Moving machines

.gitignore

@@ -10,4 +10,3 @@ templates/*.html
 celerybeat-schedule
 .dbdata/*
 *.log
-

.python-version

@@ -0,0 +1 @@
+3.11

Dockerfile

@@ -1,17 +1,18 @@
-FROM python:3.7
+FROM python:3.11
 
 WORKDIR /usr/src/app
 
 ENV PYTHONPATH /usr/src/app
 
-RUN apt update
-RUN apt upgrade -y
-RUN apt install build-essential -y
+RUN apt update && \
+  apt upgrade -y && \
+  apt install build-essential -y
+
 RUN python -m pip install --upgrade pip
 
 COPY . .
 
 # Build requirements for facebook prophet
-RUN python -m pip install pystan==2.19.1.1 numpy==1.21.2
-# Install the rest of the requirements
-RUN python -m pip install -r requirements.txt -r requirements-dev.txt
+RUN pip install pipenv && \
+  pipenv install --system --deploy --ignore-pipfile && \
+  pipenv clean

Pipfile

@@ -0,0 +1,47 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+requests = ">=2.31.0"
+finviz = ">=1.4.6"
+pystan = ">=3.7.0"
+numpy = ">=1.25.1"
+prophet = ">=1.1.4"
+plotly = ">=5.15.0"
+pandas = ">=2.0.3"
+pandas-datareader = ">=0.10.0"
+matplotlib = ">=3.7.2"
+cython = ">=0.29.36"
+pybind11 = ">=2.11.0"
+lunarcalendar = ">=0.0.9"
+holidays = ">=0.28"
+jupyterlab = ">=4.0.3"
+pytrends = ">=4.9.2"
+nest-asyncio = ">=1.5.6"
+tabulate = ">=0.9.0"
+slackbot = ">=1.0.5"
+psutil = ">=5.9.5"
+boto3 = ">=1.28.3"
+redis = ">=4.6.0"
+alpha-vantage = ">=2.3.1"
+sqlalchemy-mixins = ">=2.0.3"
+sqlalchemy = ">=2.0.19"
+mysqlclient = ">=2.2.0"
+pyyaml = ">=6.0"
+slack-sdk = ">=3.21.3"
+yfinance = ">=0.2.24"
+hy = ">=0.27.0"
+
+[dev-packages]
+isort = "*"
+flake8 = "*"
+bandit = "*"
+black = "*"
+mypy = "*"
+lock = "*"
+
+
+[requires]
+python_version = "3.11"

algo_bot/cache.hy

@@ -1,5 +1,5 @@
-(import [algo_bot.settings [AlgoBotSettings get_settings]])
-(import [algo_bot [utils]])
+(import algo_bot.settings [AlgoBotSettings get_settings])
+(import algo_bot [utils])
 (import redis)
 (import pickle)
 
@@ -24,6 +24,6 @@
             ; Weird None check for dataframe not a problem in python with `data is not None` shurg.
             (if (= (type data) (type None))
                 (do
-                    (setv data (func #*args #**kwargs))
+                    (setv data (func #* args #** kwargs))
                     (write :key cache-key :value data)))
             data)))

algo_bot/charting.py

@@ -1,6 +1,5 @@
 import matplotlib.pyplot as plt
 import plotly.graph_objects as go
-from algo_bot import utils
 
 
 def candlestick_plot(df):

algo_bot/clients/finviz_client.hy

@@ -1,22 +1,20 @@
 (import asyncio nest_asyncio finviz)
-(import [algo-bot.utils [event-loop]])
-(import [pandas :as pd])
-(import [finviz.screener [Screener]])
+(import algo-bot.utils [event-loop])
+(import pandas :as pd)
+(import finviz.screener [Screener])
 
