Markovchain — 指標和策略

Markov Chain Trend Probability A Markov Chain is a mathematical model that predicts future states based on the current state, assuming that the future depends only on the present (not the past). Originally developed by Russian mathematician Andrey Markov, this concept is widely used in: Finance: Risk modeling, portfolio optimization, credit scoring, algorithmic trading Weather Forecasting: Predicting sunny/rainy days, temperature patterns, storm tracking Here's an example of a Markov chain: If the weather is sunny, the probability that will be sunny 30 min later is say 90%. However, if the state changes, i.e. it starts raining, how the probability that will be raining 30 min later is say 70% and only 30% sunny. Similar concept can be applied to markets price action and trends. Mathematical Foundation The core principle follows the Markov Property: P(X_{t+1}|X_t, X_{t-1}, ..., X_0) = P(X_{t+1}|X_t) Transition Matrix : -------------Next State Current---- --------P11 P12 -----P21 P22 Probability Calculations: P(Up→Up) = Count(Up→Up) / Count(Up states) P(Down→Down) = Count(Down→Down) / Count(Down states) Steady-state probability: π = πP (where π is the stationary distribution) State Definition: State = UPTREND if (Price_t - Price_{t-n})/ATR > threshold State = DOWNTREND if (Price_t - Price_{t-n})/ATR < -threshold How It Works in Trading This indicator applies Markov Chain theory to market trends by: Defining States: Classifies market conditions as UPTREND or DOWNTREND based on price movement relative to ATR (Average True Range) Learning Transitions: Analyzes historical data to calculate probabilities of moving from one state to another Predicting Probabilities: Estimates the likelihood of future trend continuation or reversal How to Use Parameters: Lookback Period: Number of bars to analyze for trend detection (default: 14) ATR Threshold: Sensitivity multiplier for state changes (default: 0.5) Historical Periods: Sample size for probability calculations (default: 33) Trading Applications: Trend confirmation for entry/exit decisions Risk assessment through probability analysis Market regime identification Early warning system for potential trend reversals The indicator works on any timeframe and asset class. Enjoy!

[ALGOA+] Markov Chains Library by @metacamaleo Library "MarkovChains" Markov Chains library by @metacamaleo. Created in 09/08/2024. This library provides tools to calculate and visualize Markov Chain-based transition matrices and probabilities. This library supports two primary algorithms: a rolling window Markov Chain and a conditional Markov Chain (which operates based on specified conditions). The key concepts used include Markov Chain states, transition matrices, and future state probabilities based on past market conditions or indicators. Key functions: - `mc_rw()`: Builds a transition matrix using a rolling window Markov Chain, calculating probabilities based on a fixed length of historical data. - `mc_cond()`: Builds a conditional Markov Chain transition matrix, calculating probabilities based on the current market condition or indicator state. Basically, you will just need to use the above functions on your script to default outputs and displays. Exported UDTs include: - s_map: An UDT variable used to store a map with dummy states, i.e., if possible states are bullish, bearish, and neutral, and current is bullish, it will be stored in a map with following keys and values: "bullish", 1; "bearish", 0; and "neutral", 0. You will only use it to customize your own script, otherwise, it´s only for internal use. - mc_states: This UDT variable stores user inputs, calculations and MC outputs. As the above, you don´t need to use it, but you may get features to customize your own script. For example, you may use mc.tm to get the transition matrix, or the prob map to customize the display. As you see, functions are all based on mc_states UDT. The s_map UDT is used within mc_states´s s array. Optional exported functions include: - `mc_table()`: Displays the transition matrix in a table format on the chart for easy visualization of the probabilities. - `display_list()`: Displays a map (or array) of string and float/int values in a table format, used for showing transition counts or probabilities. - `mc_prob()`: Calculates and displays probabilities for a given number of future bars based on the current state in the Markov Chain. - `mc_all_states_prob()`: Calculates probabilities for all states for future bars, considering all possible transitions. The above functions may be used to customize your outputs. Use the returned variable mc_states from mc_rw() and mc_cond() to display each of its matrix, maps or arrays using mc_table() (for matrices) and display_list() (for maps and arrays) if you desire to debug or track the calculation process. See the examples in the end of this script. Have good trading days! Best regards, @metacamaleo ----------------------------- KEY FUNCTIONS mc_rw(state, length, states, pred_length, show_table, show_prob, table_position, prob_position, font_size) Builds the transition matrix for a rolling window Markov Chain. Parameters: state (string) : The current state of the market or system. length (int) : The rolling window size. states (array) : Array of strings representing the possible states in the Markov Chain. pred_length (int) : The number of bars to predict into the future. show_table (bool) : Boolean to show or hide the transition matrix table. show_prob (bool) : Boolean to show or hide the probability table. table_position (string) : Position of the transition matrix table on the chart. prob_position (string) : Position of the probability list on the chart. font_size (string) : Size of the table font. Returns: The transition matrix and probabilities for future states. mc_cond(state, condition, states, pred_length, show_table, show_prob, table_position, prob_position, font_size) Builds the transition matrix for conditional Markov Chains. Parameters: state (string) : The current state of the market or system. condition (string) : A string representing the condition. states (array) : Array of strings representing the possible states in the Markov Chain. pred_length (int) : The number of bars to predict into the future. show_table (bool) : Boolean to show or hide the transition matrix table. show_prob (bool) : Boolean to show or hide the probability table. table_position (string) : Position of the transition matrix table on the chart. prob_position (string) : Position of the probability list on the chart. font_size (string) : Size of the table font. Returns: The transition matrix and probabilities for future states based on the HMM.

