diff options
| author | sotech117 <michael_foiani@brown.edu> | 2025-09-03 13:54:19 -0400 |
|---|---|---|
| committer | sotech117 <michael_foiani@brown.edu> | 2025-09-03 13:54:19 -0400 |
| commit | ea05658d9868b775fef74fba79a69bd21a43f93d (patch) | |
| tree | 6c73e814224b9ea9fa756fb93b5c32c7501b62c1 | |
| parent | 5bf22fc7e3c392c8bd44315ca2d06d7dca7d084e (diff) | |
new fft algo with laplacian range of convergencemain
| -rw-r--r-- | .DS_Store | bin | 0 -> 6148 bytes | |||
| -rw-r--r-- | analysis.py | 33 | ||||
| -rw-r--r-- | api.py | 10 | ||||
| -rw-r--r-- | app.py | 133 | ||||
| -rw-r--r-- | fft_test.py | 271 | ||||
| -rw-r--r-- | last_chart_data.json | 2 |
6 files changed, 374 insertions, 75 deletions
diff --git a/.DS_Store b/.DS_Store Binary files differnew file mode 100644 index 0000000..045bea2 --- /dev/null +++ b/.DS_Store diff --git a/analysis.py b/analysis.py index 912eb0a..ca409b0 100644 --- a/analysis.py +++ b/analysis.py @@ -1,6 +1,7 @@ import json import datetime import os +import pandas as pd def summarize_results(batch_name): """ @@ -34,17 +35,22 @@ def summarize_results(batch_name): if algo_key not in algos_to_results: algos_to_results[algo_key] = { 'percent_gains': [], - 'stock_orders': [] + 'stock_orders': [], + 'file_names': [], + 'url_params': [] } algos_to_results[algo_key]['percent_gains'].append(result['percent_gain']) stock_ticker = result_data['url_params']['ticker'] algos_to_results[algo_key]['stock_orders'].append(stock_ticker) - - # summarize the results for each algo + algos_to_results[algo_key]['file_names'].append(file_path.split('/')[-1]) + algos_to_results[algo_key]['url_params'].append(result_data['url_params']) + for algo_name, result in algos_to_results.items(): algo_params = algo_name.split('_')[1] # Extract params from the key + algo_name_alone = algo_name.split('_')[0] # Extract name from the key + if not result['percent_gains'] and not result['percent_losses']: continue # Skip if no gains or losses @@ -60,7 +66,7 @@ def summarize_results(batch_name): # Append the summarized data results_summary.append([ - algo_name, + algo_name_alone, algo_params, avg_percent_gain, best_stock_order @@ -69,9 +75,15 @@ def summarize_results(batch_name): # Sort the results by average percent gain in descending order results_summary.sort(key=lambda x: x[2], reverse=True) - print(algos_to_results) - - return results_summary + print("Results Summary:") + + # Return the summarized results as a DataFrame + return pd.DataFrame(results_summary, columns=[ + 'algo_name', + 'algo_params', + 'avg_percent_gain', + 'best_stock_for_gain' + ]), algos_to_results def test(): """ @@ -79,12 +91,7 @@ def test(): """ batch_name = 'test-1-ema' results = summarize_results(batch_name) - if results: - print("Results Summary:") - for row in results: - print(row) - else: - print("No results found.") + print(results) test() @@ -150,14 +150,14 @@ def fetch_chart_data_yahoo(ticker='ADA-USD', interval='1m', period_end_date=None if period_end_date == None: period_end_date = datetime.now() if period_length == None: - period_length = timedelta(days=5) + period_length = timedelta(days=8) # cast to int to truncate the decimal period2 = int(datetime.timestamp(period_end_date)) # find the first period via subtracting the period length period1 = int(datetime.timestamp(period_end_date - period_length)) - print(datetime.isoformat(datetime.fromtimestamp(period2)), datetime.isoformat(datetime.fromtimestamp(period1))) + # print(datetime.isoformat(datetime.fromtimestamp(period2)), datetime.isoformat(datetime.fromtimestamp(period1))) params = { 'period1' : period1, # the start date (in epoch time) @@ -170,8 +170,8 @@ def fetch_chart_data_yahoo(ticker='ADA-USD', interval='1m', period_end_date=None headers = {'User-agent' : 'fin-backtesting-proj'} r = requests.get("https://query2.finance.yahoo.com/v8/finance/chart/" + ticker, headers=headers, params=params) - print(r.url) - print("status_code:\t", r.status_code) + # print(r.url) + # print("status_code:\t", r.status_code) # decode the JSON response data into a Python object try: @@ -209,4 +209,4 @@ def fetch_chart_data_yahoo(ticker='ADA-USD', interval='1m', period_end_date=None def test(): fetch_chart_data_backtest() -test()
\ No newline at end of file +# test()
\ No newline at end of file @@ -1,80 +1,101 @@ -from dash import Dash, dcc, html, Input, Output +from dash import Dash, dcc, html, Input, Output, dash_table import plotly.graph_objects as go import json from datetime import datetime, timedelta from ema_algo import Ema_Algo from api import fetch_chart_data_yahoo -from analysis import compute_results +from analysis import summarize_results import pytz +import pandas as pd app = Dash(__name__) -# pull stock data from json files -# timestamps_file = open('timestamps.json', 'r') -# timestamps_file_data = timestamps_file.read() -# timestamps_raw = json.loads(timestamps_file_data) -# timestamps = [datetime.datetime.fromtimestamp(t) for t in timestamps_raw] +# pull the data from the files +table_data, algos_to_results = summarize_results('test-1-ema') -# prices_file = open('close_prices.json', 'r') -# prices = json.loads(prices_file.read()) - -# intersection_indices = find_intersections(ema_5, ema_13, offset=13) # offset so don't calculate the SMA days -# interpolated_intersections = [interpolate_intersection(indices, timestamps, ema_5, ema_13) for indices in intersection_indices] -# intersected_x = [] -# intersected_y = [] -# for x,y in interpolated_intersections: -# intersected_x.append(x) -# intersected_y.append(y) +app.layout = html.Div([ + html.H4('Backtesting using EMA algos [ALPHA VERSION 0.1.0]'), + dcc.Dropdown( + id='batch_name', + options=[ + {'label': 'Test 1 - EMA', 'value': 'test-1-ema'} + ], + value='test-1-ema', + clearable=False + ), + html.Hr(), + dash_table.DataTable( + table_data.to_dict('records'), + [{"name": i, "id": i} for i in table_data.columns], + style_table={'overflowX': 'auto'}, + style_cell={'textAlign': 'center'}, + style_header={ + 'backgroundColor': 'rgb(230, 230, 230)', + 'fontWeight': 'bold' + }, + style_data={ + 'whiteSpace': 'normal', + 'height': 'auto' + }, + sort_action='native', + ), -app.layout = html.Div( - html.H4('Backtesting using EMA algos [ALPHA VERSION 0.0.5]'), - dcc.table(id="results_table") - dcc.Input(id="file_id", value="SPY", type="text"), html.Hr(), - dcc.Graph(id="graph"), + # dcc.Graph(id="graph"), html.P("If bought and sold on these signals, the percent gain/loss would be:"), - html.P(id="percent_gain") + html.P(id="percent_gain"), + + # for each algo, print the individual results given by the map + html.Div(id='algo_results', children=[ + html.Div([ + html.Div([ + + ]) + ]) for