 
-(with-decorator event-loop
-    (defn screener [&optional [filters ["exch_nasd" "sh_avgvol_o200" "sh_price_u5" "ta_change_u10" "geo_usa"]] [order "-volume"]]
-        (setv stock-list (Screener :filters filters :order order))
-        (setv df (pd.DataFrame stock-list.data))
-        (df.set_index "Ticker" :drop False)
-        df))
+(defn [event-loop] screener [&optional [filters ["exch_nasd" "sh_avgvol_o200" "sh_price_u5" "ta_change_u10" "geo_usa"]] [order "-volume"]]
+    (setv stock-list (Screener :filters filters :order order))
+    (setv df (pd.DataFrame stock-list.data))
+    (df.set_index "Ticker" :drop False)
+    df)
 
 ; Finviz table function generator
 (defn finviz-table [table]
-    (with-decorator event-loop
-        (fn [ticker]
-            (setv s (Screener :table table :tickers [ticker]))
-            (pd.DataFrame s.data))))
+    (fn [event-loop] [ticker]
+        (setv s (Screener :table table :tickers [ticker]))
+        (pd.DataFrame s.data)))
 
 (setv finviz-performance (finviz-table "Performance"))
 (setv finviz-overview (finviz-table "Overview"))

algo_bot/clients/trends.hy

@@ -1,4 +1,4 @@
-(import [pytrends.request [TrendReq]])
+(import pytrends.request [TrendReq])
 
 (defn interest-over-time [kw-list]
     (do

algo_bot/commands/all.py

@@ -1,13 +0,0 @@
-from algo_bot.commands import (
-    finviz,
-    help,
-    news,
-    positioning,
-    screener,
-    strategies,
-    technical,
-    ticker,
-    user,
-    watchlist,
-    simulation,
-)

algo_bot/commands/finviz.hy

@@ -1,8 +1,8 @@
 (import re)
-(import [pandas :as pd])
-(import [slackbot.bot [respond-to]])
-(import [algo-bot [utils]])
-(import [algo-bot.clients [finviz-client]])
+(import pandas :as pd)
+(import slackbot.bot [respond-to])
+(import algo-bot [utils])
+(import algo-bot.clients [finviz-client])
 
 (with-decorator (respond-to "^screener (.*)" re.IGNORECASE)
     (defn screener [message filters]

algo_bot/commands/help.hy

@@ -1,7 +1,7 @@
 (import re)
-(import [slackbot.bot [respond-to]])
-(import [algo-bot [utils]])
-(import [algo-bot.slackbot-settings [BOT_ENV]])
+(import slackbot.bot [respond-to])
+(import algo-bot [utils])
+(import algo-bot.slackbot-settings [BOT_ENV])
 
 (defn _respond [message response]
  (utils.reply-webapi message (utils.wrap-ticks response)))

algo_bot/commands/news.hy

@@ -1,28 +1,26 @@
 (import re)
-(import [pandas :as pd])
-(import [slackbot.bot [respond-to]])
-(import [algo-bot [charting utils]])
-(import [algo-bot.clients [finviz-client trends]])
+(import pandas :as pd)
+(import slackbot.bot [respond-to])
+(import algo-bot [charting utils])
+(import algo-bot.clients [finviz-client trends])
 
-(with-decorator (respond-to "^news (.*)" re.IGNORECASE)
-    (defn news [message ticker]
-        (for [url (get-news-urls ticker)]
-            (utils.reply-webapi message f"\n{url}"))))
+(defn [(respond-to "^news (.*)" re.IGNORECASE)] news [message ticker]
+    (for [url (get-news-urls ticker)]
+        (utils.reply-webapi message f"\n{url}")))
 
 
-(with-decorator (respond-to "^interest-over-time (.*)" re.IGNORECASE)
-    (defn interest-over-time [message kws]
-        (utils.processing message)
-        (setv kw-list (kws.split ","))
-        (setv interest (trends.interest-over-time kw-list))
-        (setv df (pd.DataFrame interest))
-        (setv fig (charting.line-chart-trends df))
-        (setv filename (+ (.join "_" (kws.split ",")) "_interest_over_time.html"))
-        (utils.store-graph fig filename)
-        (utils.send-webapi message
-                            ""
-                            :title f"Interest Over Time: {kws}"
-                            :title-link (utils.html-url filename))))
+(defn [(respond-to "^interest-over-time (.*)" re.IGNORECASE)] interest-over-time [message kws]
+    (utils.processing message)
+    (setv kw-list (kws.split ","))
+    (setv interest (trends.interest-over-time kw-list))
+    (setv df (pd.DataFrame interest))
+    (setv fig (charting.line-chart-trends df))
+    (setv filename (+ (.join "_" (kws.split ",")) "_interest_over_time.html"))
+    (utils.store-graph fig filename)
+    (utils.send-webapi message
+                        ""
+                        :title f"Interest Over Time: {kws}"
+                        :title-link (utils.html-url filename)))
 
