The Cup With Handle pattern, developed by William O’Neil, is a technical indicator for identifying the continuation of a trend after a period of consolidation. [1] It consists of an initial uptrend that’s ideally not too mature, a U-shaped move (cup), followed by another sharp and minor shake out (handle). The price, after a rally, starts to consolidate with a smooth slope but then bounces back to the previous highs as it faces support at lower price levels.
When previous highs are touched, investors who bought shares before consolidation and other less committed investors sell their shares, pushing the price down for one last time. Eventually, the price reverses from a second support level (above the previous one) and breaks out of the resistance. Traders use different rules to identify Cup With Handle patterns and gauge their strength, but the base usually lasts 6–65 weeks with depths ranging from 8% to 50%. When trading Cup With Handles, the profit target is usually 20–25% above the initial resistance (pivot point), and the stop-loss range is 5–8% below that line. [2] image info
Pattern Recognition, part of the IBD MarketSmith’s premium trading toolkit, identifies seven different chart patterns in daily and weekly time periods: Cup and Cup With Handle, Saucer and Saucer With Handle, Double Bottom, Flat Base, Ascending Base, Consolidation, and IPO Base. This article will focus on using Pattern Recognition API to identify and trade Cup With Handle patterns. To find more information about other properties of Pattern Recognition, check its user manual. image info
A basic understanding of Python is needed to get the most out of the article. We’ll use pydantic to validate and serialize data, zipline-reloaded and pyfolio to backtest the strategy, pandas to load and access data, python-dotenv to read environment variables, yfinance to fetch benchmark price data, and requests to make API calls. A premium MarketSmith account is required to access Pattern Recognition. Symbols data and a list of Dow Jones Industrial Average (DJIA) constituents will be fetched from Financial Modeling Prep (FMP) v3 API. To retrieve the historical price data of the constituents, you need to ingest a zipline data bundle.
Please make sure to use the following versions:
- python 3.6.12
- pyfolio 0.8.0
- pandas 0.22.0
- matplotlib 3.0.3
- numpy 1.19.5 Alternatively, you need to follow this answer and update a line at pyfolio source code to make it work with the latest stack.
With a free FMP account, we can access the list of DJIA names from this endpoint. First of all, create src/price/endpoints.py to store the FMP endpoints.
# src/price/endpoints.py
DJIA_CONSTITUENTS = "https://financialmodelingprep.com/api/v3/dowjones_constituent"
NASDAQ100_CONSTITUENTS = "https://financialmodelingprep.com/api/v3/nasdaq_constituent"
Define Constituent model to serialize data received from FMP API.
# src/models/constituent.py
from typing import Union
from pydantic import BaseModel
class Constituent(BaseModel):
"""Represents a ticker received from FMP API when retrieving constituents of an index; see `price.load_tickers` method."""
symbol: str
name: str
sector: str
subSector: str
headQuarter: Union[str, None]
dateFirstAdded: str
cik: Union[str, None]
founded: Union[str, None]
Define load_tickers to fetch and store data.
# src/price/ticker.py
import os
import csv
from typing import List
import requests
from dotenv import load_dotenv
from pydantic import parse_obj_as
from src.price.endpoints import NASDAQ100_CONSTITUENTS, DJIA_CONSTITUENTS
from src.models import Constituent
load_dotenv()
def load_tickers(endpoint: str, api_key: str = os.environ["FMP_API_KEY"]) -> None:
"""Fetches and loads list of tickers to `data/ticker.csv` file. Uses FMP API to get the latest data and requires `FMP_API_KEY` env variable to be set. Fetches the data from the passed endpoint."""
params = {"apikey": api_key}
res = requests.get(endpoint, params=params)
res = res.json()
# parse and validate data
tickers = parse_obj_as(List[Constituent], res)
# write data to file
tickers = [constituent.dict() for constituent in tickers]
keys = tickers[0].keys()
with open("data/tickers.csv", 'w', newline='') as output_file:
dict_writer = csv.DictWriter(output_file, keys)
dict_writer.writeheader()
dict_writer.writerows(tickers)
if __name__ == "__main__":
load_tickers(DJIA_CONSTITUENTS)
We first load FMP_API_KEY environment variable, pass it to the endpoint defined in constants.py and convert the response to a dictionary by calling the .json() method. We then use Pydantic’s parse_obj_as utility method to serialize response into a list of Constituent instances. In the end, the data is converted back to a list of dictionaries to be stored in data/tickers.csv .
