Cleanup (no new features)
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186
indicators.py
186
indicators.py
@ -21,20 +21,13 @@ Could also come up with a value that ties to the trading volume.
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import pandas as pd
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import numpy as np
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import datetime as dt
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# from util import get_data, get_price_volume, plot_data, get_watchlist
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from util import get_price_volume, plot_data, get_watchlist
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# from marketsim import compute_portvals, compute_portfolio_stats, normalize_data
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# import matplotlib.pyplot as plt
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# import matplotlib
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from util import get_watchlist
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from numpy.fft import fft, ifft
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import scipy.signal as sig
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import plotly.express as px
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from plotly.subplots import make_subplots
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# import plotly.graph_objects as go
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from dash import Dash, html, dcc, callback, Output, Input, State, no_update
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from waitress import serve
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import json
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import io
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from flask_caching import Cache
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from dash.exceptions import PreventUpdate
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import dash_auth
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@ -106,7 +99,6 @@ def smooth(price, hsize=10, sigma=3):
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return data
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class security:
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"""
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This can be a list of stocks, bonds, or otherinvestment vehicles.
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@ -350,100 +342,6 @@ class security:
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norm_hist = macd_hist.div(macd_long.values)
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return macd_line, macd_sig, macd_hist, norm_hist
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# def bollinger_sell(stock, wd=200):
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# """
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# Parameters
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# ----------
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# stock : TYPE class 'serurity'
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# wd : TYPE, int, optional
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# DESCRIPTION. Moving average windows. The default is 200.
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# Returns
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# -------
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# TYPE DataFrame
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# DESCRIPTION - +1 when stock price is above bollinger upper band
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# . -1 when vice versa. transition days are of value +3 and -3
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# respectively. A value of -3 is a sell signal
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# """
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# _, bol_up = stock.bollinger(wd)
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# # bol_up = bol_up[bol_up.columns[-1]].to_frame()
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# # bol_up = bol_up.iloc[:, [-1]]
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# sell = np.sign(stock.price.sub(bol_up.values))
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# sell_diff = sell.diff()
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# return sell.add(sell_diff.values)
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# def bollinger_buy(stock, wd=200):
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# """
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# Parameters
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# ----------
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# stock : TYPE class 'serurity'
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# wd : TYPE, int, optional
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# DESCRIPTION. Moving average windows. The default is 200.
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# Returns
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# -------
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# TYPE DataFrame
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# DESCRIPTION - +1 when stock price is above bollinger lower band
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# . -1 when vice versa. transition days are of value +3 and -3
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# respectively. A value of +3 is a buy signal
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# """
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# bol_low, _ = stock.bollinger(wd)
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# buy = np.sign(stock.price.sub(bol_low.values))
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# buy_diff = buy.diff()
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# return buy.add(buy_diff.values)
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# def simple_bollinger_strategy(stk):
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# # buy orders
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# buy = bollinger_buy(stk, 190)
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# buy_orders = buy[np.any(buy>2, axis=1)]
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# sell = bollinger_sell(stk, 190)
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# sell_orders = sell[np.any(sell<-2, axis=1)]
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# orders = pd.concat([buy_orders, sell_orders])
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# orders = orders.sort_index()
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# order_list = pd.DataFrame(columns = ['Date', 'Symbol', 'Order', 'Shares'])
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# for index, row in orders.iterrows():
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# for sym in orders.columns.values:
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# if row[sym] > 2: # buy order
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# order_list = order_list.append({'Date' : index, 'Symbol' : sym,
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# 'Order' : 'BUY', 'Shares' : 100}, ignore_index = True )
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# elif row[sym] < -2: # sell order
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# order_list = order_list.append({'Date' : index, 'Symbol' : sym,
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# 'Order' : 'SELL', 'Shares' : 100}, ignore_index = True )
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# order_list = order_list.set_index('Date')
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# return order_list
