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authorloit <michael.foiani@gmail.com>2025-07-28 18:00:08 -0400
committerloit <michael.foiani@gmail.com>2025-07-28 18:00:08 -0400
commit8907c286e857b622af171c2d8ac9040b05970549 (patch)
treec288bd62aa4e82f926c3666d61e00e3cb10d3b9b /analysis.py
parent8062d3a9cc10ccfec3dab7f859fa0d1d4c118d38 (diff)
add intersection interpolation code for graphing EMAs visually
Diffstat (limited to 'analysis.py')
-rw-r--r--analysis.py94
1 files changed, 94 insertions, 0 deletions
diff --git a/analysis.py b/analysis.py
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index 0000000..2b5ed59
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+++ b/analysis.py
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+import json
+import datetime
+
+# pull stock data from json files
+# timestamps_file = open('timestamps.json', 'r')
+# timestamps_file_data = timestamps_file.read()
+# timestamps = json.loads(timestamps_file_data)
+# timestamps = [datetime.datetime.fromtimestamp(t) for t in timestamps]
+
+# prices_file = open('close_prices.json', 'r')
+# prices = json.loads(prices_file.read())
+
+# print('timestamps:\t', timestamps, '\nprices:\t', prices)
+
+# make the line data for the 5 day exponential moving average (EMA)
+
+def calc_first_sma(period, prices):
+ prices_sum = 0
+ for i in range(0, period):
+ prices_sum += prices[i] # 0, 1, 2, 3 ("popping" order)
+ # print('prices_sum:\t', prices_sum)
+
+ return prices_sum / period
+
+def calc_emas(period, prices):
+ weighted_multiplier = 2.0 / (period + 1.0)
+
+ # calculate the first ema
+ first_ema = calc_first_sma(period, prices)
+
+ # calculate the rest ema's using that first
+ emas = [first_ema] * period
+ for i in range(period + 1, len(prices)): # 4, 5, 6, ... , last
+ last_ema = emas[-1]
+ next_ema = prices[i] * weighted_multiplier + last_ema * (1 - weighted_multiplier)
+ emas.append(next_ema)
+ return emas
+
+def interpolate_intersection(intersection_indices, timestamps, prices1, prices2):
+ left_index = intersection_indices[0]
+ right_index = intersection_indices[1]
+ if right_index == -1:
+ return timestamps[left_index]
+
+ y_1 = prices1[left_index]
+ y_2 = prices1[right_index] # first line
+
+ v_1 = prices2[left_index]
+ v_2 = prices2[right_index] # second line
+
+ x_1 = 0 # take this as zero the simplify the algebra
+ x_diff = timestamps[right_index] - timestamps[left_index] # same for both lines
+
+ # find intersection between those lines
+ x_diff = x_diff.total_seconds()
+ m_1 = (y_2 - y_1) / x_diff # slope of line 1
+ m_2 = (v_2 - v_1) / x_diff
+
+ x_interpolated = (v_1 - y_1) / (m_1 - m_2)
+ y_interpolated = m_1 * (x_interpolated) + y_1
+
+ # add back the time we subtracted to make x_1=0
+ x_interpolated = datetime.timedelta(seconds = x_interpolated) + timestamps[left_index]
+ return (x_interpolated, y_interpolated)
+
+
+
+"""
+Returns the indices of where two arrays' values intersects
+"""
+def find_intersections(prices1, prices2):
+ if len(prices1) != len(prices2):
+ print("ERROR IN find_intersections: len of arrs not the same")
+ return []
+ prev_p1 = prices1[0]
+ prev_p2 = prices2[0]
+ intersection_indices = set()
+ for i in range(1, len(prices1)):
+ next_p1 = prices1[i]
+ next_p2 = prices2[i]
+ # if the sign (negative to positive) changes, then there was an intersection between these pts
+ sub_prev = prev_p1 - prev_p2
+ sub_next = next_p1 - next_p2
+
+ if (sub_prev > 0 and sub_next < 0) or (sub_prev < 0 and sub_next > 0): # TODO, consider on the 0 case
+ intersection_indices.add((i-1, i))
+
+ if sub_next == 0:
+ intersection_indices.add((i, -1))
+
+ prev_p1 = next_p1
+ prev_p2 = next_p2
+
+ return intersection_indices