Make sure to store theFMP_API_KEY key in the .env file and set it to the key you received from the FMP dashboard. Now we can dispatch load_tickers from the command line. If it runs properly, we’ll have a CSV file similar to the image below. image info
We should now load the history of Cup With Handle patterns for all symbols in data/tickers.csv . Let’s first define the MarketSmith endpoints we’re going to call.
# src/ms/endpoints.py
GET_LOGIN = "https://login.investors.com/accounts.login"
HANDLE_LOGIN = "https://myibd.investors.com/register/raas/loginhandler.aspx"
SEARCH_INSTRUMENTS = "https://marketsmith.investors.com/mstool/api/chart/search-instruments"
GET_PATTERNS = "https://marketsmith.investors.com/WONServices/MSPatternRec/MSPatternRec.svc/json/getPatterns"
GET_USER_INFO = "https://marketsmith.investors.com/mstool/api/tool/user-info"
AuthSession class passes environment variables to IBD API to generate an authenticated session.
# src/ms/auth.py
import os
import json
from requests import Session
from dotenv import load_dotenv
from src.ms.endpoints import HANDLE_LOGIN, GET_LOGIN
load_dotenv()
class AuthSession:
def __init__(self,
username: str = os.environ["USERNAME"],
password: str = os.environ["PASSWORD"],
api_key: str = os.environ["API_KEY"],
include: str = "profile,data,"
):
"""Generates a session authenticated into MarketSmith"""
session = Session()
payload = {
"loginID": username,
"password": password,
"ApiKey": api_key,
"include": include,
"includeUserInfo": "true"
}
# make auth payload accessible to class consumers
self.payload = payload
# make a request to GET_LOGIN endpoint to get login info
login = session.post(GET_LOGIN, data=payload).json()
login["action"] = "login"
# pass the login info to HANDLE_LOGIN endpoint to get .ASPXAUTH cookies
res = session.post(HANDLE_LOGIN, json=login)
self.session = session
We first send the user credentials to GET_LOGIN endpoint to receive the user object, which then will be passed (along with an extra action key) to HANDLE_LOGIN . The response includes the necessary Set-Cookie headers to authenticate the session for future requests. Don’t forget to define USERNAME , PASSWORD , and API_KEY values (according to your MarketSmith account credentials) in .env .
Before fetching patterns, we need to load Instrument and User objects. Let’s start with the latter. Define the User model to serialize the object we’ll receive from the MarketSmith backend.
# src/models/user.py
from pydantic import BaseModel
class User(BaseModel):
"""Represents a MarketSmith `User` object"""
CSUserID: int
DisplayName: str
EmailAddress: str
IsSpecialAccount: bool
RemainingTrialDays: int
SessionID: str
UserDataInitializationFailed: bool
UserEntitlements: str
UserID: int
UserType: int
get_user method receives an authenticated session and returns the authenticated user information.
# src/ms/user.py
from pydantic import validate_arguments
from src.ms.auth import AuthSession
from src.ms.endpoints import GET_USER_INFO
from src.models import User
@validate_arguments(config=dict(arbitrary_types_allowed=True))
def get_user(session: AuthSession) -> User:
"""Gets information of the authenticated user in a session"""
response = session.session.get(GET_USER_INFO)
user = User(**response.json())
return user
validate_arguments decorator parses and validates arguments before the function is called. arbitrary_types_allowedparses arguments with an instance that don’t extend pydantic BaseModel class (in this case, an AuthSession instance).
It’s time to load instrument data from MS API.
