd.DataFrame(index=range(0,len(df)),columns=['Date','Close/Last'])
+for i in range(0,len(data)):
+    data["Date"][i]=df['Date'][i]
+    data["Close/Last"][i]=df["Close/Last"][i]
+scaler=MinMaxScaler(feature_range=(0,1))
+data.index=data.Date
+data.drop("Date",axis=1,inplace=True)
+final_data = data.values
+train_data=final_data[0:200,:]
+valid_data=final_data[200:,:]
+scaler=MinMaxScaler(feature_range=(0,1))
+scaled_data=scaler.fit_transform(final_data)
+x_train_data,y_train_data=[],[]
+for i in range(60,len(train_data)):
+    x_train_data.append(scaled_data[i-60:i,0])
+    y_train_data.append(scaled_data[i,0])
+lstm_model=Sequential()
+lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(np.shape(x_train_data)[1],1)))
+lstm_model.add(LSTM(units=50))
+lstm_model.add(Dense(1))
+model_data=data[len(data)-len(valid_data)-60:].values
+model_data=model_data.reshape(-1,1)
+model_data=scaler.transform(model_data)
+lstm_model.compile(loss='mean_squared_error',optimizer='adam')
+lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=1,verbose=2)
+X_test=[]
+for i in range(60,model_data.shape[0]):
+    X_test.append(model_data[i-60:i,0])
+X_test=np.array(X_test)
+X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
+predicted_stock_price=lstm_model.predict(X_test)
+predicted_stock_price=scaler.inverse_transform(predicted_stock_price)
+train_data=data[:200]
+valid_data=data[200:]
+valid_data['Predictions']=predicted_stock_price
+plt.plot(train_data["Close/Last"])
+plt.plot(valid_data[['Close/Last',"Predictions"]])
+plt.show()

charts/algorithm_4.py

@@ -0,0 +1,63 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.model_selection import train_test_split
+
+# data = pd.read_csv("static/obligacjePL.csv")
+#
+# months = data['Data']
+# prices = data['Otwarcie']
+#
+# months = np.array(months).reshape(-1, 1)
+#
+# model = LinearRegression()
+# model.fit(months, prices)
+#
+# future_months = np.array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]).reshape(-1, 1)
+# predictions = model.predict(future_months)
+# plt.plot(months, prices, label='actual')
+# plt.plot(future_months, predictions, label='prediction')
+# plt.legend()
+# plt.xlabel('Months')
+# plt.ylabel('Prices')
+# plt.title('Stock Prices Prediction')
+# plt.grid()
+# plt.show()
+
+data = pd.read_csv("static/AAPL2.csv")
+months = data['Date']
+prices = data['Open']
+months = np.array(months).reshape(-1, 1)
+poly_feat = PolynomialFeatures(degree=2)
+months_poly = poly_feat.fit_transform(months)
+model = LinearRegression()
+model.fit(months_poly, prices)
+future_months = np.array([12, 13, 14, 15, 16, 17, 18]).reshape(-1, 1)
+future_months_poly = poly_feat.fit_transform(future_months)
+predictions = model.predict(future_months_poly)
+plt.plot(months, prices, color='cyan')
+plt.plot(future_months, predictions, color='pink')
+plt.title('Polynomial Regression')
+plt.xlabel('Months')
+plt.ylabel('Prices')
+plt.xticks(range(1, 18))
+plt.show()
+X_train, X_test, y_train, y_test = train_test_split(months_poly, prices, test_size=0.2, random_state=0)
+
+# Fit the model to the training data
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions on the test data
+y_pred = model.predict(X_test)
+
+# Calculate the MSE and R-Squared
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+# Print the results
+print("Mean Squared Error:", mse)
+print("R-Squared:", r2)

charts/apps.py

@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class ChartsConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'charts'

