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

Authors:
• Algorithm Invest

## Abstract

based on the price cyclicality
function for capital markets.
Cristian Păuna
Economic Informatics Doctoral School
Paper co-financed by
Algorithm Invest SRL
https://algoinvest.biz
The 13th International Conference
21-22 March 2019
Bucharest, Romania
This paper presents:
- 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
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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