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# def plot_against_sym(df, sym=['SPY']):
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# df_temp = df.copy()
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# df_sym = get_data(sym, pd.to_datetime(df_temp.index.values), addSPY=False)
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# df_temp[sym[0]] = df_sym.values
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# df_temp = normalize_data(df_temp)
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# plot_data(df_temp)
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# return df_sym
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# def test_bollinger_sell():
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# sd = dt.datetime(2010,1,1)
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# # ed = dt.datetime.today()
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# ed = dt.datetime(2012,12,31)
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# symbol = ['XOM']
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# dates = dates = pd.date_range(sd, ed)
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# prices = get_data(symbol, dates, addSPY=False)
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# # prices = prices.dropna()
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# stk = security(prices)
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# sell = bollinger_sell(stk)
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# plot_data(sell)
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# buy = bollinger_buy(stk, 190)
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# plot_data(buy)
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def get_crossing(stocks):
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"""
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Parameters
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@ -488,49 +386,9 @@ def get_sma_slope(stocks, wd = 50):
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return slope
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# def modified_bollinger_strategy(stk):
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# rsi = stk.rsi()
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# btemp = pd.DataFrame()
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# # buy orders
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# buy = bollinger_buy(stk, 190)
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# buy_orders = buy[np.any(buy>2, axis=1)]
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# for col in buy_orders.columns:
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# buy_stk = buy_orders[col]
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# buy_stk = buy_stk[buy_stk > 2]
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# buy_stk = buy_stk[rsi[col].loc[buy_stk.index] < 70]
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# btemp = btemp.join(buy_stk, how='outer')
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# stemp = pd.DataFrame()
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# sell = bollinger_sell(stk, 190)
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# sell_orders = sell[np.any(sell<-2, axis=1)]
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# for col in sell_orders.columns:
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# sell_stk = sell_orders[col]
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# sell_stk = sell_stk[sell_stk < -2]
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# sell_stk = sell_stk[rsi[col].loc[sell_stk.index] > 30]
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# stemp = stemp.join(sell_stk, how='outer')
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# orders = pd.concat([btemp, stemp])
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# # TODO - refine orders based on slope
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# # TODO - revine further based on other conditions (RSI, MACD)
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# # TODO - transaction shares determination
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# orders = orders.sort_index()
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# order_list = pd.DataFrame(columns = ['Date', 'Symbol', 'Order', 'Shares'])
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# for index, row in orders.iterrows():
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# for sym in orders.columns.values:
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# if row[sym] > 2: # buy order
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# order_list = order_list.append({'Date' : index, 'Symbol' : sym,
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# 'Order' : 'BUY', 'Shares' : 100}, ignore_index = True )
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# elif row[sym] < -2: # sell order
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# order_list = order_list.append({'Date' : index, 'Symbol' : sym,
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# 'Order' : 'SELL', 'Shares' : 100}, ignore_index = True )
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# order_list = order_list.set_index('Date')
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# return order_list
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LB_YEAR = 3 # years of stock data to retrieve
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# PLT_YEAR = 2 # number of years data to plot
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LB_TRIGGER = 5 # days to lookback for triggering events
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def intelligent_loop_plots(sym, stk_data):
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# Only plot ones that are standing out meaning:
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@ -563,25 +421,25 @@ def intelligent_loop_plots(sym, stk_data):
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# print('{:5}: '.format(sym), end = '')
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# RSI outside window (over bought / over sold)
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rsi_tail = rsi.tail(lb_trigger)
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rsi_tail = rsi.tail(LB_TRIGGER)
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if (rsi_tail >= 70).any() or (rsi_tail <= 30).any():
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# print('--RSI', end = '')
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plot_indicator += 'RSI, '
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# VoRSI outside window (over bought / over sold)
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vorsi_tail = vorsi.tail(lb_trigger)
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vorsi_tail = vorsi.tail(LB_TRIGGER)
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if (vorsi_tail >= 70).any() or (vorsi_tail <= 30).any():
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# print('--VoRSI', end = '')
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plot_indicator += 'VoRSI, '
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# Normalized MACD histogram out of 3% range
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norm_hist_tail = abs(norm_hist.tail(lb_trigger))
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norm_hist_tail = abs(norm_hist.tail(LB_TRIGGER))
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if (abs(norm_hist_tail) >= 0.02).any():
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# print('--MACD/R', end = '') # outside normal range
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plot_indicator += 'MACD/R, '
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# MACD histogram zero crossing
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macd_hist_tail = macd_hist.tail(lb_trigger)
