Even so, "Trading Systems and Methods - 6th Ed" by P. Kaufman would be a good place to start.
Where to begin?
- Thread starter blackriver
- Start date
Even so, "Trading Systems and Methods - 6th Ed" by P. Kaufman would be a good place to start.
Thank you very much. Do you recommend any of these brokers for simple stock trading in the Dow and NASDAQ?
Jim
Given the above it appears that you already have an idea for the method that you want to start with.
What specifically do you lack toward implementing an initial prototype version of your auto trading program?
By the way, Break a Leg!
I would say all of such APIs can do this, as it's the most basic thing to do via API.
The advantage of the TD Ameritrade API is that it does not need any other program;
it simply uses the standard web-protocol (ie. HTTP/1.1 with encryption, ie. https).
I find this solution easy and flexible, easy to setup and maintain, less work etc.
Of course it will take some time (some weeks/months) to test and get familiar with the API in practice.
Of course there are also further brokers with APIs beyond the 2 or 3 I gave as an example.
Just google for "trading broker API" etc.
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Easymon1, I lack the knowhow about trading platforms, how to make my algorithm work with them, how to communicate with the stock market for getting archives of data for backtesting, and then for making trades. Probably other stuff too.
Put in a little time on a search engine session and you'll be cooking in no time.
search term: Trading platforms, how to make my algorithm work with them
https://www.dogpile.com/serp?q=trading+platforms%2C+how+to+make+my+algorithm+work+with+them
How to make my trading algorithm work with Interactive Brokers (etc)
https://www.dogpile.com/serp?q=How+to+make+my+trading+algorithm+work+with+interactive+brokers
Interactive Brokers API
https://www.dogpile.com/serp?q=Interactive+Brokers+API
Key Features:
IBKR's proprietary, open-source API
Place orders, view trades and positions, access market data, news and account data
Connect through our proprietary Trader Workstation or IB Gateway platform
Develop applications in C++, C#, Java, Python, ActiveX, RTD or DDE
Available via leased line, cross-connect or internet
TD Ameritrade API
https://www.dogpile.com/serp?q=TD+Ameritrade+API
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The Hamilton-Jacobi-Bellman (HJB) equation is a partial differential equation that arises in the field of optimal control theory. It is used to find the optimal control actions for a given dynamic system by minimizing a certain cost or maximizing a certain reward. The HJB equation represents the value function
V(t, x) = min_u { L(t, x, u) + ∂V(t, x)/∂t + ∇V(t, x) • f(t, x, u) }
where:
V(t, x) is the value function, which represents the expected cost or reward at time t and state x.
u represents the control variable or action (e.g., buying or selling stocks).
L(t, x, u) is the running cost or instantaneous reward associated with a given state x, control action u, and time t.
∂V(t, x)/∂t is the partial derivative of the value function with respect to time.
∇V(t, x) is the gradient of the value function with respect to the state variables x.
f(t, x, u) represents the dynamics of the system, describing how the state variables x evolve over time given the control actions u.
The "min" operator indicates that the goal is to minimize the total cost or maximize the total reward.
The HJB equation can be potentially applied to improve one's ability to trade stocks profitably and consistently by formulating the stock trading problem as an optimal control problem. Here, the objective is to maximize the expected profit over time while considering transaction costs, taxes, and other factors that might affect the overall profitability.
To apply the HJB equation to stock trading, one needs to:
Once the problem is formulated, the HJB equation can be used to find the optimal trading strategy. By solving the HJB equation, one can obtain the value function and the corresponding optimal control policy. This policy dictates the best action to take at any given time, given the current state of the market and the trader's holdings.
However, it is important to note that applying the HJB equation to real-world stock trading is challenging due to several factors:
V(t, x) = min_u { L(t, x, u) + ∂V(t, x)/∂t + ∇V(t, x) • f(t, x, u) }
where:
V(t, x) is the value function, which represents the expected cost or reward at time t and state x.
u represents the control variable or action (e.g., buying or selling stocks).
L(t, x, u) is the running cost or instantaneous reward associated with a given state x, control action u, and time t.
∂V(t, x)/∂t is the partial derivative of the value function with respect to time.
∇V(t, x) is the gradient of the value function with respect to the state variables x.
f(t, x, u) represents the dynamics of the system, describing how the state variables x evolve over time given the control actions u.
The "min" operator indicates that the goal is to minimize the total cost or maximize the total reward.
The HJB equation can be potentially applied to improve one's ability to trade stocks profitably and consistently by formulating the stock trading problem as an optimal control problem. Here, the objective is to maximize the expected profit over time while considering transaction costs, taxes, and other factors that might affect the overall profitability.
To apply the HJB equation to stock trading, one needs to:
- Define the state variables: These can include stock prices, cash balance, stock holdings, and other relevant market information.
- Define the control variables: These are the actions that the trader can take, such as buying, selling, or holding stocks.
- Define the dynamics: This includes modeling how the state variables evolve over time, taking into account market factors and the trader's control actions.
- Define the objective function: This is the expected profit or reward that the trader aims to maximize over time, considering factors such as transaction costs, taxes, and risk.
Once the problem is formulated, the HJB equation can be used to find the optimal trading strategy. By solving the HJB equation, one can obtain the value function and the corresponding optimal control policy. This policy dictates the best action to take at any given time, given the current state of the market and the trader's holdings.
However, it is important to note that applying the HJB equation to real-world stock trading is challenging due to several factors:
- High-dimensional state space: The state variables in stock trading problems can be numerous and continuous, leading to a complex and high-dimensional state space.
- Uncertainty and non-stationarity: Stock prices and market conditions are often affected by unpredictable events, making the problem non-stationary and difficult to model accurately.
- Computational complexity: Solving the HJB equation, especially for high-dimensional problems, can be computationally intensive and may require advanced numerical techniques.
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