Algorithmic Trading

# openjdk install !apt-get update !apt-get install -y unzip openjdk-8-jre-headless xvfb libxi6 libgconf-2-4 # Install Chrome. !apt-get -y install wget !wget http://dl.google.com/linux/chrome/deb/pool/main/g/google-chrome-beta/google-chrome-beta_91.0.4472.27-1_amd64.deb !apt -y install ./google-chrome-beta_91.0.4472.27-1_amd64.deb # Install ChromeDriver. !wget -N https://chromedriver.storage.googleapis.com/79.0.3945.36/chromedriver_linux64.zip -P ~/ !unzip ~/chromedriver_linux64.zip -d ~/ !rm ~/chromedriver_linux64.zip !mv -f ~/chromedriver /usr/local/bin/chromedriver !chown root:root /usr/local/bin/chromedriver !chmod 0755 /usr/local/bin/chromedriver # Install Selenium !pip install selenium==3.141.0 !pip install chromedriver-py==91.0.4472.19 !pip install beautifulsoup4 !pip install ipdb !pip install pystan==2.19.1.1 !pip install prophet !pip install anvil-uplink !pip install cvxpy !pip install cvxopt !pip install PyPortfolioOpt

from chromedriver_py import binary_path from selenium import webdriver from selenium.webdriver.common.action_chains import ActionChains import os import shutil import pandas as pd from bs4 import BeautifulSoup import requests import time import glob import matplotlib.pyplot as plt import numpy as np import sys import plotly.express as px import plotly.graph_objs as go from prophet import Prophet from prophet.plot import plot_plotly, plot_components_plotly import plotly.tools as tls import json

filepath = 'data' dest = 'data' url_path = 'urls.csv' mse_a = pd.read_csv(url_path) mse_a.head()

def get_data(): for url in mse_a['URL']: chrome_options = webdriver.ChromeOptions() prefs = {"download.default_directory" : 'data', "download.prompt_for_download": False, "download.directory_upgrade": True} chrome_options.add_experimental_option("prefs",prefs) chrome_options.add_argument('--headless') chrome_options.add_argument('--no-sandbox') chrome_options.add_argument('--disable-gpu') chrome_options.add_argument('--window-size=1280x1696') chrome_options.add_argument('--user-data-dir=/tmp/user-data') chrome_options.add_argument('--hide-scrollbars') chrome_options.add_argument('--enable-logging') chrome_options.add_argument('--log-level=0') chrome_options.add_argument('--v=99') chrome_options.add_argument('--single-process') chrome_options.add_argument('--data-path=/tmp/data-path') chrome_options.add_argument('--ignore-certificate-errors') chrome_options.add_argument('--homedir=/tmp') chrome_options.add_argument('--disk-cache-dir=/tmp/cache-dir') chrome_options.add_argument('user-agent=Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36') driver = webdriver.Chrome(executable_path = binary_path,options=chrome_options) try: page_response = requests.get(url, timeout=15) soup = BeautifulSoup(page_response.content) symbol = soup.find(id="trade_chart").h1.string.split('(')[-1].split(')')[0] driver.get(url) down_arrow = driver.find_element_by_xpath('//*[@id="chartdiv"]/div/div[2]/ul/li/a') save_as = driver.find_element_by_xpath('//*[@id="chartdiv"]/div/div[2]/ul/li/ul/li[2]/a') button = driver.find_element_by_xpath('//*[@id="chartdiv"]/div/div[2]/ul/li/ul/li[2]/ul/li[1]/a') ActionChains(driver).move_to_element(down_arrow).move_to_element(save_as).click(button).perform() time.sleep(10) driver.close() list_of_files = glob.glob(filepath + '/*') # * means all if need specific format then *.csv latest_file = max(list_of_files, key=os.path.getctime) os.rename(latest_file, dest + '/' + symbol + '.csv') time.sleep(10) except: print('error on: ' + url)

get_data()