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macd_hist_tail = macd_hist.tail(LB_TRIGGER)
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macd_hist_sign = np.sign(macd_hist_tail)
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macd_hist_diff = macd_hist_sign.diff()
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if (abs(macd_hist_diff) > 1).any():
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@ -589,7 +447,7 @@ def intelligent_loop_plots(sym, stk_data):
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plot_indicator += 'MACD, '
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# Stock price crosses SMA50
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sma50_cross_tail = sma50.tail(lb_trigger) - price.tail(lb_trigger)
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sma50_cross_tail = sma50.tail(LB_TRIGGER) - price.tail(LB_TRIGGER)
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sma50_cross_sign = np.sign(sma50_cross_tail)
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sma50_cross_diff = sma50_cross_sign.diff()
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if (abs(sma50_cross_diff) > 1).any():
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@ -597,7 +455,7 @@ def intelligent_loop_plots(sym, stk_data):
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plot_indicator += 'SMA50, '
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# Death cross or golden cross - SMA50 vs SMA200
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sma_cross_tail = sma50.tail(lb_trigger) - sma200.tail(lb_trigger).values
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sma_cross_tail = sma50.tail(LB_TRIGGER) - sma200.tail(LB_TRIGGER).values
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sma_cross_sign = np.sign(sma_cross_tail)
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sma_cross_diff = sma_cross_sign.diff()
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if (abs(sma_cross_diff) > 1).any():
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@ -605,9 +463,9 @@ def intelligent_loop_plots(sym, stk_data):
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plot_indicator += 'Golden/Death, '
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# Price outside bollinger band or crossing
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price_tail = price.tail(lb_trigger)
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bol_low_tail = bol_low.tail(lb_trigger)
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bol_up_tail = bol_up.tail(lb_trigger)
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price_tail = price.tail(LB_TRIGGER)
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bol_low_tail = bol_low.tail(LB_TRIGGER)
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bol_up_tail = bol_up.tail(LB_TRIGGER)
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price_high = price_tail - bol_up_tail.values
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price_low = price_tail - bol_low_tail.values
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if (price_high >= 0).any() or (price_low <= 0).any():
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@ -615,7 +473,7 @@ def intelligent_loop_plots(sym, stk_data):
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plot_indicator += 'Bollinger, '
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# Price cross 200 day moving average
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sma200_tail = sma200.tail(lb_trigger)
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sma200_tail = sma200.tail(LB_TRIGGER)
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sma200_cross = price_tail - sma200_tail.values
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sma200_cross_sign = np.sign(sma200_cross)
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sma200_cross_diff = sma200_cross_sign.diff()
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@ -624,7 +482,7 @@ def intelligent_loop_plots(sym, stk_data):
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plot_indicator += 'SMA200, '
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# Large trading volume trigger
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volume_tail = vol.tail(lb_trigger)
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volume_tail = vol.tail(LB_TRIGGER)
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vol_mean = vol.tail(50).mean()
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vol_std = vol.tail(50).std()
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if ((volume_tail[1] - vol_mean - 2*vol_std) > 0).any():
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@ -667,12 +525,6 @@ def user_login(username, password):
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return True
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return False
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if __name__ == "__main__":
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lb_year = 3 # years of stock data to retrieve
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plt_year = 2 # number of years data to plot
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lb_trigger = 5 # days to lookback for triggering events
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# Initialize the app
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app = Dash()
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dash_auth.BasicAuth(
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@ -792,11 +644,9 @@ if __name__ == "__main__":
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raise PreventUpdate
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# return no_update
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# get data
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# tmp = si.get_data(sym, start_date=sd)[["adjclose", "volume"]]
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sym = col_chosen.split()[0]
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end_date = dt.datetime.today()
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start_date = end_date - dt.timedelta(days = 365 * lb_year)
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start_date = end_date - dt.timedelta(days = 365 * LB_YEAR)
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# plot_sd = end_date - dt.timedelta(days = 365 * plt_year)
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# plot_ed = end_date
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tmp = fetch_stk_data(sym, start_date) # cached function all
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@ -815,7 +665,6 @@ if __name__ == "__main__":
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data,
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x=data.index,
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y=data.columns.to_list(),
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# y=[sym, sym+'_BOL200L', sym+'_BOL200U', sym+'_SMA200', sym+'_SMA50', sym+'_WVMA50'],
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)
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volume_line = px.bar(
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@ -876,5 +725,6 @@ if __name__ == "__main__":
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return fig
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serve(app.server, host="0.0.0.0", port=8050, threads=7)
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if __name__ == "__main__":
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serve(app.server, host="0.0.0.0", port=8050, threads=7) # using production quality WSGI server Waitress
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# app.run(debug=True)
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