pymarket.bids.demand_curves module¶

pymarket.bids.demand_curves.
demand_curve_from_bids
(bids)[source]¶ Creates a demand curve from a set of buying bids. It is the inverse cumulative distribution of quantity as a function of price.
Parameters: bids – Collection of all the bids in the market. The algorithm filters only the buying bids. Returns:  demand_curve (np.ndarray) – Stepwise constant demand curve represented as a collection of the N rightmost points of each interval (N1 bids). It is stored as a (N, 2) matrix where the first column is the xcoordinate and the second column is the ycoordinate. An extra point is a))dded with x coordinate at infinity and price at 0 to represent the end of the curve.
 index (np.ndarray) – The order of the identifier of each bid in the demand curve.
Examples
A minimal example, selling bid is ignored:
>>> bm = pm.BidManager() >>> bm.add_bid(1, 1, 0, buying=True) 0 >>> bm.add_bid(1, 1, 1, buying=False) 1 >>> dc, index = pm.demand_curve_from_bids(bm.get_df()) >>> dc array([[ 1., 1.], [inf, 0.]]) >>> index array([0])
A larger example with reordering of bids:
>>> bm = pm.BidManager() >>> bm.add_bid(1, 1, 0, buying=True) 0 >>> bm.add_bid(1, 1, 1, buying=False) 1 >>> bm.add_bid(3, 0.5, 2, buying=True) 2 >>> bm.add_bid(2.3, 0.1, 3, buying=True) 3 >>> dc, index = pm.demand_curve_from_bids(bm.get_df()) >>> dc array([[1. , 1. ], [4. , 0.5], [6.3, 0.1], [inf, 0. ]]) >>> index array([0, 2, 3])

pymarket.bids.demand_curves.
get_value_stepwise
(x, f)[source]¶ Returns the value of a stepwise constant function defined by the right extrems of its interval Functions are assumed to be defined in (0, inf).
Parameters:  x (float) – Value in which the function is to be evaluated
 f (np.ndarray) – Stepwise function represented as a 2 column matrix. Each row is the rightmost extreme point of each constant interval. The first column contains the x coordinate and is sorted increasingly. f is assumed to be defined only in the interval :math: (0, infty)
Returns: The image of x under f: f(x). If x is negative, then None is returned instead. If x is outside the range of the function (greater than f[1, 0]), then the method returns None.
Return type: Examples
>>> f = np.array([ ... [1, 1], ... [3, 4]]) >>> [pm.get_value_stepwise(x, f) ... for x in [1, 0, 0.5, 1, 2, 3, 4]] [None, 1, 1, 1, 4, 4, None]

pymarket.bids.demand_curves.
intersect_stepwise
(f, g, k=0.5)[source]¶ Finds the intersection of two stepwise constants functions where f is assumed to be bigger at 0 than g. If no intersection is found, None is returned.
Parameters:  f (np.ndarray) – Stepwise constant function represented as a 2 column matrix where each row is the rightmost point of the constat interval. The first column is sorted increasingly. Preconditions: f is nonincreasing.
 g (np.ndarray) – Stepwise constant function represented as a 2 column matrix where each row is the rightmost point of the constat interval. The first column is sorted increasingly. Preconditions: g is nondecreasing and f[0, 0] > g[0, 0]
 k (float) – If the intersection is empty or an interval, a convex combination of the yvalues of f and g will be returned and k will be used to determine hte final value. k=1 will be the value of g while k=0 will be the value of f.
Returns:  x_ast (float or None) – Axis coordinate of the intersection of both functions. If the intersection is empty, then it returns None.
 f_ast (int or None) – Index of the rightmost extreme of the interval of f involved in the intersection. If the intersection is empty, returns None
 g_ast (int or None) – Index of the rightmost extreme of the interval of g involved in the intersection. If the intersection is empty, returns None.
 v (float or None) – Ordinate of the intersection if it is uniquely identified, otherwise the kconvex combination of the y values of f and g in the last point when they were both defined.
Examples
Simple intersection with diferent domains
>>> f = np.array([[1, 3], [3, 1]]) >>> g = np.array([[2,2]]) >>> pm.intersect_stepwise(f, g) (1, 0, 0, 2)
Empty intersection, returning the middle value
>>> f = np.array([[1,3], [2, 2.5]]) >>> g = np.array([[1,1], [2, 2]]) >>> pm.intersect_stepwise(f, g) (None, None, None, 2.25)

pymarket.bids.demand_curves.
supply_curve_from_bids
(bids)[source]¶ Creates a supply curve from a set of selling bids. It is the cumulative distribution of quantity as a function of price.
Parameters: bids (pd.DataFrame) – Collection of all the bids in the market. The algorithm filters only the selling bids. Returns:  supply_curve (np.ndarray) – Stepwise constant demand curve represented as a collection of the N rightmost points of each interval (N1 bids). It is stored as a (N, 2) matrix where the first column is the xcoordinate and the second column is the ycoordinate. An extra point is added with x coordinate at infinity and price at infinity to represent the end of the curve.
 index (np.ndarray) – The order of the identifier of each bid in the supply curve.
Examples
A minimal example, selling bid is ignored:
>>> bm = pm.BidManager() >>> bm.add_bid(1, 3, 0, False) 0 >>> bm.add_bid(2.1, 3, 3, True) 1 >>> sc, index = pm.supply_curve_from_bids(bm.get_df()) >>> sc array([[ 1., 3.], [inf, inf]]) >>> index array([0])
A larger example with reordering:
>>> bm = pm.BidManager() >>> bm.add_bid(1, 3, 0, False) 0 >>> bm.add_bid(2.1, 3, 3, True) 1 >>> bm.add_bid(0.2, 1, 3, False) 2 >>> bm.add_bid(1.7, 6, 4, False) 3 >>> sc, index = pm.supply_curve_from_bids(bm.get_df()) >>> sc array([[0.2, 1. ], [1.2, 3. ], [2.9, 6. ], [inf, inf]]) >>> index array([2, 0, 3])