"XOM", "BAC", "MCD", "WMT", "HD", "CMG", "GM", "F", "TGT", "KO", "GIS",
"MRO", "PSX", "AXP", "GS", "NDAQ", "ABC", "CELG", "CVS", "AAL", "BA", "GE",
"LUV", "CTXS", "SHW", "AMT", "REG", "AEE", "EXC", "PCG", "UNP", "WM"
]
#StocksTicker=['BTC',"ETH","LTC","BCH","EOS",'LINK','TRX']
Prices = []
CurrentPrices = []
DayBefore = []
Mean = []
Correlation = []
Order = []
df = pd.DataFrame(columns=StocksTicker)
for x in StocksTicker:
y = prices(assets=symbols(x),
start='2019-04-24',
end='2019-09-24',
frequency='minute')
df[x] = pd.Series(
np.gradient(
np.gradient(
scipy.signal.savgol_filter(y, 31, 2, 0, 1.0, -1, 'interp',
0.0))))
corr = df.corr()
#corr.describe() useless for us rn
#can make it more legible if needed,
#it will be much more easier from this to see the correlation
ax = sns.heatmap(corr, vmin=-1, vmax=1, center=0, square=True)
#less number of negative correlation. bit shocking though.
# In[1]:
############INTRODUCTION########################
# Research environment functions
from quantopian.research import prices, symbols
# Pandas library: https://pandas.pydata.org/
import pandas as pd
# Query historical pricing data for AAPL
aapl_close = prices(
assets=symbols('AAPL'),
start='2013-01-01',
end='2016-01-01',
)
# Compute 50 and 200 day moving averages on
# AAPL's pricing data
aapl_sma50 = aapl_close.rolling(50).mean()
aapl_sma200 = aapl_close.rolling(200).mean()
# Combine results into a pandas DataFrame and plot
pd.DataFrame({
'AAPL': aapl_close,
'SMA50': aapl_sma50,
'SMA200': aapl_sma200
}).plot(
title='AAPL Close Price / SMA Crossover'
# Computing library imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
# ## Pricing & Returns
# Historic pricing & returns data is imported and visualized based on the long-term period defined above. DataFrames are defined for cross referencing to other auxillary data.
# In[3]:
# Query historical pricing data for TDG
tdg_close = prices(assets = asset, start = analysis_start,end = analysis_end,)
# Compute 20 and 50 day moving averages on pricing data
tdg_sma20 = tdg_close.rolling(20).mean()
tdg_sma50 = tdg_close.rolling(50).mean()
# Combine results into a pandas DataFrame and plot
tdg_pricing_dataframe = pd.DataFrame({
stock_symbol: tdg_close,
'SMA20': tdg_sma20,
'SMA50': tdg_sma50})
# Query returns data for TDG
tdg_returns = returns(assets = symbols(stock_symbol),start = analysis_start,end = analysis_end,)
# Plot pricing data
# Research environment functions
from quantopian.research import prices, symbols
# Pandas library: https://pandas.pydata.org/
import pandas as pd
# Query historical pricing data for AAPL
aapl_close = prices(
assets=symbols('AAPL'),
start='2013-01-01',
end='2015-01-01',
)
# Compute 20 and 50 day moving averages on
# AAPL's pricing data
aapl_sma20 = aapl_close.rolling(20).mean()
aapl_sma50 = aapl_close.rolling(50).mean()
# Combine results into a pandas DataFrame and plot
pd.DataFrame({
'AAPL': aapl_close,
'SMA20': aapl_sma20,
'SMA50': aapl_sma50
}).plot(title='AAPL Close Price / SMA Crossover')
import pandas as pd
import datetime
# Research environment
# Select a time range to inspect
period_start = pd.Timestamp('2014-01-01')
period_end = pd.Timestamp('2015-12-31')
# Select a security ticker to inspect
# Top choices: GOOG and GLD both perform well
stock_symbol = 'QQQ'
stock_close = prices(assets=symbols(stock_symbol),
start=period_start,
end=period_end)
extremadataframe = pd.DataFrame({
'price': [],