charts/management/commands/import_stats.py

@@ -0,0 +1,38 @@
+import csv
+
+from django.core.management.base import BaseCommand
+from dateutil.parser import parse
+
+from charts.models import BiteStat
+
+
+class Command(BaseCommand):
+    help = 'Import bite exercise stats'
+
+    def add_arguments(self, parser):
+        parser.add_argument('-c', '--csv', required=True)
+
+    def handle(self, *args, **options):
+        file = options["csv"]
+        with open(file) as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                completed = row["first_completed"]
+                if not completed:
+                    continue
+
+                level = row["user_level"]
+                if not level:
+                    level = 0
+
+                date = parse(completed)
+                stat, created = BiteStat.objects.get_or_create(
+                    exercise=row["bite_id"],
+                    completed=date,
+                    level=level,
+                )
+
+                if created:
+                    self.stdout.write(f"{stat} created")
+                else:
+                    self.stderr.write(f"{stat} already in db")

charts/migrations/0001_initial.py

@@ -0,0 +1,23 @@
+# Generated by Django 4.1.3 on 2022-12-19 20:06
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='BiteStat',
+            fields=[
+                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('exercise', models.PositiveSmallIntegerField()),
+                ('completed', models.DateField()),
+                ('level', models.PositiveSmallIntegerField(blank=True, null=True)),
+            ],
+        ),
+    ]

charts/models.py

@@ -0,0 +1,7 @@
+from django.db import models
+
+
+class BiteStat(models.Model):
+    exercise = models.PositiveSmallIntegerField()  # 0 to 32767
+    completed = models.DateField()  # I don't care about time here
+    level = models.PositiveSmallIntegerField(null=True, blank=True)  # optional, not every Bite has user feedback

charts/static/AAPL.csv

@@ -0,0 +1,252 @@
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+2022-12-28,129.669998,131.029999,125.870003,126.040001,126.040001,85438400
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+2022-12-30,128.410004,129.949997,127.430000,129.929993,129.929993,76960600

charts/static/AAPL2.csv

@@ -0,0 +1,54 @@
+Date,Open
+01.02,176.063
+01.09,176.801
+01.16,168.097
+01.23,170.52
+01.30,159.105
+02.06,169.174
+02.13,172.086
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+03.06,162.325
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+03.20,150.77
+03.27,162.774
+04.03,171.39
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+04.17,167.947
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+05.01,160.391
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+06.19,132.466
+06.26,133.014
+07.03,142.261
+07.10,137.351
+07.17,145.227
+07.24,150.282
+07.31,153.541
+08.07,160.521
+08.14,166.094
+08.21,171.236
+08.28,169.409
+09.04,160.878
+09.11,156.211
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+09.25,149.063
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+10.09,137.981
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+10.23,140.831
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+11.06,152.901
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+11.20,148.97
+11.27,150.16
+12.04,145.14
+12.11,147.77
+12.18,142.7
+12.25,135.11
+

charts/static/HistoricalData_APL.csv

@@ -0,0 +1,20 @@
+Date,Close/Last,Volume,Open,High,Low
+01/06/2023,$129.62,87754720,$126.01,$130.29,$124.89
+01/05/2023,$125.02,80962710,$127.13,$127.77,$124.76
+01/04/2023,$126.36,89113630,$126.89,$128.6557,$125.08
+01/03/2023,$125.07,112117500,$130.28,$130.9,$124.17
+12/30/2022,$129.93,77034210,$128.41,$129.95,$127.43
+12/29/2022,$129.61,75703710,$127.99,$130.4814,$127.73
+12/28/2022,$126.04,85438390,$129.67,$131.0275,$125.87
+12/27/2022,$130.03,69007830,$131.38,$131.41,$128.72
+12/23/2022,$131.86,63814890,$130.92,$132.415,$129.64
+12/22/2022,$132.23,77852110,$134.352,$134.56,$130.3
+12/21/2022,$135.45,85927990,$132.98,$136.81,$132.75
+12/20/2022,$132.3,77432820,$131.39,$133.25,$129.89
+12/19/2022,$132.37,79592610,$135.11,$135.2,$131.32
+12/16/2022,$134.51,160156900,$136.685,$137.65,$133.73
+12/15/2022,$136.5,98931910,$141.11,$141.8,$136.025
+12/14/2022,$143.21,82291180,$145.35,$146.655,$141.16
+12/13/2022,$145.47,93886160,$149.5,$149.9692,$144.24
+12/12/2022,$144.49,70462650,$142.7,$144.5,$141.06
+12/09/2022,$142.16,76097010,$142.34,$145.57,$140.9