[SGM Markov Chain]Introduction A Markov chain is a mathematical model that describes a system evolving over time among a finite number of states. This model is based on the assumption that the future state of the system depends only on the current state and not on previous states, the so-called Markov property. In the context of financial markets, Markov chains can be used to model transitions between different market conditions, for example, the probability of a price going up after going up, or going down after going down. Script Description This script uses a Markov chain to calculate closing price transition probabilities across the entire accessible chart. It displays the probabilities of the following transitions: - Up after Up (HH): Probability that the price rises after going up. - Down after Down (BB): Probability that the price will go down after going down. - Up after Down (HB): Probability that the price goes up after going down. - Down after Up (BH): Probability that the price will go down after going up. Features - Color customization: Choose colors for each transition type. - Table Position: Select the position of the probability display table (top/left, top/right, bottom/left, bottom/right).

Markov Chain Trend Indicator Overview The Markov Chain Trend Indicator utilizes the principles of Markov Chain processes to analyze stock price movements and predict future trends. By calculating the probabilities of transitioning between different market states (Uptrend, Downtrend, and Sideways), this indicator provides traders with valuable insights into market dynamics. Key Features State Identification: Differentiates between Uptrend, Downtrend, and Sideways states based on price movements. Transition Probability Calculation: Calculates the probability of transitioning from one state to another using historical data. Real-time Dashboard: Displays the probabilities of each state on the chart, helping traders make informed decisions. Background Color Coding: Visually represents the current market state with background colors for easy interpretation. Concepts Underlying the Calculations Markov Chains: A stochastic process where the probability of moving to the next state depends only on the current state, not on the sequence of events that preceded it. Logarithmic Returns: Used to normalize price changes and identify states based on significant movements. Transition Matrices: Utilized to store and calculate the probabilities of moving from one state to another. How It Works The indicator first calculates the logarithmic returns of the stock price to identify significant movements. Based on these returns, it determines the current state (Uptrend, Downtrend, or Sideways). It then updates the transition matrices to keep track of how often the price moves from one state to another. Using these matrices, the indicator calculates the probabilities of transitioning to each state and displays this information on the chart. How Traders Can Use It Traders can use the Markov Chain Trend Indicator to: Identify Market Trends: Quickly determine if the market is in an uptrend, downtrend, or sideways state. Predict Future Movements: Use the transition probabilities to forecast potential market movements and make informed trading decisions. Enhance Trading Strategies: Combine with other technical indicators to refine entry and exit points based on predicted trends. Example Usage Instructions Add the Markov Chain Trend Indicator to your TradingView chart. Observe the background color to quickly identify the current market state: Green for Uptrend, Red for Downtrend, Gray for Sideways Check the dashboard label to see the probabilities of transitioning to each state. Use these probabilities to anticipate market movements and adjust your trading strategy accordingly. Combine the indicator with other technical analysis tools for more robust decision-making.

MarkovChain Library "MarkovChain" Generic Markov Chain type functions. --- A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. --- reference: Understanding Markov Chains, Examples and Applications. Second Edition. Book by Nicolas Privault. en.wikipedia.org www.geeksforgeeks.org towardsdatascience.com github.com stats.stackexchange.com timeseriesreasoning.com www.ris-ai.com github.com gist.github.com github.com gist.github.com writings.stephenwolfram.com kevingal.com towardsdatascience.com spedygiorgio.github.io github.com www.projectrhea.org method to_string(this) Translate a Markov Chain object to a string format. Namespace types: MC Parameters: this (MC) : `MC` . Markov Chain object. Returns: string method to_table(this, position, text_color, text_size) Namespace types: MC Parameters: this (MC) position (string) text_color (color) text_size (string) method create_transition_matrix(this) Namespace types: MC Parameters: this (MC) method generate_transition_matrix(this) Namespace types: MC Parameters: this (MC) new_chain(states, name) Parameters: states (state ) name (string) from_data(data, name) Parameters: data (string ) name (string) method probability_at_step(this, target_step) Namespace types: MC Parameters: this (MC) target_step (int) method state_at_step(this, start_state, target_state, target_step) Namespace types: MC Parameters: this (MC) start_state (int) target_state (int) target_step (int) method forward(this, obs) Namespace types: HMC Parameters: this (HMC) obs (int ) method backward(this, obs) Namespace types: HMC Parameters: this (HMC) obs (int ) method viterbi(this, observations) Namespace types: HMC Parameters: this (HMC) observations (int ) method baumwelch(this, observations) Namespace types: HMC Parameters: this (HMC) observations (int ) Node Target node. Fields: index (series int) : . Key index of the node. probability (series float) : . Probability rate of activation. state State reference. Fields: name (series string) : . Name of the state. index (series int) : . Key index of the state. target_nodes (Node ) : . List of index references and probabilities to target states. MC Markov Chain reference object. Fields: name (series string) : . Name of the chain. states (state ) : . List of state nodes and its name, index, targets and transition probabilities. size (series int) : . Number of unique states transitions (matrix) : . Transition matrix HMC Hidden Markov Chain reference object. Fields: name (series string) : . Name of thehidden chain. states_hidden (state ) : . List of state nodes and its name, index, targets and transition probabilities. states_obs (state ) : . List of state nodes and its name, index, targets and transition probabilities. transitions (matrix) : . Transition matrix emissions (matrix) : . Emission matrix initial_distribution (float )

