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Diffstat (limited to 'simulate.py')
| -rw-r--r-- | simulate.py | 101 |
1 files changed, 101 insertions, 0 deletions
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()
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