charts/static/akcjeWIG40.csv

@@ -0,0 +1,13 @@
+Data,Otwarcie,Najwyzszy,Najnizszy,Zamkniecie,Wolumen
+01,5310.69
+02,5123.32
+03,4590.48
+04,4786.4
+05,4374.13
+06,4351.82
+07,4069.32
+08,4142.42
+09,3828.67
+10,3539.92
+11,3835.14
+12,4136.36

charts/static/mwig40_m.csv

@@ -0,0 +1,13 @@
+Data,Otwarcie,Najwyzszy,Najnizszy,Zamkniecie,Wolumen
+2022-01-31,5310.69,5584.52,4960.09,5106.59,110936154
+2022-02-28,5123.32,5299.36,4133.55,4589.49,124463773
+2022-03-31,4590.48,4879.14,4022.22,4778.07,250932976
+2022-04-30,4786.4,4876.56,4298.09,4370.19,178369206
+2022-05-31,4374.13,4398.18,4025.77,4349.02,161111044
+2022-06-30,4351.82,4364.81,3933.24,4069.37,127681534.74069
+2022-07-31,4069.32,4151.32,3871.78,4151.32,152999536
+2022-08-31,4142.42,4300.99,3787.83,3838.57,137259580
+2022-09-30,3828.67,4026.74,3493.06,3552.54,155079325
+2022-10-31,3539.92,3824.04,3489.63,3824.04,136478417
+2022-11-30,3835.14,4142.91,3782.99,4108.81,262803731.84112
+2022-12-31,4136.36,4199.5,3992.16,4154.32,162133955

charts/static/obligacjePL.csv

@@ -0,0 +1,13 @@
+Data,Otwarcie,Najwyzszy,Najnizszy,Zamkniecie
+01,100.11
+02,99.87
+03,99.83
+04,94.69
+05,91.27
+06,91.96
+07,92.03
+08,95.79
+09,93.63
+10,91.88
+11,88.51
+12,95.01

charts/static/xauusd_y.csv

@@ -0,0 +1,15 @@
+Data;Otwarcie;Najwyzszy;Najnizszy;Zamkniecie
+2010-12-31;1095.2;1430.6;1043.72;1420.55
+2011-12-31;1419;1921.07;1308.26;1564.81
+2012-12-31;1569.36;1795.54;1526.63;1676.4
+2013-12-31;1670.6;1696.09;1180.16;1204.12
+2014-12-31;1205.1;1388.34;1131.78;1183
+2015-12-31;1187.09;1306.43;1046.64;1061.46
+2016-12-31;1064.65;1375.42;1062.61;1150.91
+2017-12-31;1150.53;1357.56;1146.18;1303.33
+2018-12-31;1303.38;1366.12;1160.31;1282.56
+2019-12-31;1282.6;1557.1;1266.48;1517.305
+2020-12-31;1518.17;2075.11;1451.28;1898.705
+2021-12-31;1903.49;1959.11;1671.9;1829.5
+2022-12-31;1830.05;2069.94;1614.905;1823.48
+2023-12-31;1823.65;1826.7;1823.48;1824.14

charts/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

charts/urls.py

@@ -0,0 +1,6 @@
+from django.urls import path
+from charts import views
+
+urlpatterns = [
+    path('', views.index, name='graph'),
+]

charts/views.py

@@ -0,0 +1,28 @@
+from django.shortcuts import render
+
+from collections import Counter
+from math import ceil
+
+from django.shortcuts import render
+
+from charts.models import BiteStat
+
+
+def index(request):
+    stats = BiteStat.objects.order_by('completed')
+
+    data = Counter()
+    for row in stats:
+        yymm = row.completed.strftime("%Y-%m")
+        data[yymm] += 1
+
+    # unpack dict keys / values into two lists
+    labels, values = zip(*data.items())
+
+    context = {
+        "labels": labels,
+        "values": values,
+    }
+    return render(request, "graph.html", context)
+
+

education/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

education/apps.py

@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class EducationConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'education'

education/models.py

@@ -0,0 +1,3 @@
+from django.db import models
+
+# Create your models here.