 (defn get-news-urls [ticker]
     (setv urls [])

algo_bot/commands/simulation.py

@@ -21,7 +21,7 @@ def run_turtle_simulation(message, params):
     df = dailyAdjustedRaw.sort_values(by="date", ascending=False).reset_index().rename(columns=COLUMN_RENAME).drop(["7. dividend amount", "8. split coefficient"], axis=1, inplace=False)
     df = df.head(20).sort_values(by="Date", ascending=True).reset_index().drop(["index"], axis=1, inplace=False)
     signals = turtleSignals(df)
-    messages, states_buy, states_sell, total_gains, invest = turtle_buy_stock(df.Close, signals['signal'], df, initial_money=params.balance, max_buy=params.limit, max_sell=params.limit)
+    messages, states_buy, states_sell, total_gains, invest = buy_stock(df.Close, signals['signal'], df, initial_money=params.balance, max_buy=params.limit, max_sell=params.limit)
     # Reply the simulation
     utils.reply_webapi(message, utils.wrap_ticks("\n".join(messages)))
     # Send the chart
@@ -49,7 +49,7 @@ def run_moving_average_simulation(message, params):
     df = dailyAdjustedRaw.sort_values(by="date", ascending=False).reset_index().rename(columns=COLUMN_RENAME).drop(["7. dividend amount", "8. split coefficient"], axis=1, inplace=False)
     df = df.head(90).sort_values(by="Date", ascending=True).reset_index().drop(["index"], axis=1, inplace=False)
     signals = movingAverageSignals(df)
-    messages, states_buy, states_sell, total_gains, invest = moving_average_buy_stock(df.Close, signals['positions'], df, initial_money=params.balance, max_buy=params.limit, max_sell=params.limit)
+    messages, states_buy, states_sell, total_gains, invest = buy_stock(df.Close, signals['positions'], df, initial_money=params.balance, max_buy=params.limit, max_sell=params.limit)
     # Reply the simulation
     utils.reply_webapi(message, utils.wrap_ticks("\n".join(messages)))
     # Send the chart
@@ -75,95 +75,15 @@ def movingAverageSignals(df):
     long_window = int(0.05 * len(df))
     signals = pd.DataFrame(index=df.index)
     signals['signal'] = 0.0
-
     signals['short_ma'] = df['Close'].rolling(window=short_window, min_periods=1, center=False).mean()
     signals['long_ma'] = df['Close'].rolling(window=long_window, min_periods=1, center=False).mean()
-
     signals['signal'][short_window:] = np.where(signals['short_ma'][short_window:]
                                                 > signals['long_ma'][short_window:], 1.0, 0.0)
     signals['positions'] = signals['signal'].diff()
 
