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Added UI and basic monitoring

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Jamie Cash
2021-02-10 18:21:49 +00:00
parent 16d8a98609
commit ef28f80a85
14 changed files with 896 additions and 131 deletions
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mt5_correlation/correlation.py
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@@ -1,26 +1,305 @@
import math
import logging
import pandas as pd
from datetime import datetime
import time
import sched
import threading
import pytz
from scipy.stats.stats import pearsonr
from mt5_correlation.mt5 import MT5
class Correlation:
"""
A class to calculate the correlation coefficient between two sets of price data
A class to maintain the state of the calculated correlation coefficients.
"""
@staticmethod
def calculate_coefficient(symbol1_prices, symbol2_prices, max_set_size_diff_pct=90, overlap_pct=90,
max_p_value=0.05):
"""
Calculates the correlation coefficient between two sets of price data. Uses close price.
min_coefficient = 0.9
monitoring = False # Monitoring cor correlations
:param symbol1_prices:
:param symbol2_prices:
def __init__(self):
self.log = logging.getLogger(__name__)
# Create dataframe
columns = ['Symbol 1', 'Symbol 2', 'Base Coefficient', 'UTC Date From', 'UTC Date To', 'Timeframe',
'Last Check', 'Last Coefficient']
self.coefficient_data = pd.DataFrame(columns=columns)
# Create timer for continuous monitoring
self.scheduler = sched.scheduler(time.time, time.sleep)
def load(self, filename):
"""
Loads a csv file containing calculated coefficients
:param filename:
:return:
"""
self.coefficient_data = pd.read_csv(filename)
def save(self, filename):
"""
Saves the calculated coefficients as a csv file
:param filename:
:return:
"""
self.coefficient_data.to_csv(filename, index=False)
def calculate(self, date_from, date_to, timeframe, min_prices=100, max_set_size_diff_pct=90, overlap_pct=90,
max_p_value=0.05):
"""
Calculates correlation coefficient between all symbols in MetaTrader5 Market Watch. Updates coefficient data.
:param date_from: From date for price data from which to calculate correlation coefficients
:param date_to: To date for price data from which to calculate correlation coefficients
:param timeframe: Timeframe for price data from which to calculate correlation coefficients
:param min_prices: The minimum number of prices that should be used to calculate coefficient. If this threshold
is not met then returned coefficient will be None
:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
within this pct of each other
:param overlap_pct:
:param max_p_value: The maximum p value for the correlation to be meaningful
:return:
"""
# Create mt5 class. This contains required methods for interacting with MT5.
mt5 = MT5()
# Gte all visible symbols
symbols = mt5.get_symbols()
# Get price data for selected symbols. 1 week of 15 min OHLC data for each symbol. Add to dict.
price_data = {}
for symbol in symbols:
price_data[symbol] = mt5.get_prices(symbol=symbol, from_date=date_from, to_date=date_to,
timeframe=timeframe)
# Loop through all symbol pair combinations and calculate coefficient. Make sure you don't double count pairs
# eg. (USD/GBP AUD/USD vs AUD/USD USD/GBP). Use grid of all symbols with i and j axis. j starts at i + 1 to
# avoid duplicating. We will store all coefficients in a dataframe for export as CSV.
index = 0
# There will be (x^2 - x) / 2 pairs where x is number of symbols
num_pair_combinations = int((len(symbols) ** 2 - len(symbols)) / 2)
for i in range(0, len(symbols)):
symbol1 = symbols[i]
for j in range(i + 1, len(symbols)):
symbol2 = symbols[j]
index += 1
# Get price data for both symbols
symbol1_price_data = price_data[symbol1]
symbol2_price_data = price_data[symbol2]
# Get coefficient and store if valid
coefficient = self.calculate_coefficient(symbol1_prices=symbol1_price_data,
symbol2_prices=symbol2_price_data,
min_prices=min_prices,
max_set_size_diff_pct=max_set_size_diff_pct,
overlap_pct=overlap_pct, max_p_value=max_p_value)
if coefficient is not None:
self.coefficient_data = \
self.coefficient_data.append({'Symbol 1': symbol1, 'Symbol 2': symbol2,
'Base Coefficient': coefficient, 'UTC Date From': date_from,
'UTC Date To': date_to, 'Timeframe': timeframe},
ignore_index=True)
self.log.debug(f"Pair {index} of {num_pair_combinations}: {symbol1}:{symbol2} has a "
f"coefficient of {coefficient}.")
else:
self.log.debug(f"Coefficient for pair {index} of {num_pair_combinations}: {symbol1}:"
f"{symbol2} could no be calculated.")