education/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

education/views.py

@@ -0,0 +1,6 @@
+from django.shortcuts import render
+
+
+def education_view(request):
+    context = {}
+    return render(request, 'education/education.html', context)

pdf_view/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

pdf_view/apps.py

@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class PdfViewConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'pdf_view'

pdf_view/models.py

@@ -0,0 +1,3 @@
+from django.db import models
+
+# Create your models here.

pdf_view/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

pdf_view/views.py

@@ -0,0 +1,9 @@
+from django.shortcuts import render
+from django.http import  HttpResponse
+
+
+# def pdf_view(request):
+#     with open('Czym jest portfel inwestycyjny.pdf', 'rb') as pdf:
+#         response = HttpResponse(pdf.read(), content_type='application/pdf')
+#         response['Content-Disposition'] = 'inline;filename=mypdf.pdf'
+#         return response

static/Zeszyt1.csv

@@ -0,0 +1,102 @@
+writer;surprise;science;glass;mixture;current
+79770;551446;3215546;5819979770;1;57
+0;3;273;27;842;99857890
+4;32;7513624;28331;949598;6473025
+89;544527;4467;16834745;606;5681
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+993;495569353;52;974896955;319836;
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+;4074212;6262;;2849432;57
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+2829;687539227;7099532438;941859;736794314;
+590328791;679306515;3274;96950;3668;
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+;18268916;0;504456;266553541;2
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static/main.css

@@ -1,3 +1,9 @@
 .grad {
   background-image: linear-gradient(to right, orange , white);
+}
+.center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+  width: 50%;
 }

templates/graph.html

@@ -0,0 +1,11 @@
+{% load static %}
+<!DOCTYPE html>
+<html>
+<head>
+<title>Bite exercise stats</title>
+</head>
+<body>
+
+
+</body>
+</html>

templates/main.html

@@ -26,19 +26,22 @@
       <li class="nav-item">
           {% if user.is_authenticated %}
         <a class="nav-link" href="/logout">Wyloguj</a>
+          <a class="nav-link" href="/oblplot/">Użyj bezpiecznego wariantu</a>
+          <a class="nav-link" href="/wigplot">Użyj bardziej ryzykownego wariantu</a>
+          <a class="nav-link" href="https://www.xtb.com/pl">Zainwestuj z Xtb</a>
+          <a class="nav-link" href="https://www.etoro.com/pl/">Zainwestuj z Etoro</a>
               {% else %}
           <a class="nav-link" href="/login">Zaloguj</a>
           {% endif %}
       </li>
       <li class="nav-item dropdown">
         <a class="nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
-          Dropdown
+          Edukacja
         </a>
         <div class="dropdown-menu" aria-labelledby="navbarDropdown">
-          <a class="dropdown-item" href="#">Action</a>
+          <a class="dropdown-item" href="/view-pdf1">Informacje o stronie</a>
-          <a class="dropdown-item" href="#">Another action</a>
+          <a class="dropdown-item" href="/view-pdf-rynki/">Obligacje</a>
-          <div class="dropdown-divider"></div>
+          <a class="dropdown-item" href="/view-pdf-akcje/">Akcje</a>
-          <a class="dropdown-item" href="#">Something else here</a>
         </div>
       </li>
       <li class="nav-item">
@@ -48,8 +51,8 @@
       </li>
     </ul>
     <form class="form-inline my-2 my-lg-0">
-      <input class="form-control mr-sm-2" type="search" placeholder="Search" aria-label="Search">
+      <input class="form-control mr-sm-2" type="search" placeholder="Search" aria-label="Szukaj">
-      <button class="btn btn-outline-success my-2 my-sm-0" type="submit">Search</button>
+      <button class="btn btn-outline-success my-2 my-sm-0" type="submit">Szukaj</button>
     </form>
   </div>
 </nav>
@@ -57,9 +60,10 @@
     {% block content %}
     <div>
       <h1 style="text-align:center">Witaj na stronie FinTech!</h1>
-      <h4>Tutaj możesz nauczyć się jak inwestować i prognozować kursy z pomocą sztucznej inteligencji.</h4>
+      <h4 style="text-align:center">Tutaj możesz nauczyć się jak inwestować i prognozować kursy z pomocą sztucznej inteligencji.</h4>
       </div>
 
