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.
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-risk tr ading algorithm
based on the price cy
function for capit
ormatics Doctor al School
Academy of E
Paper co-financed by
th International Conf erence
On Business Excellence
- 22 2019 March
This paper presents:
- a functional tr
- making multiple low-risk
- using the Price Cyclical
- and a varia
ble entry price gr adient
- with a combined e
xit level method
- applying Rela
tive Strength Inde x for
- higher profit in normal
- and lower pr
ofit for ov erbought price
It was f
- the present
ed algorithm has only
- six functional par
ameters tha t can be
for any capit al mark et with
a reasonable comput ational eff ort
a good capital e fficiency for
- stock mar kets
- commodities mark ets
- currency mark ets and
- cryptocurrency markets
- there are tw
o functional limita tions
ading algorithm based on the
function f or capital mark e ts.
is the index of the time in terval
is the index of the multiple en tries
is Price Cyclicality Function
is the limit for the price cyclic ality
is the current price le vel
j is the entry price gradien t
ρ and δ par ameter s
is the maximal number of tr ades
is the higher tak e profit lev el
is the lower t ake pr ofit level
is the Rela tive Strength Inde x
is the RSI ov erbought price level
is a functional paramet er
k a moving a verag e with M period
ma a moving a k verag e with m period
M > m
is the period for the PCY function
The Price Cyclicality Function was introdu
Păuna, C., Lungu, I. (2018).
Price Cyclicality Model for Financial Markets. Reliable Limit Conditions for Algorithmic T
Economic computation and economic cybernetics studies and research
olume 52 Issue 4/2018. ISSN: 18423264 –
Bucharest, Romania: Academy of Economic Studies
tion process and tr ading results f or differen t markets
between 01.01.2017 and 31.12.2018 due to the low-risk trading alg orithm
for optimiza tion and implemen tation
of the low-risk tr
ading algorithm (LRT A)
- price amplitude anal
ysis for each mark et (A TR – Aver age T rue Range)
- initializing the functional
paramet ers according t o the mark et A TR level
- functional par
ameters optimiz ation f or each marke t (gradient method)
- real-time applica
tion of low-risk tr ading algorithm with optimal par ameters
ing a real-time machine-learning pr ocess to improve the paramet ers
- update the a
lgorithm with the optimiz ed values of the functional parame ters
e two major limitations:
TRA can not be applied for when the small tak e profit lev el θ < commissions
- With small timefr
ame, LRT A trades 20-25% of the time (40-45% for longer tf .)
- the Low-Risk T
rading Algorithm (LRT A) can be applied for any capit al mark et
- the LRT
A paramet ers can be optimiz ed with reasonable comput ational eff ort
- a real-time machine learning
procedure can adapt LRT A to marke t behavior
results are obt ained for high liquidity markets with low commissions
A can not be applied if the pr ofit targ et is comparable with the commission
- for small time
frames used, LRT A will trade for about 20-25% of the total time
- for high liquidity
marke ts higher timeframes can be used to improve the efficiency
- as a result of the
RRR levels obtained, LR T A is a reliable trading al gorithm
Paper co-financed by
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SuperCont is an automated capital management system using artificial intelligence to invest in a wide range of capital markets. theServer can be used by any private or institutional investor in ord
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articular goal is to identify those conditions that can make possible stress less investment. ... [more] View project January 2017
This article deals with the real estate market under the current market conditions in the Czech Republic. The main objective of this article is to verify the stated hypothesis focused on the assessment of its own assets, quality of service and prioritization of some real estate agents, based on previous personal experience of the respondent through the use of a marketing survey. The problem
... [Show full abstract] encountered on this market is the lack of professionalism and the use of unfair practices by real estate agents, which leads to the dissatisfaction of clients as well as investors considering investing into the real estate market. Another problem in this area is that several authors divide financial markets into cash, equity, commodity and currency, but many fail to mention the real estate market or real estate assets. Nevertheless, most of the adult population in the Czech Republic has used, uses or is considering to use the real estate market in the future for their personal use. Even though a considerable amount of money is invested into the real estate market by clients and investors, it is not given as much attention as e.g. the stock market, while a number of buyers purchase housing for themselves using a mortgage, which has a maturity of tens of years. Read more September 2020 · Journal of Systems Science and Systems Engineering
We investigate the directional volatility and return network connectedness among stock, commodity, bond, currency and cryptocurrency markets. The period of study covers Feb 2006 until August 2018. We utilize and expand Diebold and Yilmaz (2014 2015) connectedness measurement; accordingly, in the variance decomposition structure, we use Hierarchical Vector Autoregression (HVAR) to estimate high
