I used ddg, bing and google to find notebooks on notebooks-preview.prd.pub.quantopian.com. I replaced a few files so it easier to view and still works in the future. Below you find all downloaded notebooks.
| Title | Link |
|---|---|
| Normalization and Classifiers | quantopian_notebook_0.html |
| Building a Better Beta | quantopian_notebook_1.html |
| Calculating Log Returns With Pipeline | quantopian_notebook_2.html |
| Analyzing a Long/Short Equity Pipeline | quantopian_notebook_3.html |
| Pipeline Example: Piotrosky Score | quantopian_notebook_4.html |
| When Can Sharpe Ratio and Cumulative Returns Have Different Signs? | quantopian_notebook_5.html |
| Using TA-Lib Functions in Pipelines | quantopian_notebook_6.html |
| When Can Sharpe Ratio and Cumulative Returns Have Different Signs? | quantopian_notebook_7.html |
| Sanity Check that Values Still Match | quantopian_notebook_8.html |
| Introduction to the Quantopian Risk Model in Research | quantopian_notebook_9.html |
| In [5]: from sqlalchemy import or_ fundamentals = init_fundamentals() sp_500 = get_fundamentals( query(fun | quantopian_notebook_10.html |
| Zscore producing too many Nans? Am I doing something wrong? | quantopian_notebook_11.html |
| In [1]: import numpy as np from quantopian.research import run_pipeline from quantopian.pipeline import Pipeline from | quantopian_notebook_12.html |
| Quantopian Research | quantopian_notebook_13.html |
| In [3]: bt = get_backtest('5a10aabd4ce931411f988252') 100% Time: 0:00:57|######################################### | quantopian_notebook_14.html |
| In [1]: bt = get_backtest('58c7bf845126854761492bce') 100% Time: 0:01:03|######################################### | quantopian_notebook_15.html |
| In [89]: from pytz import timezone import matplotlib.pyplot as plt import pandas as pd In [107]: # get minute bar | quantopian_notebook_16.html |
| In [1]: bt = get_backtest('5a0f2f288b13eb440b93a10a') 100% Time: 0:00:43|######################################### | quantopian_notebook_17.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_18.html |
| Enter your backtest ID. | quantopian_notebook_19.html |
| In [1]: bt = get_backtest('57cb2d614f4c380ffbb18651') 100% Time: 0:38:22|######################################### | quantopian_notebook_20.html |
| Enter your backtest ID. | quantopian_notebook_21.html |
| In [1]: bt = get_backtest('58ac0dc57e45305dfebc72e0') 100% Time: 0:00:04|######################################### | quantopian_notebook_22.html |
| References: https://www.quantopian.com/posts/research-platform-how-to-get-a-nice-heatmap https://www.quantopian.com/pos | quantopian_notebook_23.html |
| In [1]: bt = get_backtest('5884a39bb07bf961362be5f6') 100% Time: 0:00:46|######################################### | quantopian_notebook_24.html |
| In [1]: # From https://www.quantopian.com/posts/relevant-fundamental-factors#5b9563549ad4a0004e03850d # Working Capi | quantopian_notebook_25.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_26.html |
| rudimentary stock screener - step through a list of symbols, analyze each one, and store the result | quantopian_notebook_27.html |
| In [1]: bt = get_backtest('5815b5c45a1c550f21af2250') 100% Time: 0:01:27|######################################### | quantopian_notebook_28.html |
| Enter your backtest ID. | quantopian_notebook_29.html |
| In [7]: bt = get_backtest('5a2c2959cc0e384569a87c02') 100% Time: 0:00:50|######################################### | quantopian_notebook_30.html |
| In [76]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipelin | quantopian_notebook_31.html |
| In [1]: # https://www.quantopian.com/posts/long-short-pipeline-multi-factor backtest = get_backtest('5627b09e3dfd59112 | quantopian_notebook_32.html |
| In [1]: bt = get_backtest('56a8157633749711029e987b') 100% Time: 0:00:45|######################################### | quantopian_notebook_33.html |
| In [1]: bt = get_backtest('56a8157633749711029e987b') 100% Time: 0:00:45|######################################### | quantopian_notebook_34.html |
| Alpha decay analyisis | quantopian_notebook_35.html |
| Comparing Diversification Techniques to Hierarchical Risk Parity | quantopian_notebook_36.html |
| An updated method to analyze alpha factors | quantopian_notebook_37.html |
| Machine Learning inside of Pipline | quantopian_notebook_38.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_39.html |
| In [1]: bt = get_backtest('568e8ab422d8fe1180543dae') bt.create_full_tear_sheet() 100% Time: 0:00:20|############# | quantopian_notebook_40.html |
| A tutorial on Markowitz portfolio optimization in Python using cvxopt | quantopian_notebook_41.html |
| The influence of COVID-19 cases on companies according to their geographic revenue | quantopian_notebook_42.html |
| In [2]: bt = get_backtest('5a0b26f0e92c0f41c9d758d3') 100% Time: 0:01:18|######################################### | quantopian_notebook_43.html |
| In [1]: # Get backtest object bt = get_backtest('55d5994eca36d10d86841047') # Create all tear sheets bt.create_full_te | quantopian_notebook_44.html |
| Putting It All Together | quantopian_notebook_45.html |
| In [17]: import statsmodels.api as sm import pandas as pd In [2]: data = get_pricing(['PEP', 'KO'], start_date='20 | quantopian_notebook_46.html |
| Using the Kalman Filter in Algorithmic Tradin | quantopian_notebook_47.html |
| In [138]: import numpy as np import pandas as pd from scipy import stats from pytz import timezone import matplotlib.p | quantopian_notebook_48.html |
| Kalman Filters | quantopian_notebook_49.html |
| In [56]: import numpy as np import pandas as pd from scipy import stats from pytz import timezone import matplotlib.py | quantopian_notebook_50.html |
| In [186]: import pandas as pd from scipy import stats from pytz import timezone import matplotlib.pyplot as plt import | quantopian_notebook_51.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_52.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_53.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_54.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_55.html |
| In [1]: import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns import empyrical | quantopian_notebook_56.html |
| In [20]: from collections import deque In [5]: start = '2015-09-01' end = '2016-04-20' asset = get_pricing('MSFT', | quantopian_notebook_57.html |
| In [20]: from collections import deque In [5]: start = '2015-09-01' end = '2016-04-20' asset = get_pricing('MSFT', | quantopian_notebook_58.html |
| In [20]: from collections import deque In [5]: start = '2015-09-01' end = '2016-04-20' asset = get_pricing('MSFT', | quantopian_notebook_59.html |
| Can Warren Buffett Also Predict Equity Market Downturns? | quantopian_notebook_60.html |
| In [845]: import numpy as np import scipy as sp In [846]: days_in_quarter = 3 num_of_quarters=4 In [847]: x = | quantopian_notebook_61.html |
| CHS Model (RISK OF FINANCIAL DISTRESS) | quantopian_notebook_62.html |
