Journal of Intelligent Learning Systems and Applications, 2012, 4, 279-284
http://dx.doi.org/10.4236/jilsa.2012.44029 Published Online November 2012 (http://www.SciRP.org/journal/jilsa)
279
Optimization of Intraday Trading Strategy Based on ACD
Rules and Pivot Point System in Chinese Market
Xue Tian1, Cong Quan2, Jun Zhang3 , H. J. Cai3
1School of Economics and Management, Wuhan University, Wuhan, China; 2College of Software Technology, South China Agricul-
tural University, Guangdong, China; 3International School of Software, Wuhan University, Wuhan, China.
Received May 15th, 2012; revised July 3rd, 2012; accepted July 10th, 2012
ABSTRACT
Various trading strategies are applied in intraday high-frequency market to provide investors with reference signals to
be on the right side of market at the right time. In this paper, we apply a trading strategy based on the combination of
ACD rules and pivot points system, which is first proposed by Mark B. Fisher, into Chinese market. This strategy has
been used by millions of traders to achieve substantial profits in the last two decades, however, discussions concerning
on the methods of calculating specific entry point in this trading strategy are rare, which is crucial to this strategy. We
suggest an improvement to this popular strategy, providing the calculating and optimizing methods in detail to verify its
effectiveness in recent Chinese futures market. Because of the high liquidity and low commissions in stock index fu-
tures market, this trading strategy achieves substantial profits. However, given the less liquidity in commodity futures
market, profits decrease and even be neutralized by the relatively high commissions.
Keywords: ACD Rules; Pivot Point System; Pivot Range; Optimization
1. Introduction
China has experienced rapid development on stock index
and commodity futures market in recent years, and vari-
ous studies concerning on high frequency data to achieve
profits are springing out, discussions of which range
from arbitrage opportunity [1] to wave theory [2]. Accu-
rate predictions of price movements in futures market
will bring large profits in trading, which has become a
challenging task for investors and a focus for academi-
cians. Quantitative methods and techniques have been
widely applied to forecast price trend. The Polynomial
Classifiers and back propagation feed forward neural net-
works models were conducted on Dubai Financial Mar-
ket to forecast securities’ prices movements [3]. Regres-
sion and Neural Network models are used for predicting
Shanghai Composite Index returns and price volatility [4].
More models such as ARMA-GARCH, recurrent SVM
and recurrent RVM are also applied for forecasting [5].
V. Pacelli did deep empirical studies on Artificial Neural
Network to forecast exchange rates and credit risk man-
agement [6,7], and also developed this model for further
research [8]. Various kinds of Neural Network models
are famous for their advantages of adaptability to com-
plex situations, suiting to reveal hidden relationships that
govern the data. However, Neural Network models still
subject to the risk of local minima, especially in models
with more three layers. The data ratio for model training
and forecasting is always set empirically, unable to real-
ize automatically renewal. The model we introduce in
this paper deals with this limits well, as the combination
of parameters, including data ratio for training, is renew-
ed in time according to market data to seek optimums
continually and maximize profits.
In this paper, we introduce an improvement to a popu-
lar intraday trading strategy based on ACD rules and the
pivot point system. The basic premise of the strategy is
that the price range and trend established during the first
few minutes of market open sets the tone for the rest of
the day in the sense of making high and low price for the
full day already observable, with much greater likelihood
than chance. ACD rules trading strategy was first created
by Mark. B Fisher, a famous American professor of trend
trading. It provides entry and exit points for long or short
intraday. Mark. B Fisher used this trading method for
more than 20 years and introduced it to the public in his
book “The Logic Trader: applying a method to the mad-
ness” [9].
Among most investors who had applied it to their tra-
ding system, their profits proved its effectiveness. Since
then, many related studies concerning with this method
comes out [10,11]. Moreover, part of this trading strat-

Optimization of Intraday Trading Strategy Based on ACD Rules and Pivot Point System in Chinese Market
280
egy, the pivot point system can derivate various com-
plicate technique analysis [12,13], including prevail 5
points system, 7 points system and 13 points system. Now
we only care about the pivot range in this system to com-
bine with the ACD rules to forecast the formation of pri-
ce trend as well as mood and atmosphere of the market,
providing investors with reference entry and exit points.
In ACD rules, we usually choose the high and low
price of the first 5 - 20 minutes as the upper and lower
bounds of opening range. Aup or Adown point is on or
below the bounds of opening range for a specific distance.