# src/ms/utils.py
def convert_msdate_to_date(ms_date: str) -> date:
"""Converts date string passed by MarketSmith API to `date` object
Parameters
----------
ms_date : `str`
e.g., "/Date(1536303600000-0700)/"
Returns
-------
`date`
Raises
-------
`ValueError`
Invalid input type
"""
try:
str_btwn_paranthesis = ms_date[ms_date.find("(")+1:ms_date.find(")")]
if(str_btwn_paranthesis[0] == "-"):
millis = int(str_btwn_paranthesis.split("-")[1]) * -1
else:
millis = int(str_btwn_paranthesis.split("-")[0])
date_obj = date.fromtimestamp(millis/1000)
return date_obj
except TypeError:
raise ValueError(
"Invalid date received from MS. Must be like /Date(1536303600000-0700)/")
# src/models/instrument.py
from datetime import date
from pydantic import BaseModel, validator
class Instrument(BaseModel):
"""Represents a financial `Instrument` object passed by MarketSmith API"""
mSID: int
type: int
instrumentID: int
symbol: str
name: str
earliestTradingDate: date
latestTradingDate: date
hasComponents: bool
hasOptions: bool
isActive: bool
@validator("earliestTradingDate", "latestTradingDate", pre=True, always=True)
def validate_date(cls, v):
from src.ms.utils import convert_msdate_to_date
return convert_msdate_to_date(v)
MS API passes dates with this format: /Date(1536303600000–0700)/–the first number is the date in milliseconds since the epoch, and the second number is the timezone difference with GMT. convert_msdate_to_date method converts MS API date strings to the built-in datetime.date object.
# src/ms/instrument.py
import logging
from pydantic import validate_arguments
from src.ms import AuthSession
from src.ms.endpoints import SEARCH_INSTRUMENTS
from src.models import Instrument
@validate_arguments(config=dict(arbitrary_types_allowed=True))
def get_instrument(session: AuthSession, symbol: str) -> Instrument:
"""Given a symbol (ticker), gets the corresponding `Instrument` from MarketSmith API
Parameters
----------
session : `AuthSession`
authenticated session
symbol : `str`
ticker of Instrument
Raises
----------
`AssertionError`
if the length of search results for the ticker is more than one
Returns
-------
`Instrument`
"""
# search in instruments
search_results = session.session.post(
SEARCH_INSTRUMENTS, json=symbol)
search_results = search_results.json()["content"]
# in search results, find the exact match
instrument = list(filter(
lambda result: result['symbol'] == symbol, search_results))
# there shouldn't be less or more than 1 exact match
try:
assert len(instrument) == 1
except AssertionError:
logging.error(
f"Only 1 exact match should be found. Found {len(instrument)}")
raise
instrument = Instrument(**instrument[0])
return instrument
get_instrument searches for a symbol in the MarketSmith database and then looks for an exact match in search results. If the number of exact matches for the symbol is not one, it raises AssertionError . In the end, it serializes the received dictionary into an Instrument instance.
We’re getting to the meat of the matter. Let’s load, parse, and store Cup With Handle patterns. First, define a model to serialize the data.
# src/models/pattern.py
from typing import Literal, List, Optional
from datetime import date
from pydantic import BaseModel, validator
class CupWithHandle(BaseModel):
"""Represents a cup with handle pattern object passed by MarketSmith API"""
baseID: int
baseStartDate: date
baseEndDate: date
baseNumber: int
baseStage: str
baseStatus: int
pivotPriceDate: date
baseLength: int
periodicity: int
versionID: str
leftSideHighDate: date
patternType: int
firstBottomDate: date
handleLowDate: date
handleStartDate: date
cupEndDate: date
UpBars: int
BlueBars: int
StallBars: int
UpVolumeTotal: int
DownBars: int
RedBars: int
SupportBars: int
DownVolumeTotal: int
BaseDepth: float
AvgVolumeRatePctOnPivot: float
VolumePctChangeOnPivot: float
PricePctChangeOnPivot: float
HandleDepth: float
HandleLength: int
CupLength: int
@validator("baseStartDate", "baseEndDate", "pivotPriceDate", "leftSideHighDate", "firstBottomDate", "handleLowDate", "handleStartDate", "cupEndDate", pre=True, always=True)
def validate_date(cls, v):
from src.ms.utils import convert_msdate_to_date
return convert_msdate_to_date(v)
Next, we need a few methods to handle the extraction and storage of patterns.
