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Presentation ICBE 2019 - Low-risk trading algorithm based on the price cyclicality function for capital markets.

Authors:
  • Algorithm Invest

Abstract

Buy cheap and sell more expensive is one of the basic idea trading the capital markets since hundreds of years. To apply it in practice has become difficult nowadays due to the high price volatility. The uncertainty in the price movements often leads to a high risk allocation. One main question is when the price is low enough for a low risk entry? Once established an entry point, the second question is how long to keep the open trades in order to optimize the investment efficiency? This article will answer to these questions. A general trading algorithms based on the price cyclical behavior will be revealed. The mathematical model is developed using the Price Cyclicality Function combined with other computational techniques in order to establish the low risk intervals. The algorithm will use multiple entry points in order to catch the best price opportunities. A simple empirical exit algorithm will be optimized in order to maximize the profit for a certain capital exposure level. The presented model uses a low number of functional parameters which can be optimized with reasonable computational effort for any financial market. Trading results obtained for several markets will also be included in this paper in order to reveal the efficiency of the presented methodology. It was found that the Low Risk Trading Algorithm can be used with good results for algorithmic trading in any financial market. With the right parameters set, this methodology can be wide range applied in the stock markets, currency and cryptocurrency markets, commodities and row materials markets and even for the real estate investments. The simplicity of the presented model and the good efficiency level obtained will recommend it. This methodology can be used by any investor in order to manage the investment plan with multiple capital markets.
Low-risk trading algorithm
based on the price cyclicality
function for capital markets.
Cristian Păuna
Economic Informatics Doctoral School
Academy of Economic Studies
Paper co-financed by
Algorithm Invest SRL
https://algoinvest.biz
The 13th International Conference
On Business Excellence
21-22 March 2019
Bucharest, Romania
This paper presents:
- a functional trading algorithm
- making multiple low-risk entries
- using the Price Cyclicality Function
- and a variable entry price gradient
- with a combined exit level method
- applying Relative Strength Index for
- higher profit in normal price action
- and lower profit for overbought price
It was found that:
- the presented algorithm has only
- six functional parameters that can be
- optimized for any capital market with
- a reasonable computational effort
- obtaining a good capital efficiency for
- stock markets
- commodities markets
- currency markets and
- cryptocurrency markets
- there are two functional limitations
Low-risk trading algorithm based on the
price cyclicality function for capital markets.
ALPINE SKI HOUSE
Low-risk
trading algorithm
where
i is the index of the time interval
j is the index of the multiple entries
PCY is Price Cyclicality Function
ξ is the limit for the price cyclicality
p is the current price level
δj is the entry price gradient
depending on ρ and δ parameters
N is the maximal number of trades
ϴ is the higher take profit level
θ is the lower take profit level
RSI is the Relative Strength Index
Ω is the RSI overbought price level
ALPINE SKI HOUSE
Price Cyclicality
Function
where
α is a functional parameter
Mak a moving average with M period
mak a moving average with m period
considering M > m
n is the period for the PCY function
The Price Cyclicality Function was introduced in:
Păuna, C., Lungu, I. (2018).
Price Cyclicality Model for Financial Markets. Reliable Limit Conditions for Algorithmic Trading.
Economic computation and economic cybernetics studies and research journal.
Volume 52 Issue 4/2018. ISSN: 18423264
Bucharest, Romania: Academy of Economic Studies
DOI: 10.24818/18423264/52.4.18.10
Optimization process and trading results for different markets
Market
Iterations
Last ξ
Profit
Drawdown
RRR
DAX30
10316
7.88
38552
10181
1:3.79
DJIA30
9622
2.96
37896
5769
1:6.56
FTSE100
11821
3.42
26207
9120
1:2.87
CAC40
9512
18.66
12259
7477
1:1.64
SMI20
9872
9.74
15843
10890
1:1.45
S&P500
10608
14.58
21151
10091
1:2.09
NASDAQ100
7581
3.28
39744
7143
1:5.56
NIKKEI225
12944
10.26
39362
9846
1:3.99
DJUSREI
9814
12.64
18239
9713
1:1.87
Spot Gold
6391
4.98
14549
9881
1:1.47
Brent Crude
7412
3.22
19207
9488
1:2.02
Coffee
9266
4.34
20416
9736
1:2.10
EURUSD
8322
9.28
10728
9380
1:1.14
GBPUSD
9371
4.12
8008
2413
1:3.32
BTCUSD
8722
9.64
9986
9716
1:1.02
Capital evolution between 01.01.2017 and 31.12.2018 due to the low-risk trading algorithm
DAX30
Index
Spot Gold
(XAUUSD)
Bitcoin
(BTCUSD)
Steps for optimization and implementation
of the low-risk trading algorithm (LRTA)
- price amplitude analysis for each market (ATR Average True Range)
- initializing the functional parameters according to the market ATR level
- functional parameters optimization for each market (gradient method)
- real-time application of low-risk trading algorithm with optimal parameters
- organizing a real-time machine-learning process to improve the parameters
- update the algorithm with the optimized values of the functional parameters
There are two major limitations:
- LTRA can not be applied for when the small take profit level θ < commissions
- With small timeframe, LRTA trades 20-25% of the time (40-45% for longer tf.)
Conclusions
- the Low-Risk Trading Algorithm (LRTA) can be applied for any capital market
- the LRTA parameters can be optimized with reasonable computational effort
- a real-time machine learning procedure can adapt LRTA to market behavior
- better results are obtained for high liquidity markets with low commissions
- LRTA can not be applied if the profit target is comparable with the commission
- for small timeframes used, LRTA will trade for about 20-25% of the total time
- for high liquidity markets higher timeframes can be used to improve the efficiency
- as a result of the RRR levels obtained, LRTA is a reliable trading algorithm
Email: cristian.pauna@ie.ase.ro
Phone: +407.4003.0000
Thank you!
Cristian Păuna
Paper co-financed by
Algorithm Invest SRL
https://algoinvest.biz
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