code: def least_square_mc(
      sample_paths,
      exercise_times,
      payoff_fn,
      basis_fn,
      discount_factors=None,
      dtype=None,
      name=None,
  ):
      """Values Amercian style options using the LSM algorithm.
  
      The Least-Squares Monte-Carlo (LSM) algorithm is a Monte-Carlo approach to
      valuation of American style options. Using the sample paths of underlying
      assets, and a user supplied payoff function it attempts to find the optimal
      exercise point along each sample path. With optimal exercise points known,
      the option is valued as the average payoff assuming optimal exercise
      discounted to present value.
  
      #### Example. American put option price through Monte Carlo
      ```python
      # Let the underlying model be a Black-Scholes process
      # dS_t / S_t = rate dt + sigma**2 dW_t, S_0 = 1.0
      # with `rate = 0.1`, and volatility `sigma = 1.0`.
      # Define drift and volatility functions for log(S_t)
      rate = 0.1
      def drift_fn(_, x):
        return rate - tf.ones_like(x) / 2.
      def vol_fn(_, x):
        return tf.expand_dims(tf.ones_like(x), -1)
      # Use Euler scheme to propagate 100000 paths for 1 year into the future
      times = np.linspace(0., 1, num=50)
      num_samples = 100000
      log_paths = tf.function(tff.models.euler_sampling.sample)(
              dim=1,
              drift_fn=drift_fn, volatility_fn=vol_fn,
              random_type=tff.math.random.RandomType.PSEUDO_ANTITHETIC,
              times=times, num_samples=num_samples, seed=42, time_step=0.01)
      # Compute exponent to get samples of `S_t`
      paths = tf.math.exp(log_paths)
      # American put option price for strike 1.1 and expiry 1 (assuming actual day
      # count convention and no settlement adjustment)
      strike = [1.1]
      exercise_times = tf.range(times.shape[-1])
      discount_factors = tf.exp(-rate * times)
      payoff_fn = make_basket_put_payoff(strike)
      basis_fn = make_polynomial_basis(10)
      lsm_price(paths, exercise_times, payoff_fn, basis_fn,
                discount_factors=discount_factors)
      # Expected value: [0.397]
      # European put option price
      tff.black_scholes.option_price(volatilities=[1], strikes=strikes,
                                     expiries=[1], spots=[1.],
                                     discount_factors=discount_factors[-1],
                                     is_call_options=False,
                                     dtype=tf.float64)
      # Expected value: [0.379]
      ```
      #### References
  
      [1] Longstaff, F.A. and Schwartz, E.S., 2001. Valuing American options by
      simulation: a simple least-squares approach. The review of financial studies,
      14(1), pp.113-147.
  
      Args:
        sample_paths: A `Tensor` of shape `[num_samples, num_times, dim]`, the
          sample paths of the underlying ito process of dimension `dim` at
          `num_times` different points.
        exercise_times: An `int32` `Tensor` of shape `[num_exercise_times]`.
          Contents must be a subset of the integers `[0,...,num_times - 1]`,
          representing the ticks at which the option may be exercised.
        payoff_fn: Callable from a `Tensor` of shape `[num_samples, num_times, dim]`
          and an integer scalar positive `Tensor` (representing the current time
          index) to a `Tensor` of shape `[num_samples, payoff_dim]`
          of the same dtype as `samples`. The output represents the payout resulting
          from exercising the option at time `S`. The `payoff_dim` allows multiple
          options on the same underlying asset (i.e., `samples`) to be valued in
          parallel.
        basis_fn: Callable from a `Tensor` of shape `[num_samples, dim]` to a
          `Tensor` of shape `[basis_size, num_samples]` of the same dtype as
          `samples`. The result being the design matrix used in regression of the
          continuation value of options.
        discount_factors: A `Tensor` of shape `[num_exercise_times]` or
          `[num_samples, num_exercise_times]`and the same `dtype` as `samples`,
          the k-th element of which represents the discount factor at time tick `k`.
          Default value: `None` which maps to a one-`Tensor` of the same `dtype`
            as `samples` and shape `[num_exercise_times]`.
        dtype: Optional `dtype`. Either `tf.float32` or `tf.float64`. The `dtype`
          If supplied, represents the `dtype` for the input and output `Tensor`s.
          Default value: `None`, which means that the `dtype` inferred by TensorFlow
          is used.
        name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` which is mapped to the default name
          'least_square_mc'.
      Returns:
        A `Tensor` of shape `[num_samples, payoff_dim]` of the same dtype as
        `samples`.
      """
      with tf.compat.v1.name_scope(
          name,
          default_name="least_square_mc",
          values=[sample_paths, exercise_times],
      ):
          # Conversion of the inputs to tensors
          sample_paths = tf.convert_to_tensor(
              sample_paths, dtype=dtype, name="sample_paths"
          )
          exercise_times = tf.convert_to_tensor(
              exercise_times, name="exercise_times"
          )
          num_times = exercise_times.shape.as_list()[-1]
          if discount_factors is None:
              discount_factors = tf.ones(
                  shape=exercise_times.shape,
                  dtype=sample_paths.dtype,
                  name="discount_factors",
              )
          else:
              discount_factors = tf.convert_to_tensor(
                  discount_factors, dtype=dtype, name="discount_factors"
              )
              if discount_factors.shape.rank == 1:
                  discount_factors = tf.expand_dims(
                      discount_factors, axis=0
                  )
  
          discount_factors = tf.concat(
              [
                  tf.ones_like(
                      tf.expand_dims(discount_factors[..., 0], axis=-1)
                  ),
                  discount_factors,
              ],
              axis=-1,
          )
  
          # Initialise cashflow as the payoff at final sample.
          tick = exercise_times[num_times - 1]
          # Calculate the payoff of each path if exercised now. Shape
          # [num_samples, payoff_dim]
          exercise_value = payoff_fn(sample_paths, tick)
          zeros = tf.zeros(
              exercise_value.shape + [num_times - 1],
              dtype=exercise_value.dtype,
          )
          exercise_value = tf.expand_dims(exercise_value, -1)
  
          # Shape [num_samples, payoff_dim, num_exercise]
          cashflow = tf.concat([zeros, exercise_value], -1)
          # Starting state for loop iteration.
          lsm_loop_vars = LsmLoopVars(
              exercise_index=num_times - 1, cashflow=cashflow
          )
  
          def loop_body(exercise_index, cashflow):
              return _lsm_loop_body(
                  sample_paths,
                  exercise_times,
                  discount_factors,
                  payoff_fn,
                  basis_fn,
                  num_times,
                  exercise_index,
                  cashflow,
              )
  
          loop_value = tf.while_loop(
              lsm_loop_cond,
              loop_body,
              lsm_loop_vars,
              maximum_iterations=num_times,
          )
          present_values = continuation_value_fn(
              loop_value.cashflow, discount_factors, 0
          )
          return tf.math.reduce_mean(present_values, axis=0)
  
file_path: tf-quant-finance/tf_quant_finance/experimental/lsm_algorithm/lsm.py
imports: import collections
  import tensorflow.compat.v2
label_desc: Use image node_id 2 for calling a global function with example usage: least_square_mc(sample_paths, exercise_times, payoff_fn, basis_fn, discount_factors, dtype, name) and returns: tf, _lsm_loop_body