| Using Alphalens to study if forward_earning_yield has predictive power over returns | quantopian_notebook_63.html |
| In [90]: # Import a Kalman filter and other useful libraries from pykalman import KalmanFilter import numpy as np impo | quantopian_notebook_64.html |
| The Capital Asset Pricing Model Revisited | quantopian_notebook_65.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_66.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_67.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_68.html |
| Quantopian Risk Model | quantopian_notebook_69.html |
| The CAPM Revisited II | quantopian_notebook_70.html |
| In [86]: import numpy as np import pandas as pd import numbers # adopted from https://www.mathworks.com/matlabcentral/ | quantopian_notebook_71.html |
| In [86]: import numpy as np import pandas as pd import numbers # adopted from https://www.mathworks.com/matlabcentral/ | quantopian_notebook_72.html |
| FactSet Ownership - Aggregated Insider Transactions Overview | quantopian_notebook_73.html |
| Heatmap Example | quantopian_notebook_74.html |
| EventVestor: Dividend Announcements | quantopian_notebook_75.html |
| In [1]: backtest = '56fecb03e199f10f401e380e' bt = get_backtest(backtest) 100% Time: 0:00:35|##################### | quantopian_notebook_76.html |
| In [1]: bt = get_backtest('5798bcc8a634c01301e0fed8') 100% Time: 0:01:57|######################################### | quantopian_notebook_77.html |
| Quantpedia Series: Reversal during Earnings Announcements | quantopian_notebook_78.html |
| Factor Tearsheet | quantopian_notebook_79.html |
| 13D Filings Event Study | quantopian_notebook_80.html |
| PsychSignal Series: Introduction | quantopian_notebook_81.html |
| Estimize in Quantopian: Improving your Algos with Earnings Predictions | quantopian_notebook_82.html |
| Event Study with EventVestor's Share Buybacks | quantopian_notebook_83.html |
| PsychSignal Series: Research Design | quantopian_notebook_84.html |
| You want to run a lot of backtests. I get it. | quantopian_notebook_85.html |
| Zacks: Earnings Surprise | quantopian_notebook_86.html |
| Before Proceeding: Click here to import necessary functions | quantopian_notebook_87.html |
| Before Proceeding: Click here to import necessary functions | quantopian_notebook_88.html |
| Before Proceeding: Click here to import necessary functions | quantopian_notebook_89.html |
| Can we create and optimize a strategy using share buyback data? | quantopian_notebook_90.html |
| Quantpedia Series: Predicting Earnings Following Buyback Announcements | quantopian_notebook_91.html |
| Event Study | quantopian_notebook_92.html |
| PsychSignal Series: Introduction | quantopian_notebook_93.html |
| Load a backtest containing an in sample period through the live trading period | quantopian_notebook_94.html |
| PsychSignal: StockTwits Trader Mood (All Fields) | quantopian_notebook_95.html |
| In [2]: import numpy as np import pandas as pd from datetime import timedelta from datetime import date from quantopia | quantopian_notebook_96.html |
| In [2]: import numpy as np import pandas as pd from datetime import timedelta from datetime import date from quantopia | quantopian_notebook_97.html |
| Strategy Research on Quantopian | quantopian_notebook_98.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.pipeline import CustomFactor from quantopian.research | quantopian_notebook_99.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_100.html |
| Hurst Exponent Approximation Factor | quantopian_notebook_101.html |
| Common Risk Factor Performance | quantopian_notebook_102.html |
| In [1]: # Get backtest object bt = get_backtest('5989bac564d2a359d3e420e6') 100% Time: 0:00:37|################### | quantopian_notebook_103.html |
| Gaussian Copula Conditionals | quantopian_notebook_104.html |
| Classical statistical arbitrage and maximum mean reversion | quantopian_notebook_105.html |
| In [1]: # Get backtest object bt = get_backtest('59859055e3e8b44fbeddad93') 100% Time: 0:00:31|################### | quantopian_notebook_106.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_107.html |
| In [3]: # Get backtest object bt = get_backtest('57bbdfdc1b2ff1100ff72694') # Create all tear sheets bt.create_full_te | quantopian_notebook_108.html |
| In [1]: from quantopian.pipeline.data import Fundamentals from quantopian.pipeline.data import morningstar from quanto | quantopian_notebook_109.html |
| In [16]: # Concise Checker # Replace string with your backtest URL tail end. bt = get_backtest('5ac354b804eeea42662c8c | quantopian_notebook_110.html |
| In [3]: # 5 year run bt = get_backtest('560da18fb8cfd6109585fa92') 100% Time: 0:00:13|############################ | quantopian_notebook_111.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_112.html |
| CIK In [1]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data import Fundamentals import matpl | quantopian_notebook_113.html |
| Getting Data | quantopian_notebook_114.html |
| In [1]: from quantopian.research import run_pipeline from quantopian.pipeline import Pipeline from quantopian.pipeline | quantopian_notebook_115.html |
| Beneish Model | quantopian_notebook_116.html |
| Find ETF Bond funds that move counter to SPY In [359]: import pandas as pd import matplotlib.pyplot as pyplot import | quantopian_notebook_117.html |
| Backtesting a Moving Average Crossover Strategy | quantopian_notebook_118.html |
| Backtesting a Moving Average Crossover Strategy | quantopian_notebook_119.html |
| Backtesting a Moving Average Crossover Strategy | quantopian_notebook_120.html |
| Qgrid - An interactive grid for exploring pandas DataFrames | quantopian_notebook_121.html |
| Qgrid - An interactive grid for exploring pandas DataFrames | quantopian_notebook_122.html |
| In [17]: import pandas as pd import numpy as np import statsmodels from statsmodels.tsa.stattools import coint import | quantopian_notebook_123.html |
| Characterizing Data - Skewness and Kurtosis | quantopian_notebook_124.html |
| The Correlation Coefficient | quantopian_notebook_125.html |
| In [6]: import matplotlib.pyplot as plt import pandas as pd import numpy as np In [2]: bt = get_backtest('57c13dc1 | quantopian_notebook_126.html |
| Researching a Pairs Trading Strategy | quantopian_notebook_127.html |
| Statistical Moments - Skewness and Kurtosis | quantopian_notebook_128.html |
| The regression model | quantopian_notebook_129.html |
| Linear Regression | quantopian_notebook_130.html |
| Integration, Cointegration, and Stationarity | quantopian_notebook_131.html |
| Regression Analysis | quantopian_notebook_132.html |
| Model specification | quantopian_notebook_133.html |
| Position Concentration Risk | quantopian_notebook_134.html |
| Integration, Cointegration, and Stationarity | quantopian_notebook_135.html |
| Measuring Momentum | quantopian_notebook_136.html |
| In [1]: import pandas as pd import numpy as np import statsmodels from statsmodels.tsa.stattools import coint import m | quantopian_notebook_137.html |