When the price stays above Aup point for half of the time
domain of opening range, the Aup point is established
successfully and the stop-loss point B is the lower bound
of opening range in this case. After Aup point has been
established, the reverse entry point is C. It is the turning
point of the market atmosphere, from bullish to bearish
or from bearish to bullish. It indicates a strong change in
market trends, providing investors with signal about how
to trade. It is away from the bounds of opening range for
some larger distance than that of A. After Aup point has
been established, if the price falls strongly instead of
increasing, reaching Cdown point, then investors should
establish a clear preference for short at this time, seizing
the market downward trend to get profits as much as
possible. In this case, the stop-loss point, D, is one tick
above the upper bound of opening rang. Situation on the
other side of the market is symmetric.
The other part of this trading strategy, pivot point sys-
tem, is mainly used to determine the price support or
resistance zone. The relative places of these zones and
the last close price forecast the market mood of next day.
Combining both in trading, double signals from them
increase investors’ confidence to maximize positions to
get more profits. It is also helpful to refine the stop-loss
point B and D to minimize loss.
However, In the process of implementing this trading
strategy, the most crucial and difficult problem for each
investor is how to determine the right place of entry point
A and C. In 2010, a report from Guosen applied the
trading strategy that based on ACD rules and pivot point
system into stock index futures in Chinese market and
realized substantial excess profits [14]. However, it fails
to talk about methods for calculating the place of A and
C, which is unique for each future in different market. In
this paper, we focus on improving this strategy, discuss-
ing how to determine the place of these two important
entry points, which are determined by two important pa-
rameters. We program this trading strategy to find the
optimal combination of parameters. In empirical study,
we apply this auto-trading system into stock index and
commodity futures market in China to prove its effec-
tiveness.
2. An Empirical Example
We choose a commodity future, SR1209, to show how to
apply this method to intraday trading in details. Take the
commodity futures SR1209, on Jan 5th, for example. We
take the average of the previous 4 transaction days’ range
(high-low), and use 12% of this value as the distance
between Aup/Adown and the upper/lower bound of the
opening range. On Jan 5th, the highest and lowest prices
of the first 20 minutes are 6202 and 6102 respectively.
So the opening range of this transaction day is 6102 -
6202, r2 - r1 in Figure 1(a).
The pivot range (not present in figures) in pivot point
system is calculated by the high(H), low(L) and close
price(C) of the previous one transaction day, namely Jan
4th.
Specific formulas are as follows: (PP: pivot price; SV:
the second value; PD: pivot difference; PR: pivot range)



PPH+L+C 3
SVH+L 2
PPSV = PD
PP / PDPR



 
Figure 1(a) shows that the price breaks Aup level at
9:57. From then on, the program starts to calculate time.
Once the price stays above Aup level for more than 10
minutes, it will prompt to open long positions.
Then the time goes to 10:06 and the price has stayed
above Aup level for just 10 minutes. It will prompt
investors to open long positions (“upa” point in Figure
1(a)). The price of this moment is 6228. Since then, the
program will judge the point where to close the long
positions. If the price rises continually, then investors can
sell them after 10 ticks rise. According to history data of
sugar trading, we set tick as 1. Of course, the amount of
tick may not be fixed. It varies in accordance with price
range of certain futures and investors preference How-
ever, if price falls. Instead of increasing, investors should
close the long position at stop-loss point. Combing ACD
rules and the pivot point system, the stop-loss point B in
this case is the higher one between the lower bound of
opening range, 6028 and that of pivot range, 6102. So B
is 6102 in this example. If neither of these above two
situations occurs, then close them at the closing time, 15:
00.
From Figure 1(a), we see that price rises continually,
reaching 6243, more than 10 ticks, at 10:17. Then inve-
stors can close the long positions at this moment. Calcu-
lating profits by formula: (sell price-buy price)/sell price,
we get 0.241% gains.
Later, the price stays above A up level for the remain-
ing time of this day, the program will not prompt to open
long positions or short positions, for it is unlikely for
price to have another significant rise. However, if price

Optimization of Intraday Trading Strategy Based on ACD Rules and Pivot Point System in Chinese Market 281
falls below A up point, and goes above it, staying for suf-
ficient time again, it will prompt to open long positions
once more.
Next, in Figure 1(b), we give another example, CU-
1204 on Feb 1st, to show how to deal with the situation
on the other side of the market. The price falls below A
down level and stay below it for 10 minutes at 11:24.