FunctionProbabilityViterbi Library "FunctionProbabilityViterbi" The Viterbi Algorithm calculates the most likely sequence of hidden states *(called Viterbi path)* that results in a sequence of observed events. viterbi(observations, transitions, emissions, initial_distribution) Calculate most probable path in a Markov model. Parameters: observations (int ) : array . Observation states data. transitions (matrix) : matrix . Transition probability table, (HxH, H:Hidden states). emissions (matrix) : matrix . Emission probability table, (OxH, O:Observed states). initial_distribution (float ) : array . Initial probability distribution for the hidden states. Returns: array. Most probable path.

FunctionBaumWelch Library "FunctionBaumWelch" Baum-Welch Algorithm, also known as Forward-Backward Algorithm, uses the well known EM algorithm to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed feature vectors. --- ### Function List: > `forward (array pi, matrix a, matrix b, array obs)` > `forward (array pi, matrix a, matrix b, array obs, bool scaling)` > `backward (matrix a, matrix b, array obs)` > `backward (matrix a, matrix b, array obs, array c)` > `baumwelch (array observations, int nstates)` > `baumwelch (array observations, array pi, matrix a, matrix b)` --- ### Reference: > en.wikipedia.org > github.com > en.wikipedia.org > www.rdocumentation.org > www.rdocumentation.org forward(pi, a, b, obs) Computes forward probabilities for state `X` up to observation at time `k`, is defined as the probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`. Parameters: pi (float ) : Initial probabilities. a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states. b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given state matrix is size (M x O) where M is number of states and O is number of different possible observations. obs (int ) : List with actual state observation data. Returns: - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. forward(pi, a, b, obs, scaling) Computes forward probabilities for state `X` up to observation at time `k`, is defined as the probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`. Parameters: pi (float ) : Initial probabilities. a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states. b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given state matrix is size (M x O) where M is number of states and O is number of different possible observations. obs (int ) : List with actual state observation data. scaling (bool) : Normalize `alpha` scale. Returns: - #### Tuple with: > - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. > - `array _c`: Array with normalization scale. backward(a, b, obs) Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`. Parameters: a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations obs (int ) : Array with actual state observation data. Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. backward(a, b, obs, c) Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`. Parameters: a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations obs (int ) : Array with actual state observation data. c (float ) : Array with Normalization scaling coefficients. Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. baumwelch(observations, nstates) **(Random Initialization)** Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step. Parameters: observations (int ) : List of observed states. nstates (int) Returns: - #### Tuple with: > - `array _pi`: Initial probability distribution. > - `matrix _a`: Transition probability matrix. > - `matrix _b`: Emission probability matrix. --- requires: `import RicardoSantos/WIPTensor/2 as Tensor` baumwelch(observations, pi, a, b) Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step. Parameters: observations (int ) : List of observed states. pi (float ) : Initial probaility distribution. a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations Returns: - #### Tuple with: > - `array _pi`: Initial probability distribution. > - `matrix _a`: Transition probability matrix. > - `matrix _b`: Emission probability matrix. --- requires: `import RicardoSantos/WIPTensor/2 as Tensor`

FunctionSMCMC Library "FunctionSMCMC" Methods to implement Markov Chain Monte Carlo Simulation (MCMC) markov_chain(weights, actions, target_path, position, last_value) a basic implementation of the markov chain algorithm Parameters: weights : float array, weights of the Markov Chain. actions : float array, actions of the Markov Chain. target_path : float array, target path array. position : int, index of the path. last_value : float, base value to increment. Returns: void, updates target array mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step. Parameters: weights : float array, weights of the Markov Chain. actions : float array, actions of the Markov Chain. start_value : float, base value to start simulation. n_iterations : integer, number of iterations to run. Returns: float array with path.

Function - simple* Markov Chain Monte Carlo Simulation (MCMC)Example function of a markov chain monte carlo simulation.