... [Show full abstract] dimensional networks more accurately. Our empirical results show that markets are highly connected, especially during 2008–2009. Asian stock markets are the net receiver of shocks, while European and American stock markets are the net transmitter of shocks to other markets. The pairwise connectedness results suggest that among stock markets, DAX-CAC 40, FTSE 100-CAC 40 and S&P 500-S&P_TSX index are more integrated through connectedness than the others. For other markets, WTI crude oil — Brent crude oil, 30-Year bond and 10-Year bond, Dollar Index futures-EUR/USD have notable connections. In terms of cryptocurrencies, they contribute insignificantly to other markets and are highly integrated with each other. Gold and cryptocurrencies seem to be good choices for investors to hedge during a crisis. Read more January 2013 · SSRN Electronic Journal
Price clustering was found to be a major artifact of security trading in financial markets over many decades. Several conjectures about the origin of round number effects have been put forward without a conclusive explanation. This study analyzes buy-sell imbalances in the German stock market (Xetra) and thereby complements studies from other markets and countries. The methodological approach is
... [Show full abstract] related to Bhattacharya, Holden, and Jacobsen (2012) who examine data from the New York Stock Exchange (NYSE). It turns out that round number effects in Germany are qualitatively similar to the US, but investors have other number preferences. More precisely, German investors do not focus on quarters as investors in the US, which could be related to the local currency and historical factors of these markets. By extending the methodology, I identify determinants of round number effects such as price level, tick size, and index membership. Furthermore, I analyze the development of buy-sell imbalances over time and show that effects have gradually weakened over the observation period. This can be interpreted as an increase in market efficiency. In order to differentiate between human and algorithmic traders, I use a unique data set from a regional stock exchange containing retail investor trades only. I find that biases of human investors do not decrease over time which disagrees with the identified development on Xetra, where a large and increasing share in overall trading volume is due to algorithmic traders. Thus the decreasing strength of round number effects is attributed to algorithmic trading. Since they are assumed to be unaffected by any form of number bias, the intensity of price clustering and other round number effects might be a proxy for the share of algorithmic trading in a limit order market. Read more Technical Report Full-text available January 2016
☒ Aim of the post.
The main aim of this post is to review some cycle charts of US Dow Jones and German Dax (US Dollar Index and Gold for qualitative comparation only), in order to obtain data about the current year 2016 [366 days; president election; II term of Obama(Dem.); '6' year of decade].
The cycles reviewed are the followings:
-) seasonal cycle (according to the month of year);
... [Show full abstract] cycle or 10y cycle (according to the last number of year);
-) president cycle or 4y cycle (according to the year position in relation to US elections);
-) president cycle at II-term (for presidents with 2 or 3 terms in US elections);
-) curve evolution of S&P500 'n' years ago (30y, 21y, 12y, 7y), according to some GANN's rules.
Moreover there is a complete statistics (1901-2010) for the monthly returns of Dow Jones (#4).
In order to complete these cycle data, in the second part of ths post there is also a link-platform to searches the key-prices of the ''GANN's Jannuary Rule-Effect'' and the '''December Low Rule''', as follows (#3):
-) "Watch the time periods January 2nd to 7th & 15th to 21st, each year, and note the high and low prices; until these high prices are crossed or low prices broken, consider the trend up or down."
-) '"When the Dow closes below its December closing low in the first quarter, it is frequently an excellent warning sign."'
With ticker change, it is possible to view the current situation for some benchmarks as follows.
These two rules are very interesting if integrated with a statistical approach to the stock markets, as seasonal-, monthly-, president-, president II term-, decade- cycles (see previous posts #1, #2).
Data do not take into account the volatility; data are purely statistical, and have not predictive value.
This review is qualitative only and not exhaustive of course.
☒ General stocks-benchmarks usefull for this post.
► main U.S. stocks-benchmarks (charts & notes: QQQ; ONEQ; DIA; OEF; SPY);
► global U.S. stocks-benchmarks (charts & notes: EUSA; ITOT; IWB; IWV; IYY; THRK);
► Global World stocks-benchmarks (charts & notes: ACWI; DGT; IOO; NYSE W.L.I.; VT).
✔ '5' years shows a monstre positive perfomance (see chart-1 in ref #1).
✔ Very important positive performances there are also in '8', '3', '4' years (see chart-1 in ref #1).
✔ '0' years, and also '1', '2', '7' are very dangerous years for long investors (see chart-1 in ref #1).
✔ '6' years shows the little spread between best vs. worst years (according to chart-1 in ref #1)
✔ The pre-election years are the best for Dow Jones and DAX (2015 was a pre-election year).
✔ Pre-election top area is in summer for Dow Jones & DAX (see also the bottom zones).
✔ Election (2016) years shows a positive price-bar for US Dow Jones, and a little positive price-bar for DAX.
✔ Election years are the bottom years in the president (4y) cycle, both for US Dow Jones and DAX; the pre-election years are the top.
✔ Election and midterm years, shows differences for Dow Jones vs. DAX (see the intra-annual curves).
✔ Election years top area are in (progressively) Apr., Sep., Nov., Dec., for Dow Jones; Mar.-Apr., Jul., Sep., for DAX.
✔ Election years low area are in Feb., May, Oct., for Dow Jones; Jan., Oct., Nov., Dec., for DAX.
✔ Years of II-III term president cycle shows bearish yearly bar in 2/10 [Eisenhower; Wilson].
✔ Years of II-III term president cycle shows bullish yearly bar in 2/10 [T.Roosevelt II; F.D.Roosevelt III].
