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].

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.

Prerequisites

A basic understanding of Python is required 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 work with the latest stack.

Load and Store Data

DJIA Constituents

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 the

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 the

FMP_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.

Cup With Handle Data

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

It first sends 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_allowed

parses 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 MarketSmith. 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/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)/")

Nice. Now, use the method in the

Instrument

class to convert the dates during validation.

# 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)

And fetch the instruments.

# 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

value in the payload to 2.

MarketSmith passes

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 all cup patterns under

cupWithHandles

key, but only those with a

patternType

of 1 are Cup With Handles.

Finally,

store_patterns

receives a list of pattern instances and appends them to a local CSV file. Now, call the method with the required arguments.

To wrap things up, let's write some controller functions to orchestrate all the previously defined methods. But first, define a utility function to serialize CSV records.

# 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

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 the tickers.csv file.

# 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("––––––––––––––")

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 Cup with Handle patterns for them, 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.

Strategy

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 adds timezone information to date columns of a dataframe, 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 variables) 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

. 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

zp.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")

Results

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 2016-01-04 End date 2017-12-29 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.

Improvements

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 volatile and V-shaped [4]; 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.
Prior uptrend strength: By making sure that the pattern follows a strong and established uptrend, using the height and length of the prior rally, we can ensure that a strong move backs the base.