diff options
| author | loit <michael.foiani@gmail.com> | 2025-07-29 20:32:36 -0400 |
|---|---|---|
| committer | loit <michael.foiani@gmail.com> | 2025-07-29 20:32:36 -0400 |
| commit | 4722ce02ff70cd30ceb11b0ffa93f4e53ca6f80c (patch) | |
| tree | 24ba1b51f503153b4f8731ef6dd6788e663f4c0b | |
| parent | 0372b76ee22ea4421b70d6f7f8c2b29b2c7ac9dc (diff) | |
begin infrastructure for automated backtesting, allowing for viewing details about the trial after
| -rw-r--r-- | algo.py | 33 | ||||
| -rw-r--r-- | app.py | 80 | ||||
| -rw-r--r-- | bt-recent.json | 1 | ||||
| -rw-r--r-- | ema.py | 63 | ||||
| -rw-r--r-- | ema_algo.py | 81 | ||||
| -rw-r--r-- | last_chart_data.json | 2 | ||||
| -rw-r--r-- | simulate.py | 101 |
7 files changed, 255 insertions, 106 deletions
@@ -0,0 +1,33 @@ +from abc import ABC, abstractmethod + +class Algo(ABC): + """ + Function that takes in data nad determined whether to buy, sell, or hold + current position is a float that represents the ratio from liquid to shares that you own + i.e. 1.0 is $0 cash, all shares, 0.0 is max cash, 0 shares + """ + @abstractmethod + def detemine_signal(self, timestamps, prices, current_position): + pass # to implement per algo + + """ + Function that returns an array of go.X plots to merge into graph foir analysis + """ + @abstractmethod + def export_graph(self, graph_data): + pass # to implement per algo + + @property + def name(self): + pass + + @property + def graph_data(self): + pass + + # """ + # Function that takes in data and returns a buy, sell, or hold singal per interval + # """ + # @abstractmethod + # def backtest_algo(self): + # pass
\ No newline at end of file @@ -1,11 +1,8 @@ from dash import Dash, dcc, html, Input, Output -from analysis import find_intersections, interpolate_intersection -from api import fetch_chart_data -from ema import calc_emas, calculate_profit import plotly.graph_objects as go import json -import datetime -import pandas as pd +from datetime import datetime +from ema_algo import Ema_Algo app = Dash(__name__) @@ -66,49 +63,48 @@ app.layout = html.Div([ Input("interval_dropdown", "value") ) def display_color(ticker, period, interval): - chart_data = fetch_chart_data(ticker, period, interval) - error_style = {"color" : "inherit"} - error_message = '' - if chart_data['error'] == True: - # implement a feeback mechanism for ERROR codes - error_style = {"color" : "red"} - error_message = 'Issue with parameter selection. Please try again.' - - timestamps_raw = chart_data['timestamps'] - timestamps = [datetime.datetime.fromtimestamp(t) for t in timestamps_raw] - prices = chart_data['prices'] + fd = open('bt-recent.json', 'r') + raw_data = fd.read() + trial_data = json.loads(raw_data) + fd.close() + + chart_data = trial_data['chart_data'] + backtest_results = trial_data['backtest_results'] - ema_5 = calc_emas(5, prices) - ema_13 = calc_emas(13, prices) - profit = calculate_profit(ema_5, ema_13, prices, timestamps, 13) - buy_info = profit[-2] - print(buy_info) - buy_x = [] - buy_y = [] - for x,y,_ in buy_info: - buy_x.append(x) - buy_y.append(y) - - sell_info = profit[-1] - sell_x = [] - sell_y = [] - for x,y,_ in sell_info: - sell_x.append(x) - sell_y.append(y) - - print("Result Analysis:\n", "Percent gain/loss:\t", profit[0], profit[1], profit[2]) - percent_gain = profit[0] * 100 + percent_gain = backtest_results['percent_gain'] + error_style = {"color" : "red"} + error_message = "False error" # Code to execute no matter what (optional) + + raw_timestamps = chart_data['timestamps'] + timestamps = [datetime.fromtimestamp(t) for t in raw_timestamps] + prices = chart_data['prices'] + + # test to see if graphc works, TODO make it abstracted + algoEMA = Ema_Algo() + algo_graph_data = backtest_results['algo_graph_data'] + algo_graphs = algoEMA.export_graph(algo_graph_data) + + buy_indices = backtest_results['buy_indices'] + sell_indices = backtest_results['sell_indices'] + + buy_prices, buy_times = [], [] + for i in buy_indices: + buy_prices.append(prices[i]) + buy_times.append(timestamps[i]) + sell_prices, sell_times = [], [] + for i in sell_indices: + sell_prices.append(prices[i]) + sell_times.append(timestamps[i]) + buy_sell_scatters = [ + go.Scatter(name='Buys', x=buy_times, y=buy_prices, line=dict(color='rgb(0, 0, 255)'), mode='markers', marker_size=10), + go.Scatter(name='Sells', x=sell_times, y=sell_prices, line=dict(color='rgb(255, 255, 0)'), mode='markers', marker_size=10) + ] fig = go.Figure( data = [ go.Scatter(name='Price', x=timestamps, y=prices, line=dict(color='rgb(0, 0, 0)'), mode='lines'), - # go.Scatter(name='5 day EMA', x=timestamps, y=ema_5, line=dict(color='rgb(0, 255, 0)'), mode='lines'), - # go.Scatter(name='13 day EMA', x=timestamps, y=ema_13, line=dict(color='rgb(0, 0, 255)'), mode='lines'), - # go.Scatter(name='EMA Intersections', x=intersected_x, y=intersected_y, line=dict(color='rgb(255, 0, 0)'), mode='markers'), - go.Scatter(name='Buys', x=buy_x, y=buy_y, line=dict(color='rgb(0, 0, 255)'), mode='markers', marker_size=10), - go.Scatter(name='Sells', x=sell_x, y=sell_y, line=dict(color='rgb(255, 255, 0)'), mode='markers', marker_size=10), - ], + ] + 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=' + interval + ', range=' + period + ')'), diff --git a/bt-recent.json b/bt-recent.json new file mode 100644 index 0000000..1049801 --- /dev/null +++ b/bt-recent.json @@ -0,0 +1 @@ +{"chart_data": {"timestamps": [1722346200, 1722432600, 1722519000, 1722605400, 1722864600, 1722951000, 1723037400, 1723123800, 1723210200, 1723469400, 1723555800, 1723642200, 1723728600, 1723815000, 1724074200, 1724160600, 1724247000, 1724333400, 1724419800, 1724679000, 1724765400, 1724851800, 1724938200, 1725024600, 1725370200, 1725456600, 1725543000, 1725629400, 1725888600, 1725975000, 1726061400, 1726147800, 1726234200, 1726493400, 1726579800, 1726666200, 1726752600, 1726839000, 1727098200, 1727184600, 1727271000, 1727357400, 1727443800, 1727703000, 1727789400, 1727875800, 1727962200, 1728048600, 1728307800, 1728394200, 1728480600, 1728567000, 1728653400, 1728912600, 1728999000, 1729085400, 1729171800, 1729258200, 1729517400, 1729603800, 1729690200, 1729776600, 1729863000, 1730122200, 1730208600, 1730295000, 1730381400, 1730467800, 1730730600, 1730817000, 1730903400, 1730989800, 1731076200, 1731335400, 1731421800, 1731508200, 1731594600, 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\ No newline at end of file @@ -1,63 +0,0 @@ -def compute_buy_sell_signals_ema(period1, period2, prices): - ema_5 = calc_emas(5, prices) - ema_13 = calc_emas(13, prices) - - -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] - if prices[i] == None or prices[i] == 0: - print(i) - next_ema = prices[i] * weighted_multiplier + last_ema * (1 - weighted_multiplier) - emas.append(next_ema) - return emas - -def calculate_profit(buy_line, sell_line, prices, timestamps, offset=0, starting_money=10000): - if len(buy_line) != len(sell_line): - print("ERROR IN find_intersections: len of arrs not the same") - return [] - is_bought = False - curr_money = 10000 - shares_owned = 0 - buy_info = [] # coming in, (time, cash, # current shares, # shares to buy) - sell_info = [] # (time, cash, # current shares, # shares to sell,) - for i in range(offset, len(buy_line)): - current_b1 = buy_line[i] - current_sl = sell_line[i] - # if the sign is positive, we want to hold, if it's negative, we want to sell - sign_signal = current_b1 - current_sl - - if sign_signal > 0: - if not is_bought: - # buy the stock - shares_owned = curr_money / prices[i] - curr_money = 0 - buy_info.append((timestamps[i], prices[i], i)) - is_bought = True - if sign_signal < 0: - if is_bought: - # selling the stock - curr_money = prices[i] * shares_owned - shares_owned = 0 - sell_info.append((timestamps[i], prices[i], i)) - is_bought = False - - # TODO: consider end interval - total_assets = prices[-1] * shares_owned + curr_money - percent_gain = (total_assets - starting_money) / starting_money - return (percent_gain, total_assets, starting_money, buy_info, sell_info)
\ No newline at end of file diff --git a/ema_algo.py b/ema_algo.py new file mode 100644 index 0000000..db25c08 --- /dev/null +++ b/ema_algo.py @@ -0,0 +1,81 @@ +from algo import Algo +import plotly.graph_objects as go +import datetime + +class Ema_Algo(Algo): + def __init__(self, shortPeriod=5, longPeriod=13): + self.shortPeriod = shortPeriod + self.longPeriod = longPeriod + self.g_data = { + "timestamps" : [], + "ema_short" : [], + "ema_long" : [] + } + + @property + def name(self): + return "EMA Algo" + + @property + def graph_data(self): + return self.g_data + + def export_graph(self, g_data): + timestamps = [datetime.datetime.fromtimestamp(t) for t in g_data['timestamps']] + ema_5 = g_data['ema_short'] + ema_13 = g_data['ema_long'] + exp = [ + go.Scatter(name='5 day EMA', x=timestamps, y=ema_5, line=dict(color='rgb(0, 255, 0)'), mode='lines'), + go.Scatter(name='13 day EMA', x=timestamps, y=ema_13, line=dict(color='rgb(0, 0, 255)'), mode='lines') + ] + return