clean up and added timestamp compatibility

README.md

@@ -2,15 +2,15 @@
 
 [![License: LGPL v3](https://img.shields.io/github/license/jrmeier/fast-trade)](LICENSE)
 [![PyPI](https://img.shields.io/pypi/v/fast-trade.svg?style=flat-square)](https://pypi.org/project/fast-trade/)
-[![](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/download/releases/3.7.0/)
-[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
+[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/download/releases/3.7.0/)
 [![Python application](https://github.com/jrmeier/fast-trade/workflows/Python%20application/badge.svg)](https://github.com/jrmeier/fast-trade/actions)
 
 A library built with strategy portability and performance in mind for back-test trading strategies.
 
 ## Motivations
 
 Strategies are cheap. This is the main motivation behind fast-trade. Since a strategy is just a JSON object, strategies can be created, stored, modified, versioned, and re-run easily. Ideally, a strategy could be generated and tested quickly; fast-trade is just the library to handle that.
+Fast Trade is also useful for quickly analyzing chart (`ohlc`) data.
 
 ## Indicators
 
@@ -41,8 +41,6 @@ date,close,open,high,low,volume
 
 A dataframe can also be passed to `run_backtest(...)` function such as `run_backtest(strategy, df=<your data frame>)`.
 
-I've been using this project to collect and store tick data [crypto-data](https://github.com/jrmeier/crypto-data).
-
 ## Install
 
 ```bash
@@ -209,17 +207,20 @@ Strategies include all the instructions needed to run the backtest minus the dat
 
   - optional,
   - default: `""`
-  - description: The time string of when to start the backtest with `%Y-%m-%d %H:%M:%S` date format.
+  - description: The time string of when to start the backtest with `%Y-%m-%d %H:%M:%S` date format or a timestamp. It will be tested
   - Ex.
     - `"2018-05-01 00:00:00"` May 1st, 2018 at midnight
 
 - stop: string
 
   - optional
   - default: `""`
-  - description: The time string of when to stop the backtest with `%Y-%m-%d %H:%M:%S` date format.
+  - description: The time string of when to stop the backtest with `%Y-%m-%d %H:%M:%S` date format or a timestamp
   - Ex.
     - `"2020-12-28 00:08:00"` December 28th, 2020 at 8am.
+    - `"2020-06-01"`June 6th, 2020
+    - `1590969600` (seconds) June 6th, 2020
+    - `1590969600000` (milliseconds) June 6th, 2020
 
 - base_balance: float
 
@@ -253,7 +254,7 @@ Strategies include all the instructions needed to run the backtest minus the dat
 ```python
 {
    "name": "example",
-   "chart_period": "4m",
+   "chart_period": "4T",
    "start": "2018-05-01 00:00:00",
    "stop": "2018-05-04 00:00:00",
    "enter": [

fast_trade/build_data_frame.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 import pandas as pd
 from finta import TA
 import os
@@ -78,7 +79,7 @@ def apply_charting_to_df(
     Parameters
     ----------
         df: dataframe with data loaded
-        chart_period: string, describes how often to sample data, default is '1M' (1 minute)
+        chart_period: string, describes how often to sample data, default is '1Min' (1 minute)
             see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects
         start_time: datestring in YYYY-MM-DD HH:MM (ex. 2020-08-31 04:00) of when to begin the backtest
         stop_time: datestring of YYYY-MM-DD HH:MM when to stop the backtest
@@ -92,6 +93,18 @@ def apply_charting_to_df(
     else:
         df.index = pd.to_datetime(df.index)
 
+    if start_time:
+        if isinstance(start_time, datetime) or type(start_time) is int:
+            time_unit = detect_time_unit(start_time)
+            start_time = pd.to_datetime(start_time, unit=time_unit)
+            start_time = start_time.strftime("%Y-%m-%d %H:%M:%S")
+
+    if stop_time:
+        if isinstance(stop_time, datetime) or type(stop_time) is int:
+            time_unit = detect_time_unit(stop_time)
+            stop_time = pd.to_datetime(stop_time, unit=time_unit)
+            stop_time = stop_time.strftime("%Y-%m-%d %H:%M:%S")
+
     df = df.resample(chart_period).first()
 
     if start_time and stop_time:

fast_trade/build_summary.py

@@ -114,8 +114,8 @@ def build_summary(df, perf_start_time, strategy):
         "min_trade_len": min_trade_time_held.total_seconds(),
         "total_num_winning_trades": total_num_winning_trades,
         "total_num_losing_trades": total_num_losing_trades,
-        "avg_win_per": round(float(avg_win_per)),
-        "avg_loss_per": round(float(avg_loss_per)),
+        "avg_win_per": round(float(avg_win_per), 3),
+        "avg_loss_per": round(float(avg_loss_per), 3),
         "best_trade_perc": round(max_trade_perc, 3),
         "min_trade_perc": round(min_trade_perc, 3),
         "median_trade_perc": round(median_trade_perc, 3),

fast_trade/run_backtest.py

@@ -21,14 +21,12 @@ def run_backtest(
             trade_log, dataframe of all the rows where transactions happened
     """
 
-
     perf_start_time = datetime.datetime.utcnow()
 
     if ohlcv_path:
         df = build_data_frame(strategy, ohlcv_path)
-    flagged_enter, flagged_exit, strategy = get_flagged_logiz(strategy)
+    flagged_enter, flagged_exit = get_flagged_logiz(strategy)
 
-    # just so there can be some defaults
     new_strategy = strategy.copy()
     new_strategy["base_balance"] = strategy.get("base_balance", 1000)
     new_strategy["exit_on_end"] = strategy.get("exit_on_end", True)
@@ -47,8 +45,8 @@ def run_backtest(
     else:
         perf_stop_time = datetime.datetime.utcnow()
         summary = {"test_duration": (perf_stop_time - perf_start_time).total_seconds()}
+        trade_log = None
 
-    trade_log = None
     return {
         "summary": summary,
         "df": df,
@@ -91,7 +89,7 @@ def get_flagged_logiz(strategy: dict):
     for e_en in flagged_enter:
         flagged_enter.append(strategy["enter"].pop(e_en))
 
-    return flagged_enter, flagged_exit, strategy
+    return flagged_enter, flagged_exit
 
 
 def apply_strategy_to_dataframe(df: pd.DataFrame, strategy: dict):

run_example.py

@@ -1,21 +1,28 @@
 # flake8: noqa
 from fast_trade import run_backtest
-from fast_trade.cli_helpers import save
 import datetime
+import json
 
+ds1 = "2020-06-01"
+s1 = 1590969600
+ms1 = 1590969600000
+
+ds2 = "2020-07-10"
+s2 = 1594339200
+ms2 = 1594339200000
 
 strategy = {
-    "chart_period": "3T",
-    "enter": [["price_action_1", ">", 3]],
+    "chart_period": "1T",
+    "enter": [["price_action_1", ">", 2]],
     "exit": [["price_action_2", "<", 1]],
     "exit_on_end": False,
     "commission": 0.001,
     "indicators": [
-        {"args": [80], "df": "close", "func": "ta.roc", "name": "price_action_1"},
+        {"args": [60], "df": "close", "func": "ta.roc", "name": "price_action_1"},
         {"args": [10], "df": "close", "func": "ta.roc", "name": "price_action_2"},
     ],
-    "start": "2020-02-01",
-    "stop": "2020-02-10",
+    "start": "",
+    "stop": s2,
     "name": "generated",
 }
 
@@ -25,8 +32,7 @@
         "/Users/jedmeier/Projects/fast-trade-candlesticks/gi_zip_toss/BTCUSDT_2020.csv"
     )
     tmp_start = datetime.datetime.utcnow()
-    test = run_backtest(strategy, ohlcv_path=datafile, summary=False)
-    tmp_stop = datetime.datetime.utcnow()
-    # print(test["summary"])
-    print(test["df"])
-    # print(test)
+    test = run_backtest(strategy, ohlcv_path=datafile, summary=True)
+    # print(test["df"])
+    # print(test["trade_df"])
+    print(json.dumps(test["summary"], indent=2))

test/test_build_data_frame.py

@@ -128,11 +128,15 @@ def test_apply_charting_to_df_3():
         mock_df, mock_chart_period, mock_start_time, mock_stop_time
     )
 
+    print("past_stop_time: ",past_stop_time)
+
     past_stop_time = datetime.datetime.strptime(
         "2018-04-17 04:11:00", "%Y-%m-%d %H:%M:%S"
     )
     result_df = apply_charting_to_df(
         mock_df, mock_chart_period, mock_start_time, mock_stop_time
     )
 
+    print("result_df.index[0]: ",result_df.index[0])
+
     assert result_df.index[0] < past_stop_time