ichimoku.py

import numpy as np
import pandas as pd
from pandas_datareader import data, wb
import yfinance as yf
import datetime as dt
from chart_studio import plotly
from plotly.subplots import make_subplots
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, plot, iplot

def get_ichimoku_plot():

    # Range of Crypto

    #def fetch_data():
    ticker = ["ETH-USD"]

    #start = pd.to_datetime('2020-01-01')
    end = dt.datetime.now().date()
    crypto_df = yf.download(ticker, period = "1y", group_by = 'ticker', threads = True, progress=False)[['Open','High','Low','Close']].dropna()
    #return crypto_df
    #fetch_data().tail()

    index = pd.date_range(end, periods=26, freq='D')
    columns = crypto_df.columns
    dfna = pd.DataFrame(index=index, columns=columns)
    crypto_df = pd.concat([crypto_df,dfna])

    # Tenkan-sen (Conversion Line): (nine-period high + nine-period low)/2))
    nine_period_high = crypto_df['High'].rolling(window= 9).max()
    nine_period_low = crypto_df['Low'].rolling(window= 9).min()
    crypto_df['tenkan_sen'] = (nine_period_high + nine_period_low) /2

    # Kijun-sen (Base Line): (26-period high + 26-period low)/2))
    period26_high = crypto_df['High'].rolling(window=26).max()
    period26_low = crypto_df['Low'].rolling(window=26).min()
    crypto_df['kijun_sen'] = (period26_high + period26_low) / 2

    # The most current closing price plotted 26 time periods behind (optional)
    crypto_df['chikou_span'] = crypto_df['Close'].shift(-26)

    # Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2))
    crypto_df['senkou_span_a'] = ((crypto_df['tenkan_sen'] + crypto_df['kijun_sen']) / 2).shift(26)

    # Senkou Span B (Leading Span B): (52-period high + 52-period low)/2))
    period52_high = crypto_df['High'].rolling(window=52).max()
    period52_low = crypto_df['Low'].rolling(window=52).min()
    crypto_df['senkou_span_b'] = ((period52_high + period52_low) / 2).shift(26)

    # Grab just the `date` and `close` from the IEX dataset
    #ichimoku_signals_df = pd.DataFrame(crypto_df['Close'])
    crypto_df.index.name = 'Date'

    # Initialize the new `Signal1` column
    crypto_df['Signal1'] = 0

    # Generate the trading signal (1 or 0) to when the -----Conversion cross over the Base-----
    # Note: Use 1 when Conversion is more than/crosses over the Base and 0 for when it is not/lower.
    #Conversion
    crypto_df['Signal1'] = np.where(
        (crypto_df['tenkan_sen'].shift(1) <= crypto_df['kijun_sen'].shift(1)) & (crypto_df['tenkan_sen'] > crypto_df['kijun_sen']), 1, crypto_df['Signal1'])
    #Base
    crypto_df['Signal1'] = np.where(
        (crypto_df['tenkan_sen'].shift(1) >= crypto_df['kijun_sen'].shift(1)) & (crypto_df['tenkan_sen'] < crypto_df['kijun_sen']), -1, crypto_df['Signal1'])


    df_new = crypto_df[crypto_df['Signal1'] == 1.0][['Close']]

    #declare figure

    fig = go.Figure()

    # Set up traces
    fig.add_trace(go.Candlestick(x=crypto_df.index,
                                open=crypto_df['Open'],
                                high=crypto_df['High'],
                                low=crypto_df['Low'],
                                close=crypto_df['Close'], name = 'market data'))


    fig.add_trace(go.Scatter(x=crypto_df.index, y= crypto_df['tenkan_sen'],line=dict(color='royalblue', width=.8), name = 'Conversion Line'))
    fig.add_trace(go.Scatter(x=crypto_df.index, y= crypto_df['kijun_sen'],line=dict(color='darkorange', width=.8), name = 'Base Line'))
    fig.add_trace(go.Scatter(x=crypto_df.index, y= crypto_df['senkou_span_a'],line=dict(color='green', width=.8), name = 'Leading A'))
    fig.add_trace(go.Scatter(x=crypto_df.index, y= crypto_df['senkou_span_b'],line=dict(color='red', width=.8), name = 'Leading B'))
    fig.add_trace(go.Scatter(x=crypto_df.index, y= crypto_df['chikou_span'],line=dict(color='black', width=.8), name = 'Lagging Span'))

    # Visualize entry position relative to close price
    fig.add_trace(go.Scatter(x= df_new.index, 
                            y= df_new['Close'],
                            marker_color='yellow', 
                            mode="markers", 
                            name = 'Entry', 
                            marker=dict(showscale=True)))


    #Show
    fig.update_layout(height=900, title_text="Entry and Exit Points of Ichimoku Span A & B crossover Trading Strategy", legend=dict(
    yanchor="top",
    y=0.99,
    xanchor="left",
    x=0.01
))

    return fig