使用ollama完成股票技术指标的评价

本篇文章有几个有意思的点：1.使用streamlit可视化。2.计算滚动平均值和动量等指标来了解市场趋势。3.使用Llama 3对数据做解释。这段代码定义了一些全局变量，用于存储滚动数据和跟踪每日的市场情况。rolling_window和dow_rolling_window用于存储苹果公司和道琼斯指数的滚动数据。然后定义了几个变量来跟踪每日的市场情况。daily_high和 daily_low分别

AGI大模型老王

699人浏览 · 2025-02-01 18:54:59

AGI大模型老王 · 2025-02-01 18:54:59 发布

本篇文章有几个有意思的点：

1.使用streamlit可视化。
2.计算滚动平均值和动量等指标来了解市场趋势。
3.使用Llama 3对数据做解释。

提前安装并导入如下包：

import yfinance as yf``import pandas as pd``import schedule``import time``import ollama``from datetime import datetime, timedelta

# Fetching historical data for Apple (AAPL) and Dow Jones (DJI) for yesterday (1-minute intervals)``

stock = yf.Ticker("AAPL")``dow_jones = yf.Ticker("^DJI")``data = stock.history(period="1d", interval="1m")``dow_data = dow_jones.history(period="1d", interval="1m")``data.head()

Global variables to store rolling data for analysis

   ``rolling_window = pd.DataFrame()``dow_rolling_window = pd.DataFrame()``# Variables to track daily context``daily_high = float('-inf')``daily_low = float('inf')``buying_momentum = 0``selling_momentum = 0

这段代码定义了一些全局变量，用于存储滚动数据和跟踪每日的市场情况。

rolling_window和dow_rolling_window用于存储苹果公司和道琼斯指数的滚动数据。然后定义了几个变量来跟踪每日的市场情况。
daily_high和 daily_low分别初始化为负无穷大和正无穷大，用于记录当天的最高价和最低价。通过将它们初始化为极端值，可以确保在实际数据更新时，这些变量会被正确地设置为当天的实际最高价和最低价。
buying_momentum和selling_momentum变量初始化为0，用于跟踪当天的买入和卖出动量。这些变量可以帮助分析市场情绪和趋势，判断市场是处于买入还是卖出状态。

``get_market_open_duration：Extract current time from the last element of the window

def get_market_open_duration(window):`    `# Extract current time from the last element of the window`    `current_time = window.index[-1].time()  # Returns a datetime.time object`        `# Get the previous trading day's date`    `previous_trading_day = datetime.today() - timedelta(days=1)`        `# Combine the previous trading day with the current time`    `current_datetime = datetime.combine(previous_trading_day, current_time)`        `# Define the market opening time as 09:30:00 on the previous trading day`    `market_start_time = datetime.combine(previous_trading_day, datetime.strptime("09:30:00", "%H:%M:%S").time())`        `# Calculate the duration the market has been open in minutes`    `market_open_duration = (current_datetime - market_start_time).total_seconds() / 60  # in minutes`        `return market_open_duration

get_natural_language_insights：generate natural language insights using Ollama（******重点这里，可以换成其他模型。）``

   ``def get_natural_language_insights(`    `rolling_avg, ema, rsi, bollinger_upper, bollinger_lower,`    `price_change, volume_change, dow_rolling_avg, market_open_duration, dow_price_change, dow_volume_change, daily_high, daily_low, buying_momentum, selling_momentum``):`    `prompt = f"""`    `You are a professional stock broker. Apple's stock has a 5-minute rolling average of {rolling_avg:.2f}.`    `The Exponential Moving Average (EMA) is {ema:.2f}, and the Relative Strength Index (RSI) is {rsi:.2f}.`    `The Bollinger Bands are set with an upper band of {bollinger_upper:.2f} and a lower band of {bollinger_lower:.2f}.`    `The price has changed by {price_change:.2f}, and the volume has shifted by {volume_change}.`    `The DOW price has changed by {dow_price_change:.2f}, and the volume has shifted by {dow_volume_change}.`    `Meanwhile, the Dow Jones index has a 5-minute rolling average of {dow_rolling_avg:.2f}.`    `The market has been open for {market_open_duration:.2f} minutes.`    `Today's high was {daily_high:.2f} and low was {daily_low:.2f}.`    `The buying momentum is {buying_momentum:.2f} and selling momentum is {selling_momentum:.2f}.`    `Based on this data, provide insights into the current stock trend and the general market sentiment.`    `The insights should not be longer than 100 words and should not have an introduction.`    `"""`    `response = ollama.chat(`            `model="llama3",`            `messages=[{"role": "user", "content": prompt}]`        `)`    `response_text = response['message']['content'].strip()`        `# Print the natural language insight`    `print("Natural Language Insight:", response_text)

calculate_insights：moving averages and trends

   ``def calculate_insights(window, dow_window):`    `if len(window) >= 5:`        `# Calculate 5-minute rolling average of the 'Close' prices`        `rolling_avