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Improved readme and added some customisation options in correlation calculation

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This commit is contained in:
Jamie Cash
2021-02-05 12:08:14 +00:00
parent a9b65f95c3
commit 16d8a98609
5 changed files with 152 additions and 37 deletions
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mt5_correlation/correlation.py
+11 -6
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@@ -8,24 +8,29 @@ class Correlation:
"""
@staticmethod
def calculate_coefficient(symbol1_prices, symbol2_prices):
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.
:param symbol1_prices:
:param symbol2_prices:
: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 90% of the size of the
# largest set and is the overlap set size at least 90% the size of the smallest set?
# 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?
coefficient = None
intersect_dates = (set(symbol1_prices['time']) & set(symbol2_prices['time']))
len_smallest_set = int(min([len(symbol1_prices.index), len(symbol2_prices.index)]))
len_largest_set = int(max([len(symbol1_prices.index), len(symbol2_prices.index)]))
similar_size = len_largest_set * .9 <= len_smallest_set
enough_overlap = len(intersect_dates) >= len_smallest_set * .9
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
if suitable:
@@ -38,7 +43,7 @@ class Correlation:
# Calculate coefficient. Only use if p value is < 0.01 (highly likely that coefficient is valid and null
# hypothesis is false).
coefficient_with_p_value = pearsonr(symbol1_prices_filtered['close'], symbol2_prices_filtered['close'])
coefficient = None if coefficient_with_p_value[1] > 0.01 else coefficient_with_p_value[0]
coefficient = None if coefficient_with_p_value[1] >= max_p_value else coefficient_with_p_value[0]
# If NaN, change to None
if coefficient is not None and math.isnan(coefficient):
mt5_correlation/mt5.py
+48 -3
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@@ -8,6 +8,29 @@ class MT5:
A class to connect to and interface with MetaTrader 5
"""
# Timeframes
TIMEFRAME_M1 = mt5.TIMEFRAME_M1
TIMEFRAME_M2 = mt5.TIMEFRAME_M2
TIMEFRAME_M3 = mt5.TIMEFRAME_M3
TIMEFRAME_M4 = mt5.TIMEFRAME_M4
TIMEFRAME_M5 = mt5.TIMEFRAME_M5
TIMEFRAME_M6 = mt5.TIMEFRAME_M6
TIMEFRAME_M10 = mt5.TIMEFRAME_M10
TIMEFRAME_M12 = mt5.TIMEFRAME_M10
TIMEFRAME_M15 = mt5.TIMEFRAME_M15
TIMEFRAME_M20 = mt5.TIMEFRAME_M20
TIMEFRAME_M30 = mt5.TIMEFRAME_M30
TIMEFRAME_H1 = mt5.TIMEFRAME_H1
TIMEFRAME_H2 = mt5.TIMEFRAME_H2
TIMEFRAME_H3 = mt5.TIMEFRAME_H3
TIMEFRAME_H4 = mt5.TIMEFRAME_H4
TIMEFRAME_H6 = mt5.TIMEFRAME_H6
TIMEFRAME_H8 = mt5.TIMEFRAME_H8
TIMEFRAME_H12 = mt5.TIMEFRAME_H12
TIMEFRAME_D1 = mt5.TIMEFRAME_D1
TIMEFRAME_W1 = mt5.TIMEFRAME_W1
TIMEFRAME_MN1 = mt5.TIMEFRAME_MN1
def __init__(self):
# Connect to MetaTrader5. Opens if not already open.
@@ -48,16 +71,38 @@ class MT5:
return selected_symbols
def get_prices(self, symbol, from_date, to_date):
def get_prices(self, symbol, from_date, to_date, timeframe):
"""
Gets the 1 weeks of M15 OHLC price data for the specified symbol.
Gets OHLC price data for the specified symbol.
:param symbol: The MT5 symbol to get the price data for
:param from_date: Date from when to retrieve data
:param to_date: Date where to receive data to
:param timeframe: The timeframe for the candes. Possible values are:
TIMEFRAME_M1: 1 minute
TIMEFRAME_M2: 2 minutes
TIMEFRAME_M3: 3 minutes
TIMEFRAME_M4: 4 minutes
TIMEFRAME_M5: 5 minutes
TIMEFRAME_M6: 6 minutes
TIMEFRAME_M10: 10 minutes
TIMEFRAME_M12: 12 minutes
TIMEFRAME_M15: 15 minutes
TIMEFRAME_M20: 20 minutes
TIMEFRAME_M30: 30 minutes
TIMEFRAME_H1: 1 hour
TIMEFRAME_H2: 2 hours
TIMEFRAME_H3: 3 hours
TIMEFRAME_H4: 4 hours
TIMEFRAME_H6: 6 hours
TIMEFRAME_H8: 8 hours
TIMEFRAME_H12: 12 hours
TIMEFRAME_D1: 1 day
TIMEFRAME_W1: 1 week
TIMEFRAME_MN1: 1 month
:return: Price data for symbol as dataframe
"""
# Get prices from MT5
prices = mt5.copy_rates_range(symbol.name, mt5.TIMEFRAME_M15, from_date, to_date)
prices = mt5.copy_rates_range(symbol.name, timeframe, from_date, to_date)
self.log.info(f"{len(prices)} prices retrieved for {symbol.name}.")
# Create dataframe from data and convert time in seconds to datetime format
mt5_correlation/mt5_correlation.py
-73
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@@ -1,73 +0,0 @@
from datetime import datetime, timedelta
import logging.config
import pandas as pd
import pytz
import yaml
from mt5 import MT5
from correlation import Correlation
import definitions
# Configure logger
with open(fr'{definitions.ROOT_DIR}\logging_conf.yaml', 'rt') as file:
config = yaml.safe_load(file.read())
logging.config.dictConfig(config)
log = logging.getLogger()
# Create mt5 class. This contains required methods for interacting with MT5.
mt5 = MT5()
# Gte all visible symbols
symbols = mt5.get_symbols()
# set time zone to UTC to avoid local offset issues, and get from and to dates (a week ago to today)
timezone = pytz.timezone("Etc/UTC")
utc_to = datetime.now(tz=timezone)
utc_from = utc_to - timedelta(days=7)
# 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.name] = mt5.get_prices(symbol=symbol, from_date=utc_from, to_date=utc_to)
# 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.
columns = ['Symbol 1', 'Symbol 2', 'Coefficient', 'UTC Date From', 'UTC Date To', 'Interval']
coefficients = pd.DataFrame(columns=columns)
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.name]
symbol2_price_data = price_data[symbol2.name]
# Get coefficient and store if valid
coefficient = Correlation.calculate_coefficient(symbol1_price_data, symbol2_price_data)
if coefficient is not None:
coefficients = coefficients.append({'Symbol 1': symbol1.name, 'Symbol 2': symbol2.name,
'Coefficient': coefficient, 'UTC Date From': utc_from,
'UTC Date To': utc_to, 'Interval': 'M15'}, ignore_index=True)
log.info(f"Pair {index} of {num_pair_combinations}: {symbol1.name}:{symbol2.name} has a coefficient of "
f"{coefficient}.")
else:
log.info(f"Coefficient for pair {index} of {num_pair_combinations}: {symbol1.name}:{symbol2.name} could not "
f"be calculated.")
# Sort, highest correlated first
coefficients = coefficients.sort_values('Coefficient', ascending=False)
# Save as CSV
filename = f"out/Coefficients from {utc_from:%Y%m%d %H%M%S} to {utc_to:%Y%m%d %H%M%S} at M15.csv"
log.info(f"Saving coefficients as '{filename}'.")
coefficients.to_csv(filename, index=False)