# src/ms/pattern.py
import json
from typing import Literal, List
import csv
from pydantic import validate_arguments, BaseModel
from src.ms import AuthSession, get_instrument, get_user
from src.models import Instrument, User, CupWithHandle
from src.ms.endpoints import GET_PATTERNS
@validate_arguments(config=dict(arbitrary_types_allowed=True))
def get_patterns(instrument: Instrument, user: User, session: AuthSession, start: int, end: int) -> dict:
"""Gets all patterns for an instrument in a given period
Parameters
----------
instrument : `Instrument`
Instrument object of the target name
user : `User`
Authenticated user
session : `AuthSession`
Authenticated session
start : `int`
Start in millis
end : `int`
End in millis
Returns
-------
`dict`
"""
start_date = f"/Date({start})/"
end_date = f"/Date({end})/"
payload = {
"userID": user.UserID,
"symbol": instrument.symbol,
"instrumentID": instrument.instrumentID,
"instrumentType": instrument.type,
"dateInfo": {
"startDate": start_date,
"endDate": end_date,
"frequency": 1,
"tickCount": 0
}
}
res = session.session.post(GET_PATTERNS, json=payload)
res = res.json()
return res
def flattern_pattern_properties(patterns: List[dict]) -> List[dict]:
"""Each received Pattern instance from MS includes a `properties` field, which is a list of dictionaries w/ the `Key` and `Value` fields and containts extra properties of the pattern. This method flattens Pattern instance by adding removing `properties` field and adding its keys as separate fields of instance.
Parameters
----------
patterns : `List[dict]`
list of patterns fetched from MS
Returns
-------
`List[dict]`
flattened patterns
"""
# add properties field as separate keys
pattern_properties = [pattern.pop("properties", None)
for pattern in patterns]
for index, props in enumerate(pattern_properties):
for prop in props:
patterns[index][prop["Key"]] = prop["Value"]
return patterns
def filter_cup_with_handles(patterns) -> List[CupWithHandle]:
"""Given the response object of `GET_PATTERNS` endpoint, filters cup with handle patterns from it
Parameters
----------
patterns : `object`
response of `GET_PATTERNS` endpoint
Returns
-------
List[CupWithHandle]
list of cup with handles patterns
"""
# cups w/ or w/o a handle
cups: List[CupWithHandle] = patterns.get("cupWithHandles", None)
if(cups == None):
return
# cups w/ handle
cup_with_handles = [cup
for cup in cups
if cup["patternType"] == 1]
cup_with_handles = flattern_pattern_properties(cup_with_handles)
cup_with_handles = [CupWithHandle(**cup) for cup in cup_with_handles]
return cup_with_handles
def store_patterns(patterns: List[BaseModel], ticker: str) -> None:
"""Stores a given list of patterns to `data/patterns.csv`
Parameters
----------
patterns : `List[BaseModel]`
list of pydantic models (records) of the patterns to be stored
ticker : `str`
ticker that the data belongs to
"""
filepath = "data/patterns.csv"
# convert to dict
patterns = [{**pattern.dict(), "symbol": ticker}for pattern in patterns]
keys = patterns[0].keys()
# check if is empty
with open(filepath, "r") as patterns_file:
csv_dict = [row for row in csv.DictReader(patterns_file)]
is_empty = len(csv_dict) == 0
with open(filepath, 'a') as patterns_file:
dict_writer = csv.DictWriter(patterns_file, keys)
is_empty and dict_writer.writeheader()
dict_writer.writerows(patterns)
get_patterns makes a request to the patterns endpoints and receives all chart patterns for an instrument during a certain period. Note that if you want to get patterns for the weekly chart, set frequency to 2.
MarketSmith passes a properties attribute with the instrument object that includes the instrument’s custom properties as a list. Since we only care about Cup With Handle patterns, and they share the same properties, we use flattern_pattern_properties to flatten the object by removing properties key and adding the elements of its list value to our initial instrument object.
filter_cup_with_handles receives a list of pattern objects and returns Cup With Handle patterns amongst them. One "gotcha" with this method is that MS passes Cup Without Handles and Cup With Handles under cupWithHandles key, but only those with a patternType of 1 are Cup With Handles (see lines 100–102 in the snippet above).
Finally, store_patterns receives a list of pattern instances and appends them to a local CSV file.
To wrap things up, write some controller functions to orchestrate all the previously defined methods.