✔ Years of II-III term president cycle shows a little volatility yearly bar (side-bearish, side-bullish, or side-only bars) in 4/10 (40%) [Clinton(-); Reagan(+); Nixon-Ford(+); Truman(negligible)].
✔ Years of II-III term president cycle shows collapsed yearly bar in 2/10 [Bush; F.D.Roosevelt II].
✔ Years of II-III term president cycle shows the following intra-annual tops (qualitative data): January (7 events); April (3), August (3).
✔ Years of II-III term president cycle shows the following intra-annual lows (qualitative data): February (5 events); October (6).
✔ 30y ago (positive price-bar): first lows in Jan. & second lows in Aug.Sep.Oct.; top-area in Jun.Jul.Aug.Sep.
✔ 21y ago (positive price-bar): lows in Jan.; tops in Dec.
✔ 12y ago (positive price-bar): lows in Aug.; first tops in Mar. & second tops in Dec.
✔ 07y ago (positive price-bar): lows in Mar.; tops in Dec.
✔ The best months are Jul., Jan., Apr. (returns: >1%); see September.
✔ The best 4 months with positive performances, or positive price-bars, >60%, are Dec., Jan., Jul., Mar; see September.
The following results are very interesting in order to obtain statistical data about the 2016 behaviour of Dow Jones.
✔ The '6' years of decade cycle shows the slightest volatility compared to other years of the decade.
✔ The '6' years is a statistically positive year, but it is the bottom of president (4y) cycle.
✔ In the years with II-III term of president cycle there are 5 years with negative yearly price-bars (2 collapsed years), and 4 with positive bars; one with negligible bars. This result confirm the volatility data.
✔ According to intra-yearly behaviour of president cycle and 30/21/12/7 years ago pattern, there is the following qualitative top/low map for 2016:
-) lows in Jan.+Feb. and Oct.;
-) tops in Apr. and Dec. (confirmed by monthly stats);
-) June-September is a large trading range or side zone.
✔ According to intra-yearly behaviour of decade cycle, there is the following qualitative top/low map for 2016:
-) lows in first quarter;
-) tops in Apr. and Dec. (confirmed by monthly stats);
-) II & III quarters are a large trading range or side zone.
☒ Rule LINK platform.
☒ GANN's Jannuary Rule platform.
☒ December Low Rule platform.
☒ Ticker list.
► Global World stocks-benchmarks: ACWI, DGT, $FAW, IOO, $NYL, VT.
► global U.S. stocks-benchmarks: EUSA, $SPSUPX, IWB, IWV, IYY, THRK.
► main U.S. stocks-benchmarks: QQQ, ONEQ, DIA, OEF, SPY.
► Biotech.: $BTK, $NBI, $DJUSPN, $DJUSBT, $GSPPHB, $DZO.
► Financial benchmarks: $W1FIN, $DJUSFN, $DJUSBK; $SPF, $BIX, $XBD, IAI, EUFN.
► US stocks-sectors: XLY, XLK, XLI, XLB, XLE, XLP, XLV, XLU, XLF.
► US tech.benchmarks: $COMPQ, $TXX, $IXT, $RITEC, $NDX, $PSE, $MLO, $NDXT, $MSH.
► CBOE Volatility & Option Indicators: $VIX, $VXO, $VXN, $RVX.
► European stocks-benchmarks: $E1DOW, $EUR, $STOX5E, $STOXX600, $DAX, $CAC, $MIB, $IBEX, $PSI, $ATG, $FTSE, $SMI, EDEN, EFNL, EIRL, ENOR, EPOL, ERUS, ESR, EWD, EWG, EWI, EWK, EWL, EWN, EWO, EWP, EWQ, EWU, EZU FBT, FEU, FEZ, IEUR, IEV.
► Japan stocks: $INJ, $JPDOW, $JPN, $XLJPNTR, $NIKK, $NK300, ITF.
► Currency futures/ETF: $USD, $XEU, $XJY, $XBP, $CDW, $XAD, $XSF, $ZAR.
► US Gov.Bond Prices: $USB, $UST, $USFV, $USTU.
► Commodity Indexes: $CRB, $DJAIG, $GKX, $GJX, $GYX, $GPX, $GVX, $WTIC, $BRENT, $NATGAS, WOOD, $BDI.
► Metals: $GOLD, $PLAT, $SILVER, $PALL, $COPPER, RJZ, JJM, JJP, WITE, LIT, REMX.
#1.- Salvatore SalVi Vicidomini, 2015. - Financial Markets Observatory Lab. Notes and charts about the seasonal, president and decade cycles of US Dow Jones Industrial Average Index and German DAX Index. - https://www.researchgate.net/publication/274601438
#2.- Salvatore SalVi Vicidomini, 2015. - Financial Markets Observatory Lab. GANN's January rule and December low rule for stock markets. - http://www.researchgate.net/publication/274635811
#3. Stock Trader's Almanac.
☒ Chart sources.
❖ RicercaFinanza, SeasonalCharts; StockCharts. View full-text Last Updated: 04 Jul 2022
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