Mean reversion and Breakout strategy combo |

Below is a snapshot of some trading research I have been doing.

By no means this is a trade-able strategy as is, but it does show two of the basic trading principles:

- Mean reversion

This is the principle that price tends to return to it’s mean after it has moved away from it. - Breakout

This is the principle that price tends to break away once it has moved more then a certain percentage usually after a period of consolidation.

#### The mean reversion strategy

#### The break out strategy

#### Filtering times to trade

#### Vectorized backtest

The backtest below is a so called vectorized backtest done with Python in a Jupyter notebook. This is a quick and convenient way to research trading ideas. I use this way of researching to quickly discard trading ideas. If an idea does not get discarded, I will do an event based back test. If the strategy holds up here, I will paper trade the strategy.

import pandas as pd import numpy as np import seaborn as sns; sns.set() import datetime %matplotlib inline

# Read in eurusd daily data from stooq.com df = pd.read_csv("eurusd_d.csv", index_col="Date", parse_dates=True) # Select a date range df = df[(df.index > '2000-1-1') & (df.index <= '2016-12-31')]

# Calculate daily differences df['diff'] = df['Close'].diff(periods=1)

# Calcultate the cumulative returns df['cum'] = df['diff'].cumsum()

# Meanreversion # Setting position long = 1 and short = -1 based on previous day move delta = 0.005 # If previous day price difference was less than or equal then delta, we go long # If previous day price difference was more than or equal then delta, we go short df['position_mr'] = np.where(df['diff'].shift(1) <= -delta,1, np.where(df['diff'].shift(1) >= delta, -1, 0)) df['result_mr'] = (df['diff'] * df['position_mr']).cumsum()

# We will filter execution of our strategy by only executing if our result are above it's 200 day moving average win =200 df['ma_mr'] = pd.rolling_mean(df['result_mr'], window=win) filtering_mr = df['result_mr'].shift(1) > df['ma_mr'].shift(1) df['filteredresult_mr'] = np.where(filtering_mr, df['diff'] * df['position_mr'], 0).cumsum() # if we do not want to filter we use below line of code # df['filteredresult_mr'] = (df['diff'] * df['position_mr']).cumsum() df[['ma_mr','result_mr','filteredresult_mr']].plot(figsize=(10,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7f4d8ca4c550>

# Breakout # Setting position long = 1 and short = -1 based on previous day move # By setting the delta to negative we are switching the strategy to Breakout delta = -0.01 # If previous day price difference was less than or equal then delta, we go long # If previous day price difference was more than or equal then delta, we go short df['position_bo'] = np.where(df['diff'].shift(1) <= -delta,1, np.where(df['diff'].shift(1) >= delta, -1, 0)) df['result_bo'] = (df['diff'] * df['position_bo']).cumsum()

# We will filter execution of our strategy by only executing if our result are above it's 200 day moving average win = 200 df['ma_bo'] = pd.rolling_mean(df['result_bo'], window=win) filtering_bo = df['result_bo'].shift(1) > df['ma_bo'].shift(1) df['filteredresult_bo'] = np.where(filtering_bo, df['diff'] * df['position_bo'], 0).cumsum() # df['filteredresult_bo'] = (df['diff'] * df['position_bo']).cumsum() df[['ma_bo','result_bo','filteredresult_bo']].plot(figsize=(10,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7f4d8c877358>

# Here we combine the Meanreversion and the Breakout strategy results df['combi'] = df['filteredresult_mr'] + df['filteredresult_bo'] df[['combi','filteredresult_mr','filteredresult_bo']].plot(figsize=(10,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7f4d8c5fb940>

print("Total return since 2000:",df['combi'][-1] * 100, "%")

Total return since 2000: 171.645 %

def sharpe(serie): std = serie.std() mean = serie.mean() return (mean / std) * 252 ** 0.5

print("Sharpe ratios") print("Combi", sharpe(df['combi'].diff(periods=1))) print("Mr", sharpe(df['filteredresult_mr'].diff(periods=1))) print("Bo", sharpe(df['filteredresult_bo'].diff(periods=1)))

Sharpe ratios Combi 0.824594193246 Mr 0.41962974772 Bo 0.816470905001

This is a different way of researching possible trading strategies in which I can also leverage my coding skills. It is my intention to build up my own algorithmic trading system in which I can continuously research, develop and implement trading strategies and trade these live next to my manually executed trading strategies.