# src/ms/utils.py
# ...
def convert_csv_to_records(filepath: str, klass: BaseModel) -> List[BaseModel]:
"""Converts a CSV file to a list of models
Parameters
----------
filepath : `str`
filepath of CSV file
klass : `BaseModel`
pydantic model to use for serializing the CSV records
Returns
-------
`List[BaseModel]`
serialized CSV records
"""
with open(filepath) as f:
records = [
klass(**{k: v for k, v in row.items()})
for row in csv.DictReader(f, skipinitialspace=True)]
return records
# src/ms/controller.py
from datetime import datetime
import logging
from typing import List
import src.ms as ms
from src.ms.utils import convert_csv_to_records
from src.models import Constituent
from src.ms.pattern import filter_cup_with_handles
logging.basicConfig(level=logging.INFO)
def extract_patterns(ticker: str, filter_method: callable, start: int, end: int, session=ms.AuthSession()) -> list:
"""Extracts a set of patterns, given a filter method, from MarketSmith API
Parameters
----------
ticker : `str`
symbol of Instrument to get the data for
filter_method : callable
method that filters target patterns from `GET_PATTERNS` endpoint response
start : `int`
start date in millis
end : `int`
end date in millis
session : `AuthSession`, optional
authenticated session, by default ms.AuthSession()
Returns
-------
`list`
List of filtered patterns
"""
user = ms.get_user(session)
instrument = ms.get_instrument(session, ticker)
patterns = ms.get_patterns(instrument, user, session, start, end)
filtered_patterns = filter_method(patterns)
return filtered_patterns
def extract_n_store_cup_with_handles(start: int, end: int, tickers: List[Constituent]) -> None:
"""Loads tickers from `data/tickers.csv`, calls `extract_patterns` for each ticker to load Cup With Handle patterns, and then stores them in `data/patterns.csv
Parameters
----------
start : `int`
start date in millis
end : `int`
end date in millis
"""
for ix, ticker in enumerate(tickers):
logging.info(f"Fetching data for {ticker.symbol}")
logging.info(f"{ix}/{len(tickers)}")
patterns = extract_patterns(
ticker=ticker.symbol, filter_method=filter_cup_with_handles, start=start, end=end)
ms.store_patterns(patterns=patterns, ticker=ticker.symbol)
logging.info("––––––––––––––")
convert_csv_to_records reads rows of a CSV file and serializes them with a pydantic model. We’ll later use it to read and parse the data in tickers.csv file.
extract_patterns receives a ticker, a filter method for a pattern type, start and end dates, and an authenticated session. It then orchestrates other methods to fetch and serialize filtered patterns.
extract_n_store_cup_with_handles accepts the start and end dates in milliseconds since the epoch with a list of Constituent objects, retrieves their cup with handle patterns, and stores those patterns in data/patterns.csv file. Now, call the method with the required arguments.
# src/ms/controller.py
tickers: List[Constituent] = convert_csv_to_records(
"data/tickers.csv", Constituent)
dt_to_milli = lambda dt: datetime.timestamp(dt) * 1000
start = dt_to_milli(datetime(2018, 1, 1))
end = dt_to_milli(datetime(2020, 1, 1))
extract_n_store_cup_with_handles(start, end, tickers)
Awesome! We’re done with the data collection part. Let’s define a trading algorithm based on these patterns and evaluate the results.
Create a Jupyter Notebook to develop, backtest, and analyze the strategy. First, import the requirements.
from datetime import datetime
import pandas as pd
import zipline as zp
import yfinance as yf
import pyfolio as pf
The algorithm, at each tick, loops through patterns, and if all of the following conditions are met, orders the asset:
- The current date has passed the handleLowDate property of the object, but not by more than 30 days;
- The current price has broken out of the pivot price level (the second high of the cup) by more than 1%;
- The 50-day simple moving average (SMA) is above the 200-day SMA.
The algorithm subsequently closes a position in any of these situations:
- The trade generated 15% profit or more;
- The trade led to a loss of 5% or more;
- Twenty-one days or more have been passed since the opening of the position. We use SPY (S&P 500 Trust ETF) returns as the benchmark, run the algorithm from 2016 to 2018, and use ten million dollars of capital. Let’s store all these parameters in a cell to facilitate tweaking or optimizing them.