The program will prompt to open short positions at this
place, price of which is 59,730.
Then the price decreases continually for more than 10
ticks at 11:27. Investors can close the short positions at
this place by price of 59,680, getting profits. After this
trading, we can see that the price have a small increase.
But it falls below A down level and stay below it for 10
minutes once more at 13:57. Investors can open short
positions again at this place and close them later.
Then, we can see clearly from trend that, it has no op-
portunity today after two transactions. Thus profit of this
day is 0.15% in total.
3. Optimization
We apply this trading strategy to stock index and com-
modity futures market in China to prove its effectiveness.
The core of this auto-trading system is to find the right
place of entry point A and C precisely. From the example
in Part2, we see that the place of A point for different
futures are determined by two major parameters, the
(a)
(b)
Figure 1. Trading process.
number of the past transaction days we take to get aver-
age range, called n, and the percent of that value we use,
called P. Since C is the turning point of market mood,
trade opportunity occurring at point C is so rare that it
has little influence on profits. In our study, we set the
distance between C up/down and the upper/lower bound
of the opening range two times of that of A.
In this trading model, we need high frequent price data
of the first 20 days to train this model, getting the opti-
mal combination of parameters, n and P. They will be
used to calculate the proper place of entry point A for the
next day (See Figure 2).
For training model, we select all combinations of n
and P within their effective range and calculating profits
of each combination as approach mentioned in Part 2. All
these results gathered, we draw them in one 3 dimension
draft to look for the highest point in the direction of prof-
its. The corresponding coordinate is the optimal combi-
nation of n and P.
Here, we take IF1206 in stock index futures market as
an example to look for the optimal combination of n and
P. Using 1-minute price data from Dec. 22th to Jan. 20th
to train this trading model and get the optimal parameters.
The result is shown in Figure 3.
We can see clearly that the optimal combination of n
and P for this commodity is to take the first 18 transac-
tion days’ average range and use 0.17 of this value as the
Figure 2. Algorithm flow chart.

Optimization of Intraday Trading Strategy Based on ACD Rules and Pivot Point System in Chinese Market
282
distance between A and the opening range.
These optimal parameters are to be used to determine
the entry points for long and short of the next day, Jan.
30th. Program will adjust these parameters automatically
through searching the optimal combination from the
former 20 days and calculate the specific place of A and
C for the next day constantly. Accumulative total profit
under this trading strategy from Jan. 30th to March 21st
is 2.02% and the number of trade is 36 in total, 19 for
long and 17 for short. Daily profits in these days are
shown in Figure 4. Given 0.5% unilateral commission
for stock index futures trading, net profit is 1.66%. Tak-
ing the 5 - 6 times leverage level in Chinese stock index
futures market, actual profits for investors can reach
about 10% in 36 days.
Vast experiments on data shows that futures with large
volumes, high-frequency turnovers and good fluidity suit
this strategy better, for it reduces possibility of jumps
(See Table 1). With less jumps, the price range and trend
established during the first few minutes of market open
can sets the tone for the rest of the day, making high and
Figure 3. 3-dimentional accumulative profits for parameter
optimization.
Figure 4. Daily profits of IF1206 from Jan. 30th to March
21st.
low price for the full day already observable, with much
greater likelihood than chance. We expand this trading
strategy to the front month contract and the first back
month contract in recent commodity futures market. Ta-
ble 2 shows accumulative total profit from Jan. 19th to
Apr. 19th and Table 3 shows accumulative total profit
from Apr. 9th to May. 2nd.
Table 1. Trading details of IF1206.