| Measuring monotonic relationships | quantopian_notebook_138.html |
| Checking Factor Correlation and Risk Exposure | quantopian_notebook_139.html |
| Portfolio Value at Risk and Conditional Value at Risk | quantopian_notebook_140.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_141.html |
| Model Risk Exposure | quantopian_notebook_142.html |
| Measuring monotonic relationships | quantopian_notebook_143.html |
| Momentum Strategies | quantopian_notebook_144.html |
| Researching a Pairs Trading Strategy | quantopian_notebook_145.html |
| Researching a Pairs Trading Strategy | quantopian_notebook_146.html |
| セクターが違う5つの銘柄を毎週ポジションを調整しながら保有し続ける | quantopian_notebook_147.html |
| Alphalens Quickstart Template | quantopian_notebook_148.html |
| In [6]: from quantopian.pipeline import Pipeline from quantopian.pipeline import CustomFactor from quantopian.research | quantopian_notebook_149.html |
| Kalman Filters | quantopian_notebook_150.html |
| Investing in Women-Led Companie | quantopian_notebook_151.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_152.html |
| Constructing a Pipeline in Research | quantopian_notebook_153.html |
| Investing In Women-led Fortune 1000 Companie | quantopian_notebook_154.html |
| Investing in Women-Led Companie | quantopian_notebook_155.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_156.html |
| In [1]: # coding=utf-8 import numpy as np import pandas as pd from numpy import abs from numpy import log from numpy i | quantopian_notebook_157.html |
| In [1]: # Import Zipline, the open source backester, and a few other libraries that we will use import zipline from zi | quantopian_notebook_158.html |
| Finding the Capital Market Line | quantopian_notebook_159.html |
| Pairs Trading with Natural Language Processing | quantopian_notebook_160.html |
| Pairs Trading with Machine Learning | quantopian_notebook_161.html |
| In [64]: import pandas as pd import numpy as np from quantopian.pipeline import Pipeline from quantopian.pipeline impo | quantopian_notebook_162.html |
| In [64]: import pandas as pd import numpy as np from quantopian.pipeline import Pipeline from quantopian.pipeline impo | quantopian_notebook_163.html |
| In [64]: import pandas as pd import numpy as np from quantopian.pipeline import Pipeline from quantopian.pipeline impo | quantopian_notebook_164.html |
| In [69]: vixx = local_csv('YAHOO-INDEX_VIX.csv', date_column = 'Date', use_date_column_as_index='True', timezone='UTC' | quantopian_notebook_165.html |
| Alphalens + Quantopian | How To | quantopian_notebook_166.html |
| Alphalens + Quantopian | How To | quantopian_notebook_167.html |
| Alphalens boilerplate | quantopian_notebook_168.html |
| In [1]: import numpy as np import pandas as pd from quantopian.research import run_pipeline from quantopian.pipeline i | quantopian_notebook_169.html |
| Factor Tearsheet | quantopian_notebook_170.html |
| In [6]: from quantopian.research.experimental import get_factor_returns f_returns = get_factor_returns(start="2005-01 | quantopian_notebook_171.html |
| Alphalens + Quantopian | How To | quantopian_notebook_172.html |
| Factor Tearsheet | quantopian_notebook_173.html |
| Alphalens boilerplate | quantopian_notebook_174.html |
| Hierarchical Clustering | quantopian_notebook_175.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_176.html |
| Alphalens boilerplate | quantopian_notebook_177.html |
| Alphalens + Quantopian | How To | quantopian_notebook_178.html |
| Visualizing the QTradableStocksUS | quantopian_notebook_179.html |
| Factor Tearsheet | quantopian_notebook_180.html |
| Performance Attribution | quantopian_notebook_181.html |
| Step 1: Fail | quantopian_notebook_182.html |
| May 14th Quantopian Hackathon 2016 | quantopian_notebook_183.html |
| Alphalens - Open Source Factor Analysis | quantopian_notebook_184.html |
| Algo Performance Analysis "Tearsheet" | quantopian_notebook_185.html |
| In [6]: bt = get_backtest('560469497a01cb0e2456a606') 100% Time: 0:03:34|######################################### | quantopian_notebook_186.html |
| In [2]: bt = get_backtest('5881a7241ee54f5e005ec083') bt.create_full_tear_sheet() 100% Time: 0:00:02|############# | quantopian_notebook_187.html |
| In [1]: bt = get_backtest('594144cb0a059969efde1fc2') bt.create_full_tear_sheet() 100% Time: 0:00:06|############# | quantopian_notebook_188.html |
| 101 Alphas Project: Pipeline Factor Information Coefficent | quantopian_notebook_189.html |
| Alpha Template | quantopian_notebook_190.html |
| Empirical Algorithmic Implementation of Technical Analysis | quantopian_notebook_191.html |
| RECREATING THE CNN FEAR AND GREED INDEX | quantopian_notebook_192.html |
| In [2]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_193.html |
| COVID-19 growth analysis | quantopian_notebook_194.html |
| ^^^ performance of strategy against individual stocks. | quantopian_notebook_195.html |
| In [27]: import pyfolio as pf import matplotlib.pyplot as plt plot_TAANG, = plt.plot(pf.timeseries.cum_returns(get_bac | quantopian_notebook_196.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_197.html |
| In [24]: from quantopian.pipeline import CustomFactor from quantopian.pipeline import Pipeline from quantopian.researc | quantopian_notebook_198.html |
| Comparing OOS performance "long-term-buy-and-hold-on-margin" strategies | quantopian_notebook_199.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_200.html |
| In [19]: import numpy as np import pandas as pd import talib import matplotlib.pyplot as plt In [20]: """This cell | quantopian_notebook_201.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_202.html |
| A disqualified algo out of sample backtest 07/29/2016 - 08/14/2019 | quantopian_notebook_203.html |
| In [9]: import numpy as np import pandas as pd from scipy import stats from pytz import timezone import datetime impor | quantopian_notebook_204.html |
| Comparison "My all weather trio" with original constituents but different rebalance frequency (weekly,monthly,quarterly,half_year,yearly) | quantopian_notebook_205.html |
| An updated method to analyze alpha factors | quantopian_notebook_206.html |
| An updated method to analyze alpha factors | quantopian_notebook_207.html |
| Run the cell below to create your tear sheet, or return to your algorithm. In [1]: bt = get_backtest('5a0b043672485d | quantopian_notebook_208.html |
| Round trips Summary stats Alan | quantopian_notebook_209.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_210.html |
| Load a backtest containing an in sample period through the live trading period | quantopian_notebook_211.html |
| In [55]: from quantopian.pipeline.filters import QTradableStocksUS from quantopian.pipeline import Pipeline from quant | quantopian_notebook_212.html |
| In [299]: # Author: Gael Varoquaux gael.varoquaux@normalesup.org # License: BSD 3 clause import datetime import numpy | quantopian_notebook_213.html |
