Just having some fun, thought I would share.

Inspiration from http://pro.moneymappress.com/MMRWND129/PMMRRB22/?iris=429781&ad=btgt4c-g21kmmis-h1&h=true

(warning: its one of those "very long, cant fast-forward" marketing videos)

Combo of

https://www.quantopian.com/posts/quantopian-tutorial-lesson-3-basic-fundamentals-with-piotroski-score-growth-stocks-and-uptrending-volatile-small-cap-algorithms

and

https://www.quantopian.com/posts/algorithm-with-aroon-from-ta-lib

Clone Algorithm

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Cumulative performance:

Algorithm
Benchmark

Custom data:

Total Returns

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Alpha

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Beta

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Sharpe

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Sortino

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Max Drawdown

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Benchmark Returns

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Volatility

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Returns | 1 Month | 3 Month | 6 Month | 12 Month |

Alpha | 1 Month | 3 Month | 6 Month | 12 Month |

Beta | 1 Month | 3 Month | 6 Month | 12 Month |

Sharpe | 1 Month | 3 Month | 6 Month | 12 Month |

Sortino | 1 Month | 3 Month | 6 Month | 12 Month |

Volatility | 1 Month | 3 Month | 6 Month | 12 Month |

Max Drawdown | 1 Month | 3 Month | 6 Month | 12 Month |

""" Creating an algorithm based off the Piotroski Score index which is based off of a score (0-9) Each of the following marks (-) satisfied means one point. And in the end we'll long the stocks with a score of >= 8 Profitability - Positive ROA - Positive Operating Cash Flow - Higher ROA in current year versus last year - Cash flow from operations > ROA of current year Leverage - Current ratio of long term debt < last year's ratio of long term debt - Current year's current_ratio > last year's current_ratio - No new shares issued this year Operating Efficiency - Higher gross margin compared to previous year - Higher asset turnover ratio compared to previous year This algorithm demonstrates how to grasp historical fundamental data by storing it in a Pandas Panel similar to how the Quantopian 'data' is currently structured """ import pytz from datetime import datetime, timedelta import talib import pandas as pd """ Initialize and Handle Data """ def initialize(context): #: context.days holds the number of days that we've had this algorithm context.days = 0 #: context.fundamental_dict holds the date:dictionary reference that we need context.fundamental_dict = {} #: context.fundamental_data holds the pandas Panel that's derived from the fundamental_dict context.fundamental_data = None def before_trading_start(context): """ Called before the start of each trading day (handle_data) and updates our universe with the securities and values found from fetch_fundamentals """ #: Reference fundamentals in a shorter variable f = fundamentals #: Query for the data that we need from fundamentals fundamental_df = get_fundamentals(query( f.valuation.market_cap, f.operation_ratios.roa, f.cash_flow_statement.operating_cash_flow, f.cash_flow_statement.cash_flow_from_continuing_operating_activities, f.operation_ratios.long_term_debt_equity_ratio, f.operation_ratios.current_ratio, f.valuation.shares_outstanding, f.operation_ratios.gross_margin, f.operation_ratios.assets_turnover, ) .order_by(fundamentals.valuation.market_cap.desc()) .limit(490) ) #: Set our fundamentals into a context variable context.fundamental_df = fundamental_df #: Update our universe with the values update_universe(fundamental_df.columns.values) def handle_data(context, data): #: Only run every 25 trading days if context.days % 25 == 0: #: Insert a new dataframe into our dictionary context.fundamental_dict[get_datetime()] = context.fundamental_df #: If it's greater than the first trading day if context.days > 0: context.fundamental_data = pd.Panel(context.fundamental_dict) scores = get_piotroski_scores(context.fundamental_data, get_datetime()) #: Only rebalance when we have enough data if scores != None: rebalance(context, data, scores) #: Log our current positions if (context.days - 1) % 25 == 0: #: Portfolio position string portfolio_string = "Current positions: " #: Don't log if we have no positions if len(context.portfolio.positions) != 0: #: Add our current positions to a string for pos in context.portfolio.positions: portfolio_string += "Symbol: %s and Amount: %s, " % (pos.symbol, context.portfolio.positions[pos].amount) #: Log all our portfolios log.info(portfolio_string) context.days += 1 """ Defining our rebalance method """ def rebalance(context, data, scores): highPrices = history(101, '1d', 'high') lowPrices = history(101, '1d', 'low') """ This method takes in the scores found by get_piotroski_scores and orders our portfolio accordingly """ #: Find which stocks we need to long and which ones we need to short num_long = [stock for stock in scores if scores[stock] >= 7] num_short = [stock for stock in scores if scores[stock] <= 2] #: Stocks to long for stock in num_long: Aroon = talib.AROON(highPrices[stock], lowPrices[stock], timeperiod = 100) AroonDown = Aroon[0][-1] AroonUp = Aroon[1][-1] record(AroonUp=AroonUp, AroonDown=AroonDown) if stock in data and AroonUp > AroonDown: log.info("Going long on stock %s with score %s" % (stock.symbol, scores[stock])) order_target_percent(stock, 2.0/len(num_long)) # #: Stocks to short # for stock in num_short: # if stock in data: # log.info("Going short on stock %s with score %s" % (stock.symbol, scores[stock])) # order_target_percent(stock, -1.0/len(num_short)) #: Exit any positions we might have for stock in context.portfolio.positions: if stock in data and (stock not in num_long or AroonUp < AroonDown) and stock not in num_short: log.info("Exiting our positions on %s" % (stock.symbol)) order_target_percent(stock, 0) record(number_long=len(num_long)) # record(number_short=len(num_short)) """ Defining our methods for the piotroski score """ def get_piotroski_scores(fundamental_data, current_date): """ This method finds the dataframe that contains the data for the time period we want and finds the total Piotroski score for those dates """ all_scores = {} all_dates = fundamental_data.items utc = pytz.UTC last_year = utc.localize(datetime(year=current_date.year - 1, month = current_date.month, day = current_date.day)) #: If one year hasn't passed just return None if last_year < min(all_dates): return None #: Figure out which date to use for i, date in enumerate(all_dates): if i == len(all_dates) - 1: continue if last_year > date and last_year < all_dates[i + 1]: break elif last_year == date: break #: This is pretty robust so just set last_year to whatever date currently is when you broke #: or ended the for loop last_year = date old_data = fundamental_data[last_year] current_data = fundamental_data[current_date] #: Find the score for each security for stock in current_data: profit = profit_logic(current_data, old_data, stock) leverage = leverage_logic(current_data, old_data, stock) operating = operating_logic(current_data, old_data, stock) total_score = profit + leverage + operating all_scores[stock] = total_score return all_scores def profit_logic(current_data, old_data, sid): """ Define our profitability logic here """ #: Positive ROA positive_roa = current_data[sid]['roa'] > 0 #: Positive Operating Cash Flow positive_ocf = current_data[sid]['operating_cash_flow'] > 0 #: Current ROA > Last Year ROA current_last_roa = current_data[sid]['roa'] > old_data[sid]['roa'] #: Cash flow from operations > ROA cash_flow_roa = current_data[sid]['cash_flow_from_continuing_operating_activities'] > current_data[sid]['roa'] return int(positive_roa) + int(positive_ocf) + int(current_last_roa)+ int(cash_flow_roa) def leverage_logic(current_data, old_data, sid): """ Define our leverage logic here """ #: Current ratio of long-term debt < last year's ratio of long-term debt long_term_debt = current_data[sid]['long_term_debt_equity_ratio'] > old_data[sid]['long_term_debt_equity_ratio'] #: Current year's current_ratio > last year's current_ratio current_ratio = current_data[sid]['current_ratio'] > old_data[sid]['current_ratio'] #: No new shares new_shares = current_data[sid]['shares_outstanding'] <= old_data[sid]['shares_outstanding'] return int(long_term_debt) + int(current_ratio) + int(new_shares) def operating_logic(current_data, old_data, sid): """ Define our operating efficiency logic here """ #: Higher gross margin compared to previous year gross_margin = current_data[sid]['gross_margin'] > old_data[sid]['gross_margin'] #: Higher asset turnover ratio compared to previous year asset_turnover = current_data[sid]['assets_turnover'] > old_data[sid]['assets_turnover'] return int(gross_margin) + int(asset_turnover)

We have migrated this algorithm to work with a new version of the Quantopian API. The code is different than the original version, but the investment rationale of the algorithm has not changed. We've put everything you need to know here on one page.