date profit n P long shorttotal
2012/1/30 0 150.15 0 0 0
2012/1/31 0 150.15 0 0 0
2012/2/03 0.001737150.15 1 0 1
2012/2/06 0 150.15 0 0 0
2012/2/07 0.001035150.15 0 1 1
2012/2/08 0.001112150.15 1 0 1
2012/2/09 0 9 0.25 0 0 0
2012/2/10 0.0026329 0.25 2 0 2
2012/2/13 0 9 0.25 0 0 0
2012/2/14 0 9 0.25 0 0 0
2012/2/15 0.0027899 0.25 3 0 3
2012/2/16 0.0017279 0.25 0 1 1
2012/2/17 0 9 0.25 0 0 0
2012/2/20 0 9 0.25 0 0 0
2012/2/21−0.005939 0.25 0 2 2
2012/2/22 0.000687130.18 1 0 1
2012/2/23 0 130.18 0 0 0
2012/2/24 0.001429130.18 2 0 2
2012/2/27 0.000515130.18 1 0 1
2012/2/28 0.000519130.18 0 1 1
2012/2/29 0.000373130.18 0 1 1
2012/3/01−0.00223130.18 2 0 2
2012/3/02 0.002227130.18 2 0 2
2012/3/05 0 130.18 0 0 0
2012/3/06 0.000896130.18 0 1 1
2012/3/07 0 130.18 0 0 0
2012/3/08 0.002104130.18 2 0 2
2012/3/09 0.001116150.17 1 0 1
2012/3/12 0.00157 150.17 0 2 2
2012/3/13 0.000445150.17 1 0 1
2012/3/14 0 120.17 0 0 0
2012/3/15 0.000608120.17 0 1 1
2012/3/16 0.0005354 0.18 0 1 1
2012/3/19 0 4 0.18 0 0 0
2012/3/20 0.0022814 0.18 0 3 3
2012/3/21 0.0019864 0.18 0 3 3

Optimization of Intraday Trading Strategy Based on ACD Rules and Pivot Point System in Chinese Market 283
Table 2. Total profit from Jan. 19th to Apr. 19th.
profit long short total
L1205 0.0135 15 23 38
RU1205 0.0019 12 13 25
CF1205 0.0125 17 11 28
TA1205 −0.0056 17 12 29
SR1209 0.0041 13 14 27
AU1206 −0.0028 19 10 29
Table 3. Total profit from Mar. 8th to May. 2nd.
profit long short total
PVC1209 0.0093 1 5 6
M1209 −0.0015 3 3 6
Y1209 0.0014 2 2 4
CU1206 0.0006 4 4 8
ZN1206 0.0062 5 3 8
RB1210 0.0067 1 9 10
WS1209 0.0169 0 9 9
WT1209 0.0085 5 10 15
C1209 0.0029 4 10 14
P1209 0.0039 1 2 3
Empirical studies on Chinese stock index futures mar-
ket have verified effectiveness of this improvement on
this trading strategy because of the higher liquidity and
lower commissions. But when transfer it to commodity
futures market, since commissions in this market is rela-
tively higher, about 5%, net profits are not optimistic for
many futures, at least in recent Chinese market.
Also in research, we find that the number of previous
transaction days we take and the percent of the value we
use are related with the number of trading. The shorter
time and smaller value percent we use, the more trading
we do. So the possibility of getting profits increase. How-
ever, we should determine the parameter combinations
we use taking commissions into consideration. If it is re-
latively high, investors should be cautious to open posi-
tions, ensuring to balance the profits and costs. Longer
time and larger value percent are recommended. The op-
posite situation also holds.
Those equities that work best for ACD are highly vo-
latile, very liquid (lots of daily trading volume), and sub-
ject to long trends. Moreover, we will expand this me-
thod to ETF funds, foreign exchange and other financial
derivatives which have good fluidity and high-frequency
turnovers in Chinese market to get further conclusions of
this trading strategy.
4. Conclusion
In this paper, we suggest an improvement on a famous
trading strategy based on ACD rules and pivot point sys-
tem, which was proposed by Mark B. Fisher, to futures
market in China. Among studies concerning this strategy,
discussions on the calculating method of the place of
entry point A and C are rare. We expand this study, fo-
cusing on finding the optimal combination of parameters
for entry point A. In program, we select every possible
combination of the number of past transaction days we
take to get average range, namely n, and the percent
value P, to get accumulate profits under each. For each
day, program will train the model by high frequency data
of the former 20 days, getting an optimal combination of
parameters for the next transaction day, to determine the
distance of A point away the bounds of opening range.
This process will be repeated before market open each
day automatically by program to adjust optimal parame-
ters constantly. Empirical studies of this trading strategy
in recent Chinese stock index futures market achieve
substantial profits and verify its effectiveness, given to its
high liquidity and low commissions. While in Chinese
commodity futures market it fails to achieve sustainable
net profits for most kinds in recent market conditions.
Given the ineffectiveness in recent Chinese commodity
futures market, investors should be cautious to apply this
trading strategy. We will expand our study as Chinese
futures market is maturing and having higher volumes
and liquidity to get further conclusion about its effective-
ness.
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