exp + + def detemine_signal(self, timestamps, prices): + + ema_5 = self.calc_emas(self.shortPeriod, prices) + ema_13 = self.calc_emas(self.longPeriod, prices) + + # add to graph data + self.graph_data["timestamps"].append(timestamps[-1]) + self.graph_data["ema_short"].append(ema_5[-1]) + self.graph_data["ema_long"].append(ema_13[-1]) + + # determine the sign from the most recent price + sign_signal = ema_5[-1] - ema_13[-1] + # current position, (liquid, shares) + if sign_signal > 0: + return 1.0 # buy max shares + if sign_signal < 0: + return 0.0 # sell all shares + + return 0.5 + + """ + Calculates the simple moving average of the first period of the data + """ + def calc_first_sma(self, 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 + + """ + Returns an array off all EMAs, computed according to period + """ + def calc_emas(self, period, prices): + weighted_multiplier = 2.0 / (period + 1.0) + + # calculate the first ema + first_ema = self.calc_first_sma(period, prices) + + # calculate the rest ema's using that first + emas = [first_ema] * period # 0, 1, 2 (for period 3) + for i in range(period, len(prices)): # 3, 4, 5, 6, ... , last + last_ema = emas[-1] + if prices[i] == None or prices[i] == 0: + print(i) + next_ema = prices[i] * weighted_multiplier + last_ema * (1 - weighted_multiplier) + emas.append(next_ema) + return emas
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\ No newline at end of file diff --git a/simulate.py b/simulate.py new file mode 100644 index 0000000..0ecdaff --- /dev/null +++ b/simulate.py @@ -0,0 +1,101 @@ +from algo import Algo +from ema_algo import Ema_Algo +from api import fetch_chart_data +import datetime +import json + +""" +Function that takes in data and returns a buy, sell, or hold singal per interval +""" +def backtest_algo(algo : Algo, timestamps, prices, init_offset=5, starting_money=10000): + # take a fraction of the OG data (or a set #) + assert len(timestamps) == len(prices) + assert init_offset < len(timestamps) # make sure enough data to start with + + current_timestamps = timestamps[:init_offset] + current_prices = prices[:init_offset] + + is_bought = 0.0 # not holding anything + current_liquid = 10000 + shares_owned = 0 + buy_data = [] + sell_data = [] + for i in range(init_offset, len(timestamps)): + # update prices array and run algo + current_timestamps.append(timestamps[i]) + current_prices.append(prices[i]) + current_signal = algo.detemine_signal(current_timestamps, current_prices) + + cur_p = current_prices[-1] + cur_t = current_timestamps[-1] + if current_signal == 1.0: # signal is to buy + if is_bought == 0.0: # if we haven't bought, purchase + shares_owned = current_liquid / cur_p + current_liquid = 0 + is_bought = 1.0 + buy_data.append(i) + print("buy", shares_owned, current_liquid, datetime.datetime.fromtimestamp(timestamps[i])) + elif current_signal == 0.0: # signal sell all + if is_bought == 1.0: # if we have bought, sell! + current_liquid = shares_owned * cur_p + shares_owned = 0 + is_bought = 0.0 + sell_data.append(i) + print('sell', shares_owned, current_liquid, datetime.datetime.fromtimestamp(timestamps[i])) + + + # calculate total assets + assets = prices[-1] * shares_owned + current_liquid + percent_gain = 100 * (assets - starting_money) / starting_money + print(assets, percent_gain) + + # create a json to store the reuslts + results = { + "timestamps" : timestamps, + "prices" : prices, + "buy_indices" : buy_data, + "sell_indices" : sell_data, + "percent_gain" : percent_gain, + "starting_assets" : starting_money, + "final_assets" : assets, + "algo_name" : algo.name, + "algo_graph_data" : algo.graph_data + } + + return results + + # store all algo name, buy, sell, price data, timestamps, into a json so it can be viewed as a trial + # caluclate some metrics (NOW: only how much money gained, how many trades, trades per day... etc) + + pass + +def test(): + print("MAIN simulate.py") + + ema_algo = Ema_Algo() + + # get data + data = fetch_chart_data('SPY', '1y', '1d') + print(data.keys()) + + url_params = { + "ticker" : 'SPY', + "period" : '1yr', + "interval" : '1d', + } + + results = backtest_algo(ema_algo, data['timestamps'], data['prices'], 13) + + # write the data into a json to be viewed in a chart + + trial_data = { + "chart_data" : data, + "url_params" : url_params, + "backtest_results" : results + } + + fd = open('bt-recent.json', 'w') + fd.write(json.dumps(trial_data)) + fd.close() + +test()
\ No newline at end of file |