PerfectSetup

#!/usr/bin/python3
# 
# Shows indicators, scores them or filters them based on "The Perfect Setup"
# as given by "The 5 Secrets To Highly Profitable Swing Trading" by Ivaylo Ivanov.
# 
# 
# [ The "Perfect" Setup ]
# Previous uptrend
# Near 10-day high (within 3% of it)
# High relative strength on weekly, monthly, quarterly or half year basis
# Tight side-ways consolidation on below average volume, lasting anywhere between 2 and 20 trading days.
# The closing prices of the past few trading days are very near to each other
# Coiled near its 5, 10 or 20-day moving average
# Trading above its 5-day moving average
# 5-day moving average is above its 20-day moving average
# 
# 
# [ Good Potential ]
# Up >10% over week
# Up >20% over month (or >10% for large cap)
# Up >30% over quarter (or >20% for large cap)
# Up >40% over 6 months (or >30% for large cap)
# 
# 
# [ Breakout Setup ]
# Low-volume range contraction (2 to 20 days)
# Up from the open >2%
# Daily change >2%
# New 10-day high
# 5-day MA >20-day MA
# Price is above its 5-day MA
# Average daily volume >50k
# Relative volume >1
# Price >2
# Set stop at the lows of the 2% breakout day
# 
#
# [ Anticipation Setup (i.e. buying early for a breakout) ]
# Works only in strong up-trending markets, where most setups eventually break out
# May not trigger immediately
# Be aware of strongest industries in the market at the time
# 
# 
# [Don't Buy If ...]
# ... there is no range contraction
# ... the stock is already up 3-4 days in a row


import argparse

import fetcher
import printing


CLOSE  = fetcher.DataSource.CLOSE
OPEN   = fetcher.DataSource.OPEN
HIGH   = fetcher.DataSource.HIGH
LOW    = fetcher.DataSource.LOW
VOLUME = fetcher.DataSource.VOLUME


class ComputedResult(object):
  """Bag of data to keep the results of some computed values."""

  def __init__(self, **kwargs):
    self.good = False
    self.value = None
    self.print_value = str(self.value)
    self.print_value_color = str(self.value)
    self.data = {}
    for k,v in kwargs.items():
      setattr(self, k, v)

  def __nonzero__(self):
    return self.good


class ThePerfectSetup(object):

  def __init__(self, ticker):
    self.ticker = ticker
    self.df = fetcher.DataFetcher().FetchData(ticker)

  def PreviousUptrend(self):
    """Previous uptrend."""

  def NearTenDayHigh(self):
    """Near 10-day high (within 3% of it)."""
    todays = float(self.df.tail(1)[HIGH])
    highest = float(self.df.tail(10)[HIGH].max())
    percent = (todays - highest) / highest
    result = ComputedResult(good=False,
                            value=percent,
                            print_value='{:.1f}%'.format(percent * 100),
                            print_value_color='{:.1f}%'.format(percent * 100),
                            data={'todays_highest': todays,
                                  'ten_day_highest': highest,
                                  'percent': percent})
    if percent >= -0.03 and percent <= 0.03:
      result.good = True
    return result

  def HighRelativeStrength(self):
    """High relative strength on weekly, monthly, quarterly or half year basis."""

  def TightConsolidation(self):
    """Tight side-ways consolidation on below average volume, lasting anywhere between 2 and 20 trading days."""

  def CloseClosingPrices(self):
    """The closing prices of the past few trading days are very near to each other."""

  def CoiledNearSMAs(self):
    """Coiled near its 5, 10 or 20-day moving average."""

  def TradingAbove5SMA(self):
    """Trading above its 5-day moving average."""

  def FiveSMAAbove20SMA(self):
    """5-day moving average is above its 20-day moving average."""


def main(args):
  headers = ['Ticker', 'Near 10d High']
  printer = printing.TabularPrinter(headers=headers)
  rows = []
  for ticker in args.tickers:
    ps = ThePerfectSetup(ticker)
    rows.append((ticker, bool(ps.NearTenDayHigh())))
  printer.print(rows)


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('tickers', nargs='+')
  main(parser.parse_args())