     return signals
 
 
-def moving_average_buy_stock(
-    real_movement,
-    signal,
-    df,
-    initial_money=10000,
-    max_buy=1,
-    max_sell=1,
-):
-    """
-    real_movement = actual movement in the real world
-    delay = how much interval you want to delay to change our decision from buy to sell, vice versa
-    initial_state = 1 is buy, 0 is sell
-    initial_money = 1000, ignore what kind of currency
-    max_buy = max quantity for share to buy
-    max_sell = max quantity for share to sell
-    """
-    starting_money = initial_money
-    states_sell = []
-    states_buy = []
-    current_inventory = 0
-    messages = []
-
-    def buy(i, initial_money, current_inventory):
-        shares = initial_money // real_movement[i]
-        if shares < 1:
-            messages.append(
-                'Date: %s day %d: total balances %f, not enough money to buy a unit price %f'
-                % (df.Date[i], i, initial_money, real_movement[i])
-            )
-        else:
-            if shares > max_buy:
-                buy_units = max_buy
-            else:
-                buy_units = shares
-            initial_money -= buy_units * real_movement[i]
-            current_inventory += buy_units
-            messages.append(
-                'Date: %s day %d: buy %d units at price %f, total balance %f'
-                % (df.Date[i], i, buy_units, buy_units * real_movement[i], initial_money)
-            )
-            states_buy.append(0)
-        return initial_money, current_inventory
-
-    for i in range(real_movement.shape[0] - int(0.025 * len(df))):
-        state = signal[i]
-        if state == 1:
-            initial_money, current_inventory = buy(
-                i, initial_money, current_inventory
-            )
-            states_buy.append(i)
-        elif state == -1:
-            if current_inventory == 0:
-                messages.append('Date: %s day %d: cannot sell anything, inventory 0' % (df.Date[i], i))
-            else:
-                if current_inventory > max_sell:
-                    sell_units = max_sell
-                else:
-                    sell_units = current_inventory
-                current_inventory -= sell_units
-                total_sell = sell_units * real_movement[i]
-                initial_money += total_sell
-                try:
-                    invest = (
-                        (real_movement[i] - real_movement[states_buy[-1]])
-                        / real_movement[states_buy[-1]]
-                    ) * 100
-                except:
-                    invest = 0
-                messages.append(
-                    'Date: %s day %d, sell %d units at price %f, investment %f %%, total balance %f,'
-                    % (df.Date[i], i, sell_units, total_sell, invest, initial_money)
-                )
-            states_sell.append(i)
-    invest = ((initial_money - starting_money) / starting_money) * 100
-    total_gains = initial_money - starting_money
-    return messages, states_buy, states_sell, total_gains, invest
-
-
 def turtleSignals(df):
     count = int(np.ceil(len(df) * 0.1))
     signals = pd.DataFrame(index=df.index)
@@ -176,7 +96,7 @@ def turtleSignals(df):
     return signals
 
 
-def turtle_buy_stock(
+def buy_stock(
     real_movement,
     signal,
     df,
@@ -202,7 +122,7 @@ def buy(i, initial_money, current_inventory):
         shares = initial_money // real_movement[i]
         if shares < 1:
             messages.append(
-                'DATE: %s day %d: total balances %f, not enough money to buy a unit price %f'
+                'Date: %s day %d: total balances %f, not enough money to buy a unit price %f'
                 % (df.Date[i], i, initial_money, real_movement[i])
             )
         else:
@@ -213,7 +133,7 @@ def buy(i, initial_money, current_inventory):
             initial_money -= buy_units * real_movement[i]
             current_inventory += buy_units
             messages.append(
-                'DATE: %s day %d: buy %d units at price %f, total balance %f'
+                'Date: %s day %d: buy %d units at price %f, total balance %f'
                 % (df.Date[i], i, buy_units, buy_units * real_movement[i], initial_money)
             )
             states_buy.append(0)
@@ -228,7 +148,7 @@ def buy(i, initial_money, current_inventory):
             states_buy.append(i)
         elif state == -1:
             if current_inventory == 0:
-                messages.append('DATE: %s day %d: cannot sell anything, inventory 0' % (df.Date[i], i))
+                messages.append('Date: %s day %d: cannot sell anything, inventory 0' % (df.Date[i], i))
             else:
                 if current_inventory > max_sell:
                     sell_units = max_sell
@@ -245,11 +165,10 @@ def buy(i, initial_money, current_inventory):
                 except:
                     invest = 0
                 messages.append(
-                    'DATE: %s day %d, sell %d units at price %f, investment %f %%, total balance %f,'
+                    'Date: %s day %d, sell %d units at price %f, investment %f %%, total balance %f,'
                     % (df.Date[i], i, sell_units, total_sell, invest, initial_money)
                 )
             states_sell.append(i)
-
     invest = ((initial_money - starting_money) / starting_money) * 100
     total_gains = initial_money - starting_money
     return messages, states_buy, states_sell, total_gains, invest