# Sort, highest correlated first
self.coefficient_data = self.coefficient_data.sort_values('Base Coefficient', ascending=False)
def update_coefficient(self, symbol1, symbol2, date_from, date_to, min_prices=100, max_set_size_diff_pct=90,
overlap_pct=90, max_p_value=0.05):
"""
Updates the coefficient for the specified symbol pair
:param symbol1: Name of symbol to calculate coefficient for.
:param symbol2: Name of symbol to calculate coefficient for.
:param date_from: From date for tick data from which to calculate correlation coefficients
:param date_to: To date for tick data from which to calculate correlation coefficients
:param min_prices: The minimum number of prices that should be used to calculate coefficient. If this threshold
is not met then returned coefficient will be None
:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
within this pct of each other
:param overlap_pct:
:param max_p_value: The maximum p value for the correlation to be meaningful
:return: correlation coefficient, or None if coefficient could not be calculated.
"""
# Get the tick data
mt5 = MT5()
symbol1ticks = mt5.get_ticks(symbol=symbol1, from_date=date_from, to_date=date_to)
symbol2ticks = mt5.get_ticks(symbol=symbol2, from_date=date_from, to_date=date_to)
# Resample to 1 sec OHLC, this will help with coefficient calculation ensuring that we dont have more than one
# tick per second and ensuring that times can match. We will need to set the index to time for the resample
# then revert back to a 'time' column. We will then need to remove rows with nan in 'close' price
if symbol1ticks is not None and symbol2ticks is not None and \
len(symbol1ticks.index) > 0 and len(symbol2ticks.index) > 0:
symbol1ticks.set_index('time', inplace=True)
symbol2ticks.set_index('time', inplace=True)
symbol1prices = symbol1ticks['ask'].resample('1S').ohlc()
symbol2prices = symbol2ticks['ask'].resample('1S').ohlc()
symbol1prices.reset_index(inplace=True)
symbol2prices.reset_index(inplace=True)
symbol1prices = symbol1prices[symbol1prices['close'].notna()]
symbol2prices = symbol2prices[symbol2prices['close'].notna()]
# Calculate the coefficient
coefficient = self.calculate_coefficient(symbol1_prices=symbol1prices, symbol2_prices=symbol2prices,
min_prices=min_prices,
max_set_size_diff_pct=max_set_size_diff_pct,
overlap_pct=overlap_pct, max_p_value=max_p_value)
else:
coefficient = None
# Find the correct row in the coefficient data and update with calculation date and calculated coefficient
timezone = pytz.timezone("Etc/UTC")
now = datetime.now(tz=timezone)
# Update data if we have a coefficient
if coefficient is not None:
self.coefficient_data.loc[(self.coefficient_data['Symbol 1'] == symbol1) &
(self.coefficient_data['Symbol 2'] == symbol2),
'Last Check'] = now
self.coefficient_data.loc[(self.coefficient_data['Symbol 1'] == symbol1) &
(self.coefficient_data['Symbol 2'] == symbol2),
'Last Coefficient'] = coefficient
return coefficient
def update_all_coefficients(self, date_from, date_to, min_prices=100, max_set_size_diff_pct=90, overlap_pct=90,
max_p_value=0.05):
"""
Updates the coefficient for all symbol pairs in that meet the min_coefficient threshold. Symbol pairs that meet
the threshold can be accessed through the filtered_coefficient_data property.
:param date_from: From date for tick data from which to calculate correlation coefficients
:param date_to: To date for tick data from which to calculate correlation coefficients
:param min_prices: The minimum number of prices that should be used to calculate coefficient. If this threshold
is not met then returned coefficient will be None
:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
within this pct of each other
:param overlap_pct:
:param max_p_value: The maximum p value for the correlation to be meaningful
:return: correlation coefficient, or None if coefficient could not be calculated.
"""
# Update latest coefficient for every pair
for index, row in self.filtered_coefficient_data.iterrows():
symbol1 = row['Symbol 1']
symbol2 = row['Symbol 2']
self.update_coefficient(symbol1=symbol1, symbol2=symbol2, date_from=date_from, date_to=date_to,
min_prices=min_prices, max_set_size_diff_pct=max_set_size_diff_pct,
overlap_pct=overlap_pct, max_p_value=max_p_value)
def start_monitor(self, interval, date_from, date_to, min_prices=100, max_set_size_diff_pct=90, overlap_pct=90,
max_p_value=0.05):
"""
Starts monitor to continuously update the coefficient for all symbol pairs in that meet the min_coefficient
threshold.
