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16036bc 960b05a 79cf85f 16036bc 960b05a 16036bc 79cf85f 960b05a 79cf85f 960b05a 529c6a0 960b05a 529c6a0 960b05a 529c6a0 960b05a 79cf85f 16036bc 960b05a 79cf85f 16036bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | import pandas as pd
from flask import Flask, render_template, request
import plotly.graph_objs as go
app = Flask(__name__, template_folder="templates")
# Load the data
assets_data = pd.read_csv("data/assets_data.csv", index_col=0)
train_predictions = pd.read_csv("data/train_prediction.csv", index_col=0)
test_predictions = pd.read_csv("data/test_prediction.csv", index_col=0)
predictions = pd.concat([train_predictions, test_predictions])
# Create a column for buy/sell signals
predictions['Signal'] = 'None'
predictions.loc[predictions['Prediction'] > 0, 'Signal'] = 'Buy'
predictions.loc[predictions['Prediction'] < 0, 'Signal'] = 'Sell'
@app.route('/', methods=['GET', 'POST'])
def plot():
starting_value = 400 # Default starting value
if request.method == 'POST':
starting_value = float(request.form['starting_value'])
# Calculate the value of the investment
investment_value = []
current_value = starting_value
buy_dates = []
sell_dates = []
prev_signal = None # Track the previous signal
for date in assets_data.index:
if date in predictions.index:
signal = predictions.loc[date, 'Signal']
if signal == 'Buy':
price_change = assets_data.loc[date, 'target'] / 100
current_value *= (1 + price_change)
if signal != prev_signal:
buy_dates.append(date)
elif signal == 'Sell':
if signal != prev_signal:
sell_dates.append(date)
prev_signal = signal
investment_value.append(current_value)
investment_data = pd.DataFrame(data=investment_value,
index=assets_data.index,
columns=['Investment Value'])
table_data = pd.DataFrame({
'Date': assets_data.index,
'BTC Price': assets_data['close'],
'Prediction': predictions['Prediction'],
'Investment Value': investment_data['Investment Value']
})
fig = go.Figure()
# Add the BTC Price line
fig.add_trace(go.Scatter(
x=assets_data.index,
y=assets_data['close'],
mode='lines',
name='BTC Price'
))
# Add the Investment Value line
fig.add_trace(go.Scatter(
x=investment_data.index,
y=investment_data['Investment Value'],
mode='lines',
name='Investment Value'
))
# Add Buy and Sell signals as markers
fig.add_trace(go.Scatter(
x=buy_dates,
y=assets_data.loc[buy_dates, 'close'],
mode='markers',
name='Buy',
marker_symbol='triangle-up',
marker=dict(size=10, color='green')
))
fig.add_trace(go.Scatter(
x=sell_dates,
y=assets_data.loc[sell_dates, 'close'],
mode='markers',
name='Sell',
marker_symbol='triangle-down',
marker=dict(size=10, color='red')
))
fig.update_layout(
xaxis_title='Date',
yaxis_title='Value',
title='BTC Price vs. Investment Value with Buy/Sell Signals',
)
plot_url = fig.to_html(full_html=False)
return render_template('plot.html', plot_url=plot_url,
starting_value=starting_value, data=table_data)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860)
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