| Modified "How to Get an Allocation" to better suit Sentdex | quantopian_notebook_214.html |
| Naive Bayes High Low Return Prediction Analysis based on Thomas Wiecki 's Post | quantopian_notebook_215.html |
| What is the strategy? | quantopian_notebook_216.html |
| Alphalens + Quantopian | How To | quantopian_notebook_217.html |
| Simulating S&P 500, Russel 1000, Russell 3000 in Research | quantopian_notebook_218.html |
| In [1]: from quantopian.research import run_pipeline from quantopian.pipeline import Pipeline from quantopian.pipeline | quantopian_notebook_219.html |
| In [15]: import pyfolio as pf In [1]: pravin = get_backtest("578e3b0fbefbc00f975a5816") 100% Time: 0:01:53|### | quantopian_notebook_220.html |
| ================================================================ | quantopian_notebook_221.html |
| Running Pipeline algorithm in research | quantopian_notebook_222.html |
| In [9]: # Import Zipline, the open source backester, and a few other libraries that we will use import zipline from zi | quantopian_notebook_223.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_224.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_225.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_226.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_227.html |
| New in Pipeline: Column Slices and New Factor Methods | quantopian_notebook_228.html |
| Federal Reserve Sentiment and Macro-Tracking ETFs | quantopian_notebook_229.html |
| In [131]: import matplotlib.pyplot as plt #import matplotlib.gridspec as gridspec import pyfolio as pf ############### | quantopian_notebook_230.html |
| Running Pipeline algorithm in research | quantopian_notebook_231.html |
| Example of using CEOChange in pipeline | quantopian_notebook_232.html |
| In [78]: import pandas as pd import matplotlib.pyplot as plt small_avg = 30 large_avg = 60 start_date = '2014-01-03' e | quantopian_notebook_233.html |
| In [2]: # - Will cause an error because I have not bought the Eventvestor data yet #from quantopian.interactive.data.e | quantopian_notebook_234.html |
| The Bean Report | quantopian_notebook_235.html |
| Making Fama French Visuals | quantopian_notebook_236.html |
| In [1]: # - Will cause an error because I have not bought the Eventvestor data yet #from quantopian.interactive.data.e | quantopian_notebook_237.html |
| During the "Quant Crash" of Aug 7-9, 2007, numerous fundamental factors, like value factors, performed extraordinarly p | quantopian_notebook_238.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_239.html |
| In [1]: bt = get_backtest('56e2d18ac8b52b0f4e1506ba') # Create all tear sheets bt.create_full_tear_sheet() 100% Ti | quantopian_notebook_240.html |
| In [1]: bt = get_backtest('56e2d18ac8b52b0f4e1506ba') # Create all tear sheets bt.create_full_tear_sheet() 100% Ti | quantopian_notebook_241.html |
| Researching & Developing a Market Neutral Strategy | quantopian_notebook_242.html |
| Domains | quantopian_notebook_243.html |
| Part 1: Introduction to Research Environment | quantopian_notebook_244.html |
| Futures API Introduction | quantopian_notebook_245.html |
| In [1]: #Imports from quantopian.pipeline import CustomFactor, CustomFilter, Pipeline from quantopian.research import | quantopian_notebook_246.html |
| Faster Fundamental Data - Overview & Performance Metrics | quantopian_notebook_247.html |
| In [1]: from quantopian.research import prices In [6]: llex_prices = prices( symbols('LLEX'), '01-01-2014', | quantopian_notebook_248.html |
| The best way to select a basket of ETFs is with the EquityMetadata dataset. In [1]: from quantopian.pipeline import | quantopian_notebook_249.html |
| In [1]: import pandas as pd import numpy as np import matplotlib.cm as cm import matplotlib.pyplot as plt from datetim | quantopian_notebook_250.html |
| RBICS Focus | quantopian_notebook_251.html |
| Researching & Developing a Market Neutral Strategy | quantopian_notebook_252.html |
| Enter your backtest ID. | quantopian_notebook_253.html |
| FactSet Fundamentals Example | quantopian_notebook_254.html |
| "Premium" Dataset | quantopian_notebook_255.html |
| Japan challenge submission template | quantopian_notebook_256.html |
| In [1]: import pandas as pd from datetime import datetime, timedelta import numpy as np import matplotlib.pyplot as pl | quantopian_notebook_257.html |
| Equity Metadata | quantopian_notebook_258.html |
| In [1]: from quantopian.research import get_pricing #from quantopian.pipeline.filters import Q1500US from quantopian.p | quantopian_notebook_259.html |
| In [21]: import numpy as np import cvxopt as opt from cvxopt import blas, solvers import pandas as pd import matplotli | quantopian_notebook_260.html |
| CHS Model (RISK OF FINANCIAL DISTRESS) | quantopian_notebook_261.html |
| Testing different methods of EV/EBITDA | quantopian_notebook_262.html |
| In [1]: # Get backtest object bt = get_backtest('5847c731c9cbc264e999216e') # Create all tear sheets bt.create_full_te | quantopian_notebook_263.html |
| Alphalens Example Tear Sheet | quantopian_notebook_264.html |
| Replication on Morningstar Financial Health Grade | quantopian_notebook_265.html |
| Dynamic Efficient Asset Allocation Strategy | quantopian_notebook_266.html |
| Dynamic Efficient Asset Allocation Strategy | quantopian_notebook_267.html |
| In [1]: bt = get_backtest('58ac2a44d446705dfac76552') 100% Time: 0:00:04|######################################### | quantopian_notebook_268.html |
| Engineered Momentum Strategy | quantopian_notebook_269.html |
| In [3]: import pandas as pd import matplotlib.pyplot as plt def engineered_momentum(end, start, sp500, world_small, tr | quantopian_notebook_270.html |
| In [4]: """ Rotate between S&P 500, mid-cap value, small cap international, emerging markets, and intermediate treasur | quantopian_notebook_271.html |
| In [4]: """ Rotate between S&P 500, mid-cap value, small cap international, emerging markets, and intermediate treasur | quantopian_notebook_272.html |
| Dataframe of patterns and outcomes: | quantopian_notebook_273.html |
| In [9]: import talib import matplotlib.pyplot as pyplot import pandas as pd In [10]: # Get year-to-date closing p | quantopian_notebook_274.html |
| Plotting | quantopian_notebook_275.html |
| In [3]: # Step One: The Setup In [11]: """ This cell is going to create the basic framework of the algorithm """ | quantopian_notebook_276.html |
| In [1]: bt = get_backtest('568dd8df0c1d760d2fa71dee') 100% Time: 0:00:12|######################################### | quantopian_notebook_277.html |
| Import packages, data and modules In [77]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data im | quantopian_notebook_278.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_279.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_280.html |
| In [5]: bt = get_backtest('5967d9a6a1deeb5664209246') bt.create_full_tear_sheet() 100% Time: 0:00:01|############ | quantopian_notebook_281.html |
| In [1]: import pandas as pd from pandas import Timedelta as td import numpy as np import scipy.stats as stats import m | quantopian_notebook_282.html |
| .. 1) done | quantopian_notebook_283.html |
| Machine Learning - Classifier comparison on close_price | quantopian_notebook_284.html |
| Initial Screen | quantopian_notebook_285.html |
| Dual Momentum | quantopian_notebook_286.html |
| Predicting Volatility | quantopian_notebook_287.html |
| Comparing Diversification Techniques to Hierarchical Risk Parity | quantopian_notebook_288.html |
| In [30]: import pandas as pd import numpy as np import talib import matplotlib.pyplot as plt In [117]: dfPrices = | quantopian_notebook_289.html |
| First Problem, the price doesn't match | quantopian_notebook_290.html |
| Alpha research - Quality companies | quantopian_notebook_291.html |
| Stochastics Foundations with Python | quantopian_notebook_292.html |
| In [1]: # Thanh Duong 2018.02.07 # www.quantopian.com/posts/k-means-clustering-help # Nick Lupica 2018.02.07 # www.qua | quantopian_notebook_293.html |
| Mean Reversion on Futures | quantopian_notebook_294.html |
| Generalized Method of Moments with ARCH and GARCH Models | quantopian_notebook_295.html |
| Exercises: Introduction to Pairs Trading - Answer Key | quantopian_notebook_296.html |
| Exercises: Introduction to Pairs Trading - Answer Key | quantopian_notebook_297.html |
| Common Risk Factor Performance | quantopian_notebook_298.html |
| Ranking Universes by Factors | quantopian_notebook_299.html |
| Kalman Filters | quantopian_notebook_300.html |
| Enter your backtest ID. | quantopian_notebook_301.html |
| Fundamental factor models | quantopian_notebook_302.html |
| Hypothesis Testing | quantopian_notebook_303.html |
| Factor Analysis | quantopian_notebook_304.html |
| Multiple Linear Regression | quantopian_notebook_305.html |
| Conclusion/How to Constrain Risk | quantopian_notebook_306.html |
| Introduction to pandas | quantopian_notebook_307.html |
| Factor Manipulation Using Numpy Arrays | quantopian_notebook_308.html |
| In [13]: # import pipeline stuff from quantopian.pipeline import CustomFactor, Pipeline from quantopian.research impor | quantopian_notebook_309.html |
| # Works but outputs do not correspond to my calculations of gaps. Figure out if the CustomFactor args are wrong # (e.g. | quantopian_notebook_310.html |
| Pipeline Example Using Market Cap and Sentiment | quantopian_notebook_311.html |
| Rolling mean with get_pricing example | quantopian_notebook_312.html |
| Example returns factor | quantopian_notebook_313.html |
| Getting Fundamentals Example | quantopian_notebook_314.html |
| Run the cell below to create your tear sheet, or return to your algorithm. In [ ]: bt = get_backtest('5a2c4061605f5 | quantopian_notebook_315.html |
| MaxHigh Custom Factor | quantopian_notebook_316.html |
| CAGR Custom Factor | quantopian_notebook_317.html |
| Price correlation vs return correlation | quantopian_notebook_318.html |
| Rank of Last Close Price vs Previous Prices | quantopian_notebook_319.html |
| Fundamental Check Example | quantopian_notebook_320.html |
| Import packages, data and modules In [10]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data i | quantopian_notebook_321.html |
| Last EWMA Crossover Custom Factor | quantopian_notebook_322.html |
| Get Industry Codes Using Pipeline | quantopian_notebook_323.html |
| In [1]: from quantopian.pipeline import Pipeline, CustomFilter from quantopian.research import run_pipeline from quant | quantopian_notebook_324.html |
| Plot XIV 'bars to fill'Â | quantopian_notebook_325.html |
| get_pricing data example | quantopian_notebook_326.html |
| N Days Ago Custom Factor | quantopian_notebook_327.html |
| Output Universe | quantopian_notebook_328.html |
| get_pricing for specific minutes example | quantopian_notebook_329.html |
| get_pricing vs data.history methods | quantopian_notebook_330.html |
| Run the cell below to create your tear sheet, or return to your algorithm. In [1]: bt = get_backtest('5a2c43c9cc0e3 | quantopian_notebook_331.html |
| Getting Fundamentals Example | quantopian_notebook_332.html |
| Getting Fundamentals Example | quantopian_notebook_333.html |
| In [1]: from quantopian.research import run_pipeline from quantopian.pipeline import Pipeline from quantopian.pipeline | quantopian_notebook_334.html |
| In [1]: # def initialize(context): from quantopian.pipeline import Pipeline from quantopian.pipeline.data.builtin impo | quantopian_notebook_335.html |
| Fundamental Basic EPS Example | quantopian_notebook_336.html |
| Fundamental sector example | quantopian_notebook_337.html |
| Factor Tearsheet | quantopian_notebook_338.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_339.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_340.html |
| In [1]: bt = get_backtest('57d171e81cf5bc102ae5cc7a') bt.create_full_tear_sheet() 100% Time: 0:00:18|############# | quantopian_notebook_341.html |
| Searching for a signal in CEO change and news sentiment data | quantopian_notebook_342.html |
| Alpha Library | quantopian_notebook_343.html |
| The Q500US and Q1500US | quantopian_notebook_344.html |
| TradeableUS Methodology | quantopian_notebook_345.html |
| Q500US and Q1500US Market Cap Breakdown | quantopian_notebook_346.html |
| FORECASTING STOCK RETURNS WITH BIG DATA AND MACHINE LEARNING | quantopian_notebook_347.html |
| Constructing a Pipeline in Research | quantopian_notebook_348.html |
| Run the cell below to create your tear sheet. In [ ]: bt = get_backtest('5b05ccc356f61742c42b04a8') bt.create_full_ | quantopian_notebook_349.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data.factset import Fundamentals, EquityMet | quantopian_notebook_350.html |
| Portfolio Analysis using pyfolio | quantopian_notebook_351.html |
| In [6]: # bt = get_backtest('').create_perf_attrib_tear_sheet() bt = get_backtest('').create_perf_attrib_tear_sheet() | quantopian_notebook_352.html |
| Behavioral Arbitrage - Design Strategies That Time Market Mistakes | quantopian_notebook_353.html |
| $Alpha_{5}$ : $(rank((open - (sum(vwap, 10) / 10))) * (-1 * abs(rank((close - vwap)))))$ | quantopian_notebook_354.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_355.html |
| In [75]: import numpy as np import pandas as pd from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_356.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_357.html |
| How much does it hurt, when it hurts to hurt? | quantopian_notebook_358.html |
| In [3]: from quantopian.pipeline.data import Fundamentals from quantopian.pipeline.data import morningstar from quanto | quantopian_notebook_359.html |
| plot_candles: Candlestick Charts for Quantopian | quantopian_notebook_360.html |
| Factor Combination Theory and Tools | quantopian_notebook_361.html |
| QTradeableStocksUS | quantopian_notebook_362.html |
| 101 Alphas #2 with Parameter Optimization | quantopian_notebook_363.html |
| Volume Based Activity Bars Construction | quantopian_notebook_364.html |
| This is my first notebook. My initial aims for this notebook is to import the last one years stock data for AAPL and pl | quantopian_notebook_365.html |
| In [1]: from quantopian.research import run_pipeline from quantopian.pipeline import Pipeline from quantopian.pipeline | quantopian_notebook_366.html |
| In [10]: import pandas as pd import numpy as np import alphalens from quantopian.research import run_pipeline, local_c | quantopian_notebook_367.html |
| Bottom with Dynamic Exit | quantopian_notebook_368.html |
| Building the Foundations for Hypothesis Testing | quantopian_notebook_369.html |
| Quantpedia Trading Strategy Series: An Analysis on Cross-Sectional Mean Reversion Strategies | quantopian_notebook_370.html |
| In [1]: bt = get_backtest('581246405459b5124126d07d') bt.create_full_tear_sheet() 100% Time: 0:00:14|############# | quantopian_notebook_371.html |
| In [58]: stock = 'SPY' prices_daily = get_pricing( stock, # fields='close_price', start_date="2020-05-01", end_ | quantopian_notebook_372.html |
| Example Alpha Factors with Cointegrated Pairs | quantopian_notebook_373.html |
| Alphalens + Quantopian | How To | quantopian_notebook_374.html |
| In [1]: from quantopian.pipeline.data import factset from quantopian.pipeline import Pipeline from quantopian.research | quantopian_notebook_375.html |
| Main Project | quantopian_notebook_376.html |
| Risk Management | quantopian_notebook_377.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_378.html |
| Alpha Vertex Precog 500 Alpha Testing | quantopian_notebook_379.html |
| Using the Kalman Filter in Algorithmic Tradin | quantopian_notebook_380.html |
| In [13]: from quantopian.pipeline.factors import CustomFactor from quantopian.pipeline.filters import StaticAssets fro | quantopian_notebook_381.html |
| Psychsignal - StockTwits Trader Mood & Optimize API (Long/Short)Â | quantopian_notebook_382.html |
| The Estimize Signal | quantopian_notebook_383.html |
| Weekly security movement prediction using Machine Learning and Google Trends/Alternative Data | quantopian_notebook_384.html |
| Market Regime detetection using Hidden Markov Model or Anomaly Detection (OneClassSVM) | quantopian_notebook_385.html |
| Run the cell below to create your tear sheet, or return to your algorithm. In [1]: bt = get_backtest('59c2936403ac71 | quantopian_notebook_386.html |
| Implementation | quantopian_notebook_387.html |
| Applying Alpha Vertext Machine Learning to a Mean-Reversion Strategy | quantopian_notebook_388.html |
| Strategy | quantopian_notebook_389.html |
| In [3]: # Get backtest object bt = get_backtest('588e7fe79c134c5e1ad0369c') # Create all tear sheets bt.create_full_te | quantopian_notebook_390.html |
| In [15]: # Import statements from quantopian.research import run_pipeline from quantopian.pipeline import CustomFactor | quantopian_notebook_391.html |
| In [7]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data import EquityPricing, factset from qu | quantopian_notebook_392.html |
| BENCHMARK: MARKET PERFORMANCE | quantopian_notebook_393.html |
| BENCHMARK: MARKET PERFORMANCE | quantopian_notebook_394.html |
| In [6]: import pyfolio as pf import matplotlib.pyplot as plt plotAlgo, = plt.plot(pf.timeseries.cum_returns(get_backte | quantopian_notebook_395.html |
| In [20]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline,symbols from quantopian | quantopian_notebook_396.html |
| In [1]: from quantopian.pipeline import CustomFactor, Pipeline from quantopian.research import run_pipeline from quant | quantopian_notebook_397.html |
| In [107]: # def initialize(context): from quantopian.pipeline import Pipeline from quantopian.pipeline.data.builtin im | quantopian_notebook_398.html |
| Alpha Vertex Precog 500 Alpha Testing | quantopian_notebook_399.html |
| Import all of the needed classes into code In [28]: from quantopian.research import run_pipeline, returns from quan | quantopian_notebook_400.html |
| In [5]: import numpy, datetime from quantopian.research import run_pipeline from quantopian.pipeline import Pipeline, | quantopian_notebook_401.html |
| Exercises: Introduction to Pairs Trading | quantopian_notebook_402.html |
| Introduction to Pairs Trading | quantopian_notebook_403.html |
| Run the cell below to create your tear sheet, or return to your algorithm. In [ ]: bt = get_backtest('5a0b95c9eedb1b | quantopian_notebook_404.html |
| 1. Quantopian and Alphalens Processing | quantopian_notebook_405.html |
| 1. Quantopian and Alphalens Processing | quantopian_notebook_406.html |
| Run the cell below to create your tear sheet. In [1]: import numpy as np import matplotlib.pyplot as plt import pand | quantopian_notebook_407.html |
| In [21]: from random import SystemRandom class RingBuffer: def __init__(self, size): self.data = [None for i in | quantopian_notebook_408.html |
| Alphalens + Quantopian | How To | quantopian_notebook_409.html |
| In [21]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipelin | quantopian_notebook_410.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_411.html |
| Exploring Market Trends: Seasonality | quantopian_notebook_412.html |
| In [8]: import pandas as pd from zipline import TradingAlgorithm from zipline.api import order, sid import matplotlib. | quantopian_notebook_413.html |
| Load backtests | quantopian_notebook_414.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_415.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_416.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_417.html |
| An updated method to analyze alpha factors | quantopian_notebook_418.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_419.html |
| Null Hypothesis: If short-term interest rates increase, then stock market returns will be unchanged or less volatile. | quantopian_notebook_420.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_421.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_422.html |
| Performance Attribution | quantopian_notebook_423.html |
| In [1]: import numpy as np import pandas as pd from quantopian.research import run_pipeline from quantopian.pipeline i | quantopian_notebook_424.html |
| In [23]: from quantopian.pipeline.factors import AverageDollarVolume, RSI, SimpleMovingAverage, CustomFactor, Bollinge | quantopian_notebook_425.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_426.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_427.html |
| In [1]: bt=get_backtest("59a46e3dee59a9510a4a6179") 100% Time: 0:00:00|########################################### | quantopian_notebook_428.html |
| Linear Regression | quantopian_notebook_429.html |
| Tackling overfitting via cross-validation over quarters | quantopian_notebook_430.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_431.html |
| In [20]: from sklearn.preprocessing import MinMaxScaler import numpy as np import pandas as pd from quantopian.pipelin | quantopian_notebook_432.html |
| Analyzing a Signal and Creating a Contest Algorithm with Self-Serve Data | quantopian_notebook_433.html |
| In [64]: from quantopian.research import run_pipeline from quantopian.pipeline import Pipeline from quantopian.pipelin | quantopian_notebook_434.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_435.html |
| In [63]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipelin | quantopian_notebook_436.html |
| In [20]: import numpy as np from quantopian.pipeline import Pipeline from quantopian.pipeline import CustomFactor from | quantopian_notebook_437.html |
| In [1]: from quantopian.pipeline.data import Fundamentals from quantopian.pipeline.data import morningstar from quanto | quantopian_notebook_438.html |
| In [82]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data.alpha_vertex import ( # Top 100 Secu | quantopian_notebook_439.html |
| Factor Risk Exposure | quantopian_notebook_440.html |
| An updated method to analyze alpha factors | quantopian_notebook_441.html |
| In [6]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data.factset.ownership import Form3Aggregate | quantopian_notebook_442.html |
| In [6]: import quantopian.optimize as opt from quantopian.research import run_pipeline from quantopian.pipeline import | quantopian_notebook_443.html |
| In [132]: from time import time from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline | quantopian_notebook_444.html |
| Model Misspecification | quantopian_notebook_445.html |
| Self-Serve Data - How does it work? | quantopian_notebook_446.html |
| In [1]: from quantopian.interactive.data.quandl import fred_gdp as dataset from quantopian.interactive.data.quandl imp | quantopian_notebook_447.html |
| Introduction to Self-Serve Data | quantopian_notebook_448.html |
| EMA Weekly Calculations | quantopian_notebook_449.html |
| In [9]: # Imports import numpy as np import pandas as pd import matplotlib.pyplot as plt from quantopian.pipeline impo | quantopian_notebook_450.html |
| Comparing Diversification Techniques to Hierarchical Risk Parity | quantopian_notebook_451.html |
| quantopian_notebook_452.html | |
| In [2]: from quantopian.pipeline import Pipeline, CustomFactor from quantopian.research import run_pipeline from quant | quantopian_notebook_453.html |
| In [5]: from quantopian.pipeline.data import Fundamentals from quantopian.pipeline import Pipeline from quantopian.res | quantopian_notebook_454.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_455.html |
| In [2]: import pandas as pd import numpy as np import matplotlib.pyplot as plt from pykalman import KalmanFilter from | quantopian_notebook_456.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data import USEquityPricing from quantopian. | quantopian_notebook_457.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data import USEquityPricing from quantopian. | quantopian_notebook_458.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data import USEquityPricing from quantopian. | quantopian_notebook_459.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_460.html |
| In [ ]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_461.html |
| In [1]: from quantopian.pipeline import Pipeline In [2]: from quantopian.pipeline.data.builtin import USEquityPri | quantopian_notebook_462.html |
| Test Strategy | quantopian_notebook_463.html |
| In [11]: from quantopian.pipeline import Pipeline, CustomFactor from quantopian.research import run_pipeline from quan | quantopian_notebook_464.html |
| International Factor Research - Alphalens Example | quantopian_notebook_465.html |
| In [1]: import pandas as pd import numpy as np from quantopian.pipeline import Pipeline, CustomFactor from quantopian. | quantopian_notebook_466.html |
| Notebook to plot the price and drawdown of a particular stock | quantopian_notebook_467.html |
| In [1]: from quantopian.research.experimental import continuous_future, history from quantopian.pipeline import Pipeli | quantopian_notebook_468.html |
| In [43]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipelin | quantopian_notebook_469.html |
| In [1]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_470.html |
| 1. Get Data from Data Upload | quantopian_notebook_471.html |
| In [2]: import pandas as pd In [1]: bt = get_backtest('<your_backtest_id_here>') orders = bt.orders 100% Time: | quantopian_notebook_472.html |
| Analyzing Alpha in 10-Ks and 10-Qs (Alphalens Study) | quantopian_notebook_473.html |
| Scraping 10-Ks and 10-Qs for Alpha (Data Cleaning) | quantopian_notebook_474.html |
| 1. Easy-to-understand example | quantopian_notebook_475.html |
| 2. Similar idea, cleaner code | quantopian_notebook_476.html |
| 2: Financial Data Structures | quantopian_notebook_477.html |
| 2: Financial Data Structures | quantopian_notebook_478.html |
| Labeling Data for Financial Machine Learning | quantopian_notebook_479.html |
| In [7]: from quantopian.pipeline import Pipeline,CustomFactor from quantopian.research import run_pipeline from quanto | quantopian_notebook_480.html |
| Exercise 2.1 | quantopian_notebook_481.html |
| In [ ]: """ This is an algorithm.... copy past the code to a new algorithm to test it.... """ import quantopian.algori | quantopian_notebook_482.html |
| In [ ]: # This is a simple trading algorithm that buy low sell high # Algorithm: # 1. Use 27 stock context # 2. For | quantopian_notebook_483.html |
| In [8]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_484.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_485.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_486.html |
| In [345]: import alphalens Z = W[:-1].copy() # W are the factors Z /= np.tile(np.sum(np.abs(Z), axis=1), (Z.shape[1], | quantopian_notebook_487.html |
| In [2]: from quantopian.pipeline import Pipeline, CustomFactor from quantopian.pipeline.data import EquityPricing, fac | quantopian_notebook_488.html |
| Smooth PCA | quantopian_notebook_489.html |
| In [7]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_490.html |
| Define Helper Functions | quantopian_notebook_491.html |
| International Factor Research - Alphalens Example | quantopian_notebook_492.html |
| International Factor Research - Alphalens Example | quantopian_notebook_493.html |
| In [39]: import pandas as pd import numpy as np from quantopian.pipeline import Pipeline, CustomFactor from quantopian | quantopian_notebook_494.html |
| Topics Course Homework 2 - Overnight Returns | quantopian_notebook_495.html |
| In [1]: from quantopian.research import prices, symbols from quantopian.pipeline.filters import Q1500US import numpy a | quantopian_notebook_496.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_497.html |
| In [1]: # Import Pipeline from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline # Impo | quantopian_notebook_498.html |
| In [23]: # Imports the pipeline which will let us filter down our universe of potential stocks. from quantopian.pipeli | quantopian_notebook_499.html |
| Get a list of symbols | quantopian_notebook_500.html |
| In [1]: import numpy as np import pandas as pd import time from quantopian.pipeline import Pipeline import quantopian. | quantopian_notebook_501.html |
| In [97]: import numpy as np import pandas as pd import time from quantopian.pipeline import Pipeline import quantopian | quantopian_notebook_502.html |
| In [17]: from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline, volumes, prices from q | quantopian_notebook_503.html |
| In [ ]: pip install arch In [ ]: pip install -U statsmodels In [ ]: pip install datapackage In [ ]: pip in | quantopian_notebook_504.html |
| Lesson3-Pipeline API | quantopian_notebook_505.html |
| "Infographics Challenge, Economic Implications of COVID-19" First submission notebook Lucas BL In [136]: import pand | quantopian_notebook_506.html |
| In [66]: import numpy as np from matplotlib import pyplot as plt import pandas as pd In [67]: df = get_pricing(sym | quantopian_notebook_507.html |
| In [1]: from quantopian.interactive.data.sentdex import sentiment In [2]: from quantopian.pipeline.filters.mornin | quantopian_notebook_508.html |
| 21 day returns of a given factor | quantopian_notebook_509.html |
| Fundamentals exchange_id test | quantopian_notebook_510.html |
| Notice that pipeline returns a multi-index dataframe. | quantopian_notebook_511.html |
| Last close above SMA for n consecutive days | quantopian_notebook_512.html |
| CloseOnN Custom Factor | quantopian_notebook_513.html |
| Plotting | quantopian_notebook_514.html |
| Lesson3-Pipeline API | quantopian_notebook_515.html |
| Talib example | quantopian_notebook_516.html |
| International Factor Research - Alphalens Example | quantopian_notebook_517.html |
| Results for timestamp 1 - Min Max Scaled | quantopian_notebook_518.html |
| Example of a custom factor using fundamental data | quantopian_notebook_519.html |
| TTM Custom Factor Test | quantopian_notebook_520.html |
| Recursive factors - Three ways to do EWMA | quantopian_notebook_521.html |
| In [1]: from quantopian.pipeline.data import Fundamentals from quantopian.pipeline import Pipeline from quantopian.res | quantopian_notebook_522.html |
| Those are the basics to plotting pandas dataframes. | quantopian_notebook_523.html |
| Non-US Factor Example | quantopian_notebook_524.html |
| Get a stock symbol from an asset | quantopian_notebook_525.html |
| In [20]: # Import modules needed for pipeline from quantopian.research import run_pipeline from quantopian.pipeline im | quantopian_notebook_526.html |
| Getting the linear regression terms including R Squared | quantopian_notebook_527.html |
| Notebook to plot the price and drawdown of a particular stock | quantopian_notebook_528.html |
| In [60]: import pandas as pd In [61]: hp = pd.DataFrame(data=[['1-1-2020', 1, 'one'], ['2-1-2020', 2, | quantopian_notebook_529.html |
| Scoring based upon multiple conditions | quantopian_notebook_530.html |
| Example Pipeline and Slicing by Dates | quantopian_notebook_531.html |
| In [ ]: # Define a stock we want to look at along with some dates stock = 'AAPL' start_date = '1-1-2020' end_date = '1 | quantopian_notebook_532.html |
| In [1]: from quantopian.pipeline.data import factset from quantopian.pipeline import Pipeline from quantopian.research | quantopian_notebook_533.html |
| In [1]: # Import Pipeline from quantopian.pipeline import Pipeline from quantopian.research import run_pipeline # Impo | quantopian_notebook_534.html |
| Performance Relative to Common Risk Factors | quantopian_notebook_535.html |
| In this notebook, we make use of the peer_count Classifier method to identify Factset RBICS Focus subsectors with low a | quantopian_notebook_536.html |
| ADX - Custom factor for pipeline (WIP) In [1]: from quantopian.pipeline import Pipeline from quantopian.pipeline.dat | quantopian_notebook_537.html |
| In [4]: spy = get_pricing('spy', start_date='2019-01-02', end_date='2019-09-25', frequency='daily') iwm = get_pricing( | quantopian_notebook_538.html |
| wti oil In [1]: from quantopian.research.experimental import history import pandas as pd import matplotlib.pyplot as | quantopian_notebook_539.html |
| In [187]: import numpy as np import pandas as pd import statsmodels.api as sm from statsmodels import regression impor | quantopian_notebook_540.html |
| In [ ]: import numpy as np from pandas.tseries.offsets import CustomBusinessDay from scipy.stats import mode def compu | quantopian_notebook_541.html |
| Factor Analysis | quantopian_notebook_542.html |
| In [3]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data.morningstar import Fundamentals from qu | quantopian_notebook_543.html |
| In [71]: #Custom factor for Sector PE from quantopian.pipeline import CustomFactor class SectorPE(CustomFactor): # D | quantopian_notebook_544.html |
| In [67]: import numpy as np import pandas as pd import matplotlib.pyplot as plt from quantopian.research import prices | quantopian_notebook_545.html |
| In [39]: import numpy as np import pandas as pd from quantopian.research import run_pipeline from quantopian.pipeline | quantopian_notebook_546.html |
| In [1]: import numpy as np import matplotlib.pyplot as plt import pandas as pd from statsmodels.stats.stattools import | quantopian_notebook_547.html |
| In [2]: import pandas as pd import numpy as np # Pipeline imports from quantopian.pipeline import CustomFactor, Custom | quantopian_notebook_548.html |
| In [1]: import pandas as pd import numpy as np # Pipeline imports from quantopian.pipeline import CustomFactor, Custom | quantopian_notebook_549.html |
| Define Helper Functions | quantopian_notebook_550.html |
| Fundamentals | quantopian_notebook_551.html |
| Sentiment trading strategy | quantopian_notebook_552.html |
| In [15]: from quantopian.pipeline import CustomFactor from quantopian.pipeline import Pipeline from quantopian.pipelin | quantopian_notebook_553.html |
| In [10]: #Important Python Modules import numpy as np import pandas as pd from scipy import stats # Pipeline essentia | quantopian_notebook_554.html |
| Creating Tear Sheets With Alphalens | quantopian_notebook_555.html |
| In [9]: # Research environment pipeline imports from quantopian.pipeline import Pipeline from quantopian.research impo | quantopian_notebook_556.html |
| In [1]: from quantopian.interactive.data.sentdex import sentiment In [2]: from quantopian.pipeline.filters.morning | quantopian_notebook_557.html |
| In [9]: from quantopian.pipeline import Pipeline from quantopian.pipeline.data import EquityPricing, factset from qu | quantopian_notebook_558.html |
| In [1]: from quantopian.pipeline.factors import Returns from quantopian.pipeline import Pipeline, CustomFactor from qu | quantopian_notebook_559.html |
| In [ ]: # From: https://www.quantopian.com/posts/long-only-non-day-trading-algorithm-for-live # PYLIVETRADER # from qu | quantopian_notebook_560.html |
| In [3]: from quantopian.pipeline import Pipeline In [4]: from quantopian.research import run_pipeline In [5]: | quantopian_notebook_561.html |