'type': [],
'trend': [],
},
index=[])
orders = pd.DataFrame({
'type': [],
'prev_exitgain': [],
'marker': []
},
index=[])
increasing = False
# Importar libreria de analisis de datos
import pandas as pd
# Importar componentes de la libreria de Quantopian
from quantopian.research import prices, symbols
# Obtener precios de cierre diario de una organización
serie = prices(
assets=symbols(acronimo), # Acronimo de la organización
start='fecha_inicio', # 'AAAA-MM-DD'
end='fecha_fin', # 'AAAA-MM-DD'
)
# Obtener retornos diarios de una organización
serie = returns(
assets=symbols(acronimo), # Acronimo de la organización
start='fecha_inicio', # 'AAAA-MM-DD'
end='fecha_fin', # 'AAAA-MM-DD'
)
# Obtener el numero de acciones negociadas
serie = volumes(
assets=symbols(acronimo), # Acronimo de la organización
start='fecha_inicio', # 'AAAA-MM-DD'
end='fecha_fin', # 'AAAA-MM-DD'
)
# Obtener precios logatirmicos de cierre diario de una organización
serie = log_prices(
assets=symbols(acronimo), # Acronimo de la organización
@author: Hp
"""
######################THIS PROJECT WILL WORK ONLY ON QUANTOPIAN#####################
#SIMPLE MOVING AVERAGE CROSSOVER
#Import the Libraries
from quantopian.research import prices, symbols, returns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#CLOSING PRICES FOR PEPSI AND COKE FROM 2018 to MAY 2020
pep_close = prices(assets=symbols('PEP'), start='2018-01-01', end='2020-05-27')
coke_close = prices(assets=symbols('KO'), start='2018-01-01', end='2020-05-27')
#ROLLING WINDOW FOR 15 and 75 DAY MOVING AVERAGE FOR BOTH STOCKS
pep_sma_15 = pep_close.rolling(window=15, min_periods=1, center=False).mean()
coke_sma_15 = coke_close.rolling(window=15, min_periods=1, center=False).mean()
pep_sma_75 = pep_close.rolling(window=75, min_periods=1, center=False).mean()
coke_sma_75 = coke_close.rolling(window=75, min_periods=1, center=False).mean()
#DATA FRAME TO CALCULATE THE SIGNALS (TRADING POINTS)
trade_points = pd.DataFrame()
#INITIALIZING THE DATA FRAME WITH THE 15- & 75-DAY MOVING AVERAGES
trade_points['Pepsi_15_Day_SMA'] = pep_sma_15
trade_points['Pepsi_75_Day_SMA'] = pep_sma_75
# In addition to sentiment data, we will need pricing data for all assets present in this period. We can easily get a list of these assets from our pipeline output's index, and pass that list to `prices` to get the pricing data we need:
# In[4]:
# Import prices function
from quantopian.research import prices
# Get list of unique assets from the pipeline output
asset_list = pipeline_output.index.levels[1].unique()
# Query pricing data for all assets present during
# evaluation period
asset_prices = prices(
asset_list,
start=period_start,
end=period_end
)
# Now we can use Quantopian's open source factor analysis tool, [Alphalens](https://www.quantopian.com/lectures/factor-analysis-with-alphalens), to test the quality of our selection strategy. First, let's combine our factor and pricing data using get_clean_factor_and_forward_returns. This function classifies our factor data into quantiles and computes forward returns for each security for multiple holding periods. We will separate our factor data into 2 quantiles (the top and bottom half), and use 1, 5 and 10 day holding periods:
# In[5]:
# Import Alphalens
import alphalens as al
# Get asset forward returns and quantile classification
# based on sentiment scores
factor_data = al.utils.get_clean_factor_and_forward_returns(