WATCHLIST_WINDOW_DAYS = 30
ABOVE_PIVOT_PCT = 1.01
TAKE_PROFIT_PCT = 1.15
STOP_LOSS_PCT = .95
PATIENCE_WINDOW_DAYS = 21
START = datetime(2016, 1, 1)
END = datetime(2018, 1, 1)
BENCHMARK = "SPY"
SHORT_MA_LEN = 50
LONG_MA_LEN = 200
CAPITAL_BASE = 10000000
Before defining the logic, we need a utility function that makes date columns of a DataFrame timezone-aware, which allows us to compare dates in the patterns.csv file to zipline built-in dates.
def convert_date_cols(df: pd.DataFrame) -> pd.DataFrame:
"""Given a dataframe, adds UTC timezone to all columns that have date in their names."""
for col in df.columns:
if("date" in col.lower()):
df[col] = pd.to_datetime(df[col]).dt.tz_localize("UTC")
return df
Zipline requires two functions: initialize and handle_data. The former sets up the backtesting context by receiving an argument and adding global variables to it. The latter gets called at each ticker and accepts two arguments–context (the global context of the algorithm) and data that includes the information specific to the current tick–and makes trades based on the current market conditions. By hiding future price data, zipline ensures that there’s no look-ahead bias in the logic.
def initialize(context):
# avoid out of bounds error by dropping firstBottomDate col
patterns = pd.read_csv("data/patterns.csv").drop(["firstBottomDate"], axis=1)
patterns = convert_date_cols(patterns)
context.patterns = patterns
tickers = pd.read_csv("data/tickers.csv")
tickers = convert_date_cols(tickers)
context.stocks = [zp.api.symbol(ticker) for ticker in tickers.symbol]
context.position_dates = {}
Note that zipline.api.symbol method receives a ticker and returns the corresponding Equity object.
def handle_data(context, data):
current_dt = zp.api.get_datetime()
prices = data.history(context.stocks, "price", bar_count=200, frequency="1d")
# look for new trades
for ix, pattern in context.patterns.iterrows():
# skip if asset is already in portfolio
open_positions = set(context.portfolio.positions.keys())
symbol = zp.api.symbol(pattern["symbol"])
is_open = symbol in open_positions
if(is_open): continue
# check date window from handleLowDate to N days after
is_in_window = (pattern["handleLowDate"] <= current_dt) and (pattern["handleLowDate"] >= (current_dt - pd.DateOffset(WATCHLIST_WINDOW_DAYS)))
if (not is_in_window): continue
# get symbol and price history
price_history = prices[symbol]
# check price above pivot
pivot_price_date = pattern["pivotPriceDate"]
try:
pivot_price = price_history[pivot_price_date]
except KeyError:
pivot_price = None
current_price = data.current(symbol, "price")
if(current_price / pivot_price < ABOVE_PIVOT_PCT): continue
# check short MA above long MA
short_ma = price_history.tail(SHORT_MA_LEN).mean()
long_ma = price_history.tail(LONG_MA_LEN).mean()
if(long_ma > short_ma): continue
# add new position and update previous ones
open_positions.add(symbol)
target_pct = 1 / len(open_positions)
for position in open_positions:
zp.api.order_target_percent(position, target_pct)
context.position_dates[symbol] = current_dt
# look for closing positions
open_positions = context.portfolio.positions
for position in open_positions.values():
current_price = position.last_sale_price
buy_price = position.cost_basis
should_take_profit = (current_price / buy_price) > TAKE_PROFIT_PCT
should_stop_loss = (current_price / buy_price) < STOP_LOSS_PCT
does_exceed_patience = (current_dt - pd.DateOffset(PATIENCE_WINDOW_DAYS)) >= context.position_dates[position.asset]
should_close_position = should_take_profit or does_exceed_patience or should_stop_loss
if(should_close_position): zp.api.order_target_percent(position.asset, 0)
First, data.history loads the price data of the stocks list for the past 200 trading days. Then the method loops through patterns and finds the instances that satisfy all the requirements and are not already in the portfolio. When opening a new position, the capital is re-allocated equally amongst all positions, using zp.api.order_target_percent. Eventually, the code stores the current date in context.position_dates dictionary for future reference. Finally, it loops over open positions and, if any sell requirements are satisfied, sells the asset.