## Momentum Strategy def buy_sell(data): sigPriceBuy = [] sigPriceSell = [] flag = -1 for i in range(len(data)): if data['SMA30'][i] > data['SMA100'][i]: if flag != 1: sigPriceBuy.append(data['value'][i]) sigPriceSell.append(np.nan) flag = 1 else: sigPriceBuy.append(np.nan) sigPriceSell.append(np.nan) elif data['SMA30'][i] < data['SMA100'][i]: if flag != 0: sigPriceBuy.append(np.nan) sigPriceSell.append(data['value'][i]) flag = 0 else: sigPriceBuy.append(np.nan) sigPriceSell.append(np.nan) else: sigPriceBuy.append(np.nan) sigPriceSell.append(np.nan) return (sigPriceBuy, sigPriceSell)

import anvil.server import anvil.mpl_util anvil.server.connect("GVJYCJ6HX42BEZG6FR2FQBEN-UB3GQ5B2IA2KQ32O") @anvil.server.callable def load_data(symbol): df= pd.read_csv('data/'+symbol+'.csv') df['SMA30'] = df['value'].rolling(window = 30).mean() df['SMA100'] = df['value'].rolling(window = 100).mean() bs = buy_sell(df) df['Buy_Signal_Price'] = bs[0] df['Sell_Signal_Price'] = bs[1] array = df.to_numpy() date = array[:,0] value = array[:,1] SMA30 = array[:,3] SMA100 = array[:,4] buy_signal = array[:,5] sell_signal = array[:,6] return date, value, SMA30, SMA100, buy_signal, sell_signal

from pypfopt.efficient_frontier import EfficientFrontier from pypfopt import risk_models from pypfopt import expected_returns from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices from functools import reduce

@anvil.server.callable def optimize(symbols,investment): df_merged = pd.DataFrame(columns = ['date']) df_merged = df_merged.set_index('date') for item in symbols: temp_df = pd.read_csv('data/'+item+'.csv') temp_df = temp_df.set_index('date') temp_df = temp_df.drop(columns = {'volume'}).rename(columns = {'value': item}) df_merged = pd.merge(df_merged, temp_df, how='outer', on='date') #df_merged = df_merged.dropna() #portfolio: mu = expected_returns.mean_historical_return(df_merged) S = risk_models.sample_cov(df_merged) ef = EfficientFrontier(mu,S) ef.add_constraint(lambda w: sum(w) == 1) weights = ef.max_sharpe() var = ef.portfolio_performance(verbose=True) #Allocation latest_prices = get_latest_prices(df_merged) da = DiscreteAllocation(weights,latest_prices,total_portfolio_value = investment) allocation,leftover = da.lp_portfolio() return var,weights, allocation, leftover

a = dict(optimize(['APU','LEND'],20000)[1]) a

b = optimize(['APU','LEND'],20000)[2] b

def mergeDict(dict1, dict2): ''' Merge dictionaries and keep values of common keys in list''' dict3 = {**dict1, **dict2} for key, value in dict3.items(): if key in dict1 and key in dict2: dict3[key] = {value , dict1[key]} return dict3

c = mergeDict(a,b)

@anvil.server.callable def prophet_data(symbol): data = pd.read_csv('data/'+symbol+'.csv') data['date'] = pd.to_datetime(data['date']) data = data.drop(columns = ['volume']).rename(columns={"date": "ds", "value": "y"}) data = data[data['ds'].dt.dayofweek < 5] m = Prophet(interval_width=0.95, weekly_seasonality= False) m.add_seasonality(name = 'monthly', period=30.5, fourier_order=5) m.fit(data) future = m.make_future_dataframe(periods=365) forecast = m.predict(future) #the main one fig = plot_plotly(m,forecast,xlabel = 'Огноо',ylabel = 'Хувьцааны үнэ (₮)', trend =True ) fig1 = fig.to_json() fig_data = json.loads(fig1)['data'] fig_layout = json.loads(fig1)['layout'] #the seasonality sea = m.plot_components(forecast).savefig(os.devnull) return fig1,fig_data, fig_layout, anvil.mpl_util.plot_image()