2/16/2016 This tutorial is out-dated, please view the new versions here: https://www.quantopian.com/posts/quantopian-tutorials
Hey all,
This is the third lesson in our Quantopian Tutorial Series. In this one, we covered:
The Basics of Fundamentals:
- before_trading_start, get_fundamentals, and update_universe
Advanced Concepts:
- Universe Size
Three different algorithms:
- Naoki Nagai's Growth Ranking Strategy
- Richard Prokopyshen's Uptrending Volatile Small Cap Strategy
- The Piotroski Score by yours truly
HELPFUL LINKS:
- The PDF of the tutorial outline can be found here
- The Video version of the tutorial can be found here
QUANTOPIAN TUTORIAL SERIES:
- Basics of the IDE
- Basics of Fetcher and Security Universe with Jessica Stauth's Portfolio Rebalancing Algorithm
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
| 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 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,
)
.filter(fundamentals.valuation.market_cap != None)
.filter(fundamentals.operation_ratios.roa != None)
.filter(fundamentals.cash_flow_statement.operating_cash_flow != None)
.filter(fundamentals.cash_flow_statement.cash_flow_from_continuing_operating_activities != None)
.filter(fundamentals.operation_ratios.long_term_debt_equity_ratio != None)
.filter(fundamentals.valuation.shares_outstanding != None)
.filter(fundamentals.operation_ratios.current_ratio != None)
.filter(fundamentals.valuation.shares_outstanding != None)
.order_by(fundamentals.valuation.market_cap.desc())
.limit(40)
)
#: 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):
"""
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] >= 8]
num_short = [stock for stock in scores if scores[stock] <= 2]
#: Stocks to long
for stock in num_long:
if stock in data:
log.info("Going long on stock %s with score %s" % (stock.symbol, scores[stock]))
order_target_percent(stock, 1.0/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 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.