Almost done. Define a method to fetch benchmark price data from yfinance and process it to the acceptable pyfolio format (a pandas Series with date index).
def get_benchmark_returns() -> pd.Series:
bench = yf.Ticker(BENCHMARK)
bench_hist = bench_hist.history(start=START, end=END, auto_adjust=True).tz_localize("UTC")
returns = pd.Series(bench_hist["Close"].pct_change().values, index=bench_hist.index).dropna()
returns.index.names = ["date"]
return returns
Note that returns are calculated by calling the pct_change method on the Close column of the price history dataframe. Now we need to handle the analysis of the algorithm.
def analyze(perf: pd.DataFrame, bench: pd.Series) -> None:
returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(perf)
pf.create_full_tear_sheet(returns=returns, benchmark_rets=bench)
analyze receives two arguments: perf , the return value of zipline run_algorithm function, and bench , the benchmark returns retrieved from the previously defined method. pf.utils.extract_rets_pos_txn_from_zipline extracts daily returns, positions history, and the list of all transactions made by the algorithm from the performance dataframe. We pass benchmark and backtest returns to pf.create_full_tear_sheet to generate a comprehensive strategy analysis. In the end, let’s call run_algorithm and inspect the results. Make sure to convert start and end dates to a localized pandas Timestamp object.
# format start end
to_localized_ts = lambda dt: pd.Timestamp(dt).tz_localize("UTC")
start, end = to_localized_ts(START), to_localized_ts(END)
# get returns
benchmark = get_benchmark_returns()
# run strat
results = zp.run_algorithm(
start=start,
end=end,
initialize=initialize,
handle_data=handle_data,
benchmark_returns=benchmark,
capital_base=CAPITAL_BASE,
bundle='quandl',
data_frequency='daily')
# analyze results
analyze(results, benchmark)
# store results to CSV
results.to_csv("results.csv")
It’s time to receive our just deserts. After running the analyze method, pyfolio generates a tear sheet that includes several tables and charts to present a detailed analysis of the results.
Start date 2016年01月04日
End date 2017年12月29日
Total months 23
Backtest
---------
Annual return 9.7%
Cumulative returns 20.2%
Annual volatility 7.5%
Sharpe ratio 1.27
Calmar ratio 1.96
Stability 0.91
Max drawdown -4.9%
Omega ratio 1.62
Sortino ratio 2.4
Skew 3.63
Kurtosis 45.14
Tail ratio 1.6
Daily value at risk -0.9%
Alpha 0.08
Beta 0.1
With 0.08 alpha and 0.1 beta, the strategy seems too passive, which could be improved by increasing the number of watchlist stocks. But the risk-return measures of the strategy look solid — notably, Sharpe, Sortino, and Calmar ratios display acceptable returns given the low exposure. You can find the full tear sheet of the strategy results below. image info
The strategy could be enhanced in many ways; let’s discuss some of them.
- % of up bars: by taking the ratio of green bars to red bars during the pattern formation, particularly in the latter half of the cup, we can gauge the strength of the bullish pattern and the potential breakout.
- % of up volume: similarly, showing above-average volume during up days (skyscrapers of accumulation) may confirm that institutions are interested in the asset. [3]
- Volume on breakout: another solution could be to buy the name when the volume is above average on the breakout day.
- The volatility of the cup: the cup shouldn’t be V-shaped; using the Average True Range or standard deviation of the price action, we can gauge the smoothness of the price movement while forming the cup pattern. [4]
- Prior uptrend strength: by making sure that the pattern follows a strong and established uptrend, using the height and length of the rally, we can ensure that a strong move backs the base.
[1] D. Saito-Chung, When To Buy The Best Growth Stocks: How To Analyze A Stock’s Cup With Handle (2020), Investor’s Business Daily
[2] Cup With Handle, StockCharts ChartSchool
[3] S. Lehtonen, Roku, One Of The Top Stocks Of 2019, Built ‘Skyscrapers’ Of Accumulation Before A Breakout (2019), Investor’s Business Daily
[4] W. J. O’Neill, How to Make Money in Stocks: A Winning System in Good Times and Bad (2009)