:param interval: How often to check in seconds
:param date_from: From date for tick data from which to calculate correlation coefficients
:param date_to: To date for tick data from which to calculate correlation coefficients
:param min_prices: The minimum number of prices that should be used to calculate coefficient. If this threshold
is not met then returned coefficient will be None
:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
within this pct of each other
:param overlap_pct:
:param max_p_value: The maximum p value for the correlation to be meaningful
:return: correlation coefficient, or None if coefficient could not be calculated.
:return:
"""
self.log.debug(f"Starting monitor.")
self.monitoring = True
# Create thread to run monitoring This will call private __monitor method that will run the calculation and
# keep scheduling itself while self.monitoring is True
params = {'interval': interval, 'date_from': date_from, 'date_to': date_to, 'min_prices': min_prices,
'max_set_size_diff_pct': max_set_size_diff_pct, 'overlap_pct': overlap_pct,
'max_p_value': max_p_value}
thread = threading.Thread(target=self.__monitor, kwargs=params)
thread.start()
def stop_monitor(self):
"""
Stops monitoring symbol pairs for correlation.
:return:
"""
self.log.debug(f"Stopping monitor.")
self.monitoring = False
def __monitor(self, interval, date_from, date_to, min_prices=100, max_set_size_diff_pct=90, overlap_pct=90,
max_p_value=0.05):
"""
The actual monitor method. Private. This should not be called outside of this class. Use start_monitoring and
stop_monitoring.
:param interval: How often to check in seconds
:param date_from: From date for tick data from which to calculate correlation coefficients
:param date_to: To date for tick data from which to calculate correlation coefficients
:param min_prices: The minimum number of prices that should be used to calculate coefficient. If this threshold
is not met then returned coefficient will be None
:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
within this pct of each other
:param overlap_pct:
:param max_p_value: The maximum p value for the correlation to be meaningful
:return: correlation coefficient, or None if coefficient could not be calculated.
:return:
"""
self.log.debug(f"In monitor event. Monitoring: {self.monitoring}.")
# Only run if monitor is not stopped
if self.monitoring:
# Update all coefficients
self.update_all_coefficients(date_from=date_from, date_to=date_to, min_prices=min_prices,
max_set_size_diff_pct=max_set_size_diff_pct, overlap_pct=overlap_pct,
max_p_value=max_p_value)
# Schedule the timer to run again
params = {'interval': interval, 'date_from': date_from, 'date_to': date_to, 'min_prices': min_prices,
'max_set_size_diff_pct': max_set_size_diff_pct, 'overlap_pct': overlap_pct,
'max_p_value': max_p_value}
self.scheduler.enter(delay=interval, priority=1, action=self.__monitor, kwargs=params)
self.scheduler.run()
@property
def filtered_coefficient_data(self):
"""
:return: Coefficient data filtered so that all base coefficients >= min coefficient
"""
if self.coefficient_data is not None:
return self.coefficient_data.loc[self.coefficient_data['Base Coefficient'] >= self.min_coefficient]
else:
return None
@staticmethod
def calculate_coefficient(symbol1_prices, symbol2_prices, min_prices=100, max_set_size_diff_pct=90,
overlap_pct=90, max_p_value=0.05):
"""
Calculates the correlation coefficient between two sets of price data. Uses close price.
:param symbol1_prices: prices or ticks for symbol 1
:param symbol2_prices: prices or ticks for symbol 2
:param min_prices: The minimum number of prices that should be used to calculate coefficient. If this threshold
is not met then returned coefficient will be None
:param max_set_size_diff_pct: Correlations will only be calculated if the sizes of the two price data sets are
within this pct of each other
:param overlap_pct:
:param max_p_value: The maximum p value for the correlation to be meaningful
:return: correlation coefficient, or None if coefficient could not be calculated.
"""
# Calculate size of intersection and determine if prices for symbols have enough overlapping timestamps for
# correlation coefficient calculation to be meaningful. Is the smallest set at least max_set_size_diff_pct % of
# the size of the largest set and is the overlap set size at least overlap_pct % the size of the smallest set?
@@ -31,7 +310,8 @@ class Correlation:
len_largest_set = int(max([len(symbol1_prices.index), len(symbol2_prices.index)]))
similar_size = len_largest_set * (max_set_size_diff_pct / 100) <= len_smallest_set
enough_overlap = len(intersect_dates) >= len_smallest_set * (overlap_pct / 100)
suitable = similar_size and enough_overlap
enough_prices = len_smallest_set >= min_prices
suitable = similar_size and enough_overlap and enough_prices
if suitable:
# Calculate coefficient on close prices
@@ -50,6 +330,3 @@ class Correlation:
coefficient = None
return coefficient