+<div style="flex:2"><img class="center" src="{% static 'images/charts.jpg' %}"></div>
     {% endblock %}
   <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" crossorigin="anonymous"></script>
     <script src="https://cdn.jsdelivr.net/npm/popper.js@1.12.9/dist/umd/popper.min.js" integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q" crossorigin="anonymous"></script>

templates/my_template.html

@@ -0,0 +1,10 @@
+{% load matplotlib %}
+<html>
+    <head>
+        <title>My Plot</title>
+    </head>
+    <body>
+        <h1>My Plot</h1>
+        {% matplotlib_inline fig %}
+    </body>
+</html>

templates/obligacjePLplot.html

@@ -0,0 +1,13 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+<img class="center" src="{{ uri }}" alt="Stock Prices Prediction">
+<h3>Oto wykres obligacji polskich przedstawiający zmianę ich wartości w rozbiciu miesięcznym w roku 2022 z
+    prognozą na kolejne 6 miesięcy. Jak widać wahania nie są duże, więc ryzyko związane z inwestycją
+    jest relatywnie niskie. Trzeba pamiętać jednak, że zysk nie będzie również wysoki.</h3>
+</body>
+</html>

templates/plot.html

@@ -0,0 +1,11 @@
+{% extends "main.html" %}
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+
+</body>
+</html>

templates/registration/login.html

@@ -29,7 +29,7 @@
   <form method="POST">
     {% csrf_token %}
     {{ login_form|crispy }}
-    <button class="btn btn-primary" type="submit">Login</button>
+    <button class="btn btn-primary" type="submit">Zaloguj</button>
   </form>
   <p class="text-center">Nie masz konta? <a href="/register">Zarejestruj się</a>.</p>
 </div>

templates/registration/register.html

@@ -7,13 +7,13 @@
 
 <!--Register--> 
 <div class="container py-5">
-	<h1>Register</h1>
+	<h1>Zarejestruj się</h1>
 	<form method="POST">
 		{% csrf_token %}
 		{{ register_form|crispy }}                    
-		<button class="btn btn-primary" type="submit">Register</button>
+		<button class="btn btn-primary" type="submit">Zarejestruj</button>
 	</form>
-	<p class="text-center">If you already have an account, <a href="/login">login</a> instead.</p>
+	<p class="text-center">Jeżeli masz już konto, <a href="/login">zaloguj się</a>. </p>
 </div>
 
 {% endblock %}

templates/wigplot.html

@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+<img class="center" src="{{ uri }}" alt="Stock Prices Prediction">
+<h3>Oto wykres wartości 40 największych polskich spółek na przestrzeni roku 2022 w podziale na miesiące
+    z prognozą na kolejne pół roku. Jak widać wahania są całkiem duże, więc można dużo zyskać ale również dużo stracić</h3>
+</body>
+</html>

tests/test_1.py

@@ -0,0 +1,21 @@
+from django.contrib.auth.models import User
+from django.http import request, HttpResponseRedirect
+from django.test import TestCase
+from django.urls import reverse
+
+
+class LogInTest(TestCase):
+    def setUp(self):
+        self.credentials = {
+            'username': 'test123',
+            'password': 'Fintech123'}
+        User.objects.create_user(**self.credentials)
+
+    def test_login(self):
+        response = self.client.post('/accounts/login', self.credentials, follow=True)
+        self.assertFalse(response.context['user'].is_authenticated)
+
+class ButtonTest(TestCase):
+    def button_test(self):
+        if request.POST.get('Submit') == 'Submit':
+            print('user clicked submit')