algo_name, result in algos_to_results.items() + ]) ]) @app.callback( - Output("results_table", "figure"), - Output("graph", "figure"), - Output("error_message", "children"), - Input("file_id", "value"), - ) -def display_color(file_id): + Input("batch_name", "value"), +) +def display_color(batch_name): # compute the results to show in the table - path = 'test-1-ema' - result_summary = compute_results(path, file_id) + path = batch_name + result_summary = summarize_results(path) if not result_summary: - return go.Figure(), go.Figure(), "No results found for the given file ID." + return "No results found for the given file ID." + + # update the table data + global table_data + table_data = result_summary + + # table = go.Figure( + # data = [go.Table( + # header=dict(values=["algo name", "algo params", "avg percent gain", "best stock for gain"]), + # cells=dict(values=result_summary) + # )] + # ) + # data = fetch_chart_data_yahoo('XRP-USD', '1d', None, timedelta(weeks=52)) + # times = [datetime.fromtimestamp(t).astimezone(pytz.timezone('US/Eastern')) for t in data['timestamps']] + # comp_scatter = go.Scatter(name='Price (yahoo)', x=times, y=data['prices'], line=dict(color='rgb(255, 0, 0)'), mode='lines') + # fig = go.Figure( + # data = [ + # go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 0, 0)'), mode='lines'), + # comp_scatter + # ] + # # + algo_graphs + buy_sell_scatters + # , + # layout = go.Layout( + # title=go.layout.Title(text='Chart for ' + chart_data['name']), + # xaxis=go.layout.XAxis(title='Date (dt=' + url_params['interval'] + ', range=' + url_params['period'] + ')'), + # yaxis=go.layout.YAxis(title='Price ($)') + # ) + # ) + - print(result_summary) - table = go.Figure( - data = [go.Table( - header=dict(values=["algo name", "algo params", "avg percent gain", "best stock order"]), - cells=dict(values=result_summary) - )] - ) - data = fetch_chart_data_yahoo('XRP-USD', '1d', None, timedelta(weeks=52)) - times = [datetime.fromtimestamp(t).astimezone(pytz.timezone('US/Eastern')) for t in data['timestamps']] - comp_scatter = go.Scatter(name='Price (yahoo)', x=times, y=data['prices'], line=dict(color='rgb(255, 0, 0)'), mode='lines') - fig = go.Figure( - data = [ - go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 0, 0)'), mode='lines'), - comp_scatter - ] - # + algo_graphs + buy_sell_scatters - , - layout = go.Layout( - title=go.layout.Title(text='Chart for ' + chart_data['name']), - xaxis=go.layout.XAxis(title='Date (dt=' + url_params['interval'] + ', range=' + url_params['period'] + ')'), - yaxis=go.layout.YAxis(title='Price ($)') - ) - ) - return fig, percent_gain, error_style, error_message + return diff --git a/fft_test.py b/fft_test.py new file mode 100644 index 0000000..bd25e73 --- /dev/null +++ b/fft_test.py @@ -0,0 +1,271 @@ +import numpy as np +import matplotlib.pyplot as plt +from api import fetch_chart_data_yahoo, pull_last_from_file +import warnings +from datetime import timedelta, datetime +import concurrent.futures + +def main(): + + + # take the fft of sin(t) for t in [0, 2*pi] + dt = 1 + + t = np.arange(0, 100, dt) + y = np.sin(t) + y += np.min(y) + y_fft = np.fft.fftshift((np.fft.fft(y))) + + freqs = ( 1 / dt ) * np.linspace(-1/2, 1/2, len(y)) + + # graph the original and the fft + plt.subplot(2, 1, 1) + plt.plot(t, y) + plt.title("Original Signal") + plt.subplot(2, 1, 2) + plt.plot(freqs, np.abs(y_fft)) + plt.title("FFT of Signal") + plt.show() + + # introduce new variable gamma to sweep over range of convergence + gamma = np.linspace(-.05, .25, 100) + for g in gamma: + y_damped = y * np.exp(-g * t) + y_fft_damped = np.fft.fftshift((np.fft.fft(y_damped))) + freqs = (1 / dt) * np.linspace(-1/2, 1/2, len(y_damped)) + #plt.plot(freqs, np.abs(y_fft_damped), label=f"Gamma: {g:.2f}") + # print what the limit approaches + print(f"Limit at gamma={g:.2f}, x -> infinity: {np.real(y_fft_damped[-1])}") + # make a numpy function accordig to data timeseries + data = fetch_chart_data_yahoo('AAPL') + print(data.keys()) + y_test = np.array(data['prices']) + y_test += np.min(y_test) + t_test = np.arange(0, len(data['prices']), 1) + print(y_test.shape, t_test.shape) + + gamma = find_gamma_where_area_changes_signs(y_test, t_test) + print(gamma) + + # Example: Add more S&P 500 stocks to the list + + deltatime= timedelta(days=8) + interval = '1m' + + stock_list = [ + 'AAPL', 'MSFT', 'GOOGL', 'AMZN', 'NVDA', 'TSLA', 'NFLX', 'PLTR', 'META', + 'JPM', 'V', 'UNH', 'HD', 'MA', 'PG', 'LLY', 'AVGO', 'XOM', 'COST', + 'MRK', 'ABBV', 'PEP', 'CVX', 'ADBE', 'WMT', 'CRM', 'ACN', 'MCD', 'DHR', + 'AMD', 'TXN', 'LIN', 'NEE', 'UNP', 'HON', 'AMAT', 'LOW', 'QCOM', 'INTC', + 'TMO', 'COP', 'BKNG', 'SPGI', 'GS', 'ISRG', 'NOW', 'BLK', 'AXP', 'DE', + 'CAT', 'LMT', 'MDT', 'SYK', 'C', 'AMGN', 'ELV', 'SCHW', 'CB', 'PGR', + 'VRTX', 'REGN', 'CI', 'ADP', 'GILD', 'MO', 'SO', 'DUK', 'MMC', 'TGT', + 'FISV', 'BSX', 'PNC', 'BDX', 'ITW', 'NSC', 'CME', 'AON', 'ETN', 'ECL', + 'EMR', 'AIG', 'HCA', 'PSA', 'APD', 'ORLY', 'SHW', 'SRE', 'MCO', 'ROST', + 'KMB', 'WELL', 'TRV', 'STZ', 'PAYX', 'VLO', 'WMB', 'MTD', 'F', 'GM' + ] + + def gamma_worker(ticker): + data = fetch_chart_data_yahoo(ticker, interval, period_length=deltatime) + # print the first and last date and price of this ticker + start_date = datetime.fromtimestamp(data['timestamps'][0]).strftime('%Y-%m-%d %H:%M:%S') + end_date = datetime.fromtimestamp(data['timestamps'][-1]).strftime('%Y-%m-%d %H:%M:%S') + print(f"{ticker}: {start_date} {data['prices'][0]} {end_date} {data['prices'][-1]}") + # normalize the data using min-max scaling + min_price = np.min(data['prices']) + max_price = np.max(data['prices']) + normalized_prices = (data['prices'] - min_price) / (max_price - min_price) if max_price > min_price else data['prices'] + # for now, set normalized prices to data['prices'] + normalized_prices = data['prices'] + return ticker, find_gamma_where_area_changes_signs(normalized_prices, np.arange(0, len(normalized_prices), 1)), (data['prices'][-1] - data['prices'][0]) / data['prices'][0] + + with concurrent.futures.ThreadPoolExecutor() as executor: + results = list(executor.map(gamma_worker, stock_list)) + keys = [r[0] for r in results] + value = [r[1] for r in results] + percent_change = [r[2] for r in results] + gamma_map = dict(zip(keys, value)) + percent_change_map = dict(zip(keys, percent_change)) + # gamma_map = {} + # print(results) + # for ticker in stock_list: + # print(ticker) + # data = fetch_chart_data_yahoo(ticker) + # gamma_map[ticker] = find_gamma_where_area_changes_signs(data['prices'], np.arange(0, len(data['prices']), 1)) + + spy_data = fetch_chart_data_yahoo('SPY', interval, period_length=deltatime) + gamma_spy = find_gamma_where_area_changes_signs(spy_data['prices'], np.arange(0, len(spy_data['prices']), 1)) + distribution_std = np.std([gamma_map[ticker] for ticker in stock_list]) + + # make a normal distrubiton where gamma spy is the mean + normal_dist = np.random.normal(loc=gamma_spy, scale=distribution_std, size=500) + + # for each stock in the gamma map, determine a p-value to see who is under and over performing + p_values = {} + for ticker in stock_list: + p_values[ticker] = np.sum(normal_dist < gamma_map[ticker]) / len(normal_dist) + + # pretty print on each line the pvalues + for ticker in stock_list: + print(f"{ticker}: {p_values[ticker]}") + + # print the statistically significant stocks with an alpha of 0.05 + alpha = 0.10 + print(f"Statistically significant stocks (alpha={alpha}):") + for ticker in stock_list: + if p_values[ticker] < alpha or p_values[ticker] > 1 - alpha: + # determine their percent gain/loss over this time period from the prices + percent_change = percent_change_map[ticker] * 100 + print(f" - {ticker}: {p_values[ticker]} ({percent_change:.2f}%)") + + # data = pull_last_from_file() + + # y = np.array(data['prices']) + # t = np.array(data['timestamps']) + + # y += np.min(y) + + # print(len(y), len(t), len(y) == len(t)) + + # # gamma_low = low_where_no_overflow(y, t) + # gamma_low = 0.00000001 + + # F, dF = compute_laplacian_transform(gamma_low, y, t) + # print(f"gamma_low: {gamma_low}\n") + # print(F) + # print(dF) + # print(t) + + # print(np.argwhere(np.abs(F) < .0001)) + # print(np.argwhere(np.abs(dF) < 1)) + + # prev_sign = dF[0] + # swings = [] + # for i, v in enumerate(dF): + # if i == 0: continue + + # if np.sign(prev_sign) * np.sign(v) < 0: + # # print(f'sign_change found in dF: i = {i}, prev_value = {dF[i-1]}, value = {v}, swing = {v - dF[i-1]}') + # swings.append(v - dF[i-1]) + + # prev_sign = np.sign(v) + + # s_avg = np.average(swings) + # s_std = np.std(swings) + # s_max = np.max(swings) + # s_min = np.min(swings) + # s_cnt = len(swings) + # print(f'swings: [avg: {s_avg}, std: {s_std}, max: {s_max}, min: {s_min}, count: {s_cnt}]') + + # t_scores = (swings - s_avg) / s_std + # # print(np.sort(t_scores)) + + # normal_s_dist = np.random.normal(loc=s_avg, scale=s_std, size=1000000) + # p_values = [] + # for t in t_scores: + # p_values.append(np.sum(normal_s_dist < t) / 1000000) + + # p_values = np.array(p_values) + # print("p_values: ", p_values) + # print(np.argwhere(np.abs(p_values) < .25)) + + + +def compute_laplacian_transform(gamma, y, t): + # compute the laplacian transform for a given gamma + # check to see if -gamma * t is too large and will throw an overflow + + y_damped = y * np.exp(-gamma * t) + y_fft_damped = np.fft.fftshift((np.fft.fft(y_damped))) + area = np.real((y_fft_damped)) + # such that s = gamma + iw + # dy/dt = s * y(s) - y(0) + # is the space of these wavelengths + # dt = t[1] - t[0] # i need to determine what the frequencies are here... they seem like they should be on the unit circle + # wi = ( 1.j / dt ) * np.linspace(-1, 1, len(y)) + s = t * 1.j + gamma + # print('s___\n', s) + dy_dt = s * y_fft_damped - y_fft_damped[0] + + da = np.real(dy_dt) + return area, da + +def compute_laplacian_transform_convergence(gamma, y, t): + # compute the laplacian transform for a given gamma + # check to see if -gamma * t is too large and will throw an overflow + y_damped = y * np.exp(-gamma * t) + y_fft_damped = np.fft.fftshift((np.fft.fft(y_damped))) + area = np.real(y_fft_damped[-1]) + return area + +def low_where_no_overflow(y, t): + + # setup a raise overflow exepction when + warnings.filterwarnings("error", category=RuntimeWarning) + low = -7 + while True: + try: + compute_laplacian_transform_convergence(low, y, t) + return low + except RuntimeWarning: + low += .1 + + +def find_gamma_where_area_changes_signs(y, t): + # start at gamma = 100 + # do a binary search over the gamma values + # set high to the max float + low = low_where_no_overflow(y, t) + low_area = compute_laplacian_transform_convergence(low, y, t) + + high = 100000000 + high_area = compute_laplacian_transform_convergence(high, y, t) + + found = False + # print(f"Log: starting with high={high} and low={low}, high_area={high_area}, low_area={low_area}.") + iters = 0 + while not found: + if iters > 10000: + raise Exception("ERROR: find_gamma_where_area_changes_signs did not converge after 10,000 iterations.") + + iters += 1 + mid = low * 0.5 + high * 0.5 + + # if there is no sign change between high and low return mid with a warning + if np.sign(high_area) * np.sign(low_area) > 0: + print(f"Warning: No sign change between high={high} and low={low}, high_area={high_area}, low_area={low_area}. Returning mid={mid}.") + return mid + + # compute laplacian transform at this gamma + mid_area = compute_laplacian_transform_convergence(mid, y, t) + # if there is a sign change between mid and high, then set low to mid + if np.sign(high_area) * np.sign(mid_area) <= 0: + low = mid + low_area = mid_area + + # if low and area are ,001 apart, then we have found the root + if abs(low - high) < .000000001: + found = True + # print('branch1') + continue + + # if there is a sign change between mid and low, then set high to mid + if np.sign(low_area) * np.sign(mid_area) <= 0: + high = mid + high_area = mid_area + + # if high and area are one apart, then we have found the root + if abs(high - low) < .000000001: + found = True + + # print('branch2') + continue + + # print('branch3', low_area, high_area) + + # return the gamma value where the area changes sign + # print(f"Log: found gamma={low} after {iters} iterations.") + return low + +if __name__ == "__main__": + main()
\ No newline at end of file diff --git a/last_chart_data.json b/last_chart_data.json index 990b70d..526f0f0 100644 --- a/last_chart_data.json +++ b/last_chart_data.json @@ -1 +1 @@ -{"timestamps": [1706090880.0, 1706091120.0, 1706091420.0, 1706091480.0, 1706091900.0, 1706092680.0, 1706092740.0, 1706092800.0, 1706092980.0, 1706093280.0, 1706093400.0, 1706093460.0, 1706093700.0, 1706094000.0, 1706094300.0, 1706094420.0, 1706094480.0, 1706094600.0, 1706094780.0, 1706094840.0, 1706095140.0, 1706095200.0, 1706095380.0, 1706095440.0, 1706095620.0, 1706095980.0, 1706096100.0, 1706097000.0, 1706097060.0, 1706098080.0, 1706098140.0, 1706098500.0, 1706098560.0, 1706098800.0, 1706100660.0, 1706101380.0, 1706101680.0, 1706102100.0, 1706102160.0, 1706102400.0, 1706103120.0, 1706103300.0, 1706103540.0, 1706103600.0, 1706104200.0, 1706104260.0, 1706105580.0, 1706107860.0, 1706108280.0, 1706108340.0, 1706108880.0, 1706109900.0, 1706111100.0, 1706111160.0, 1706111340.0, 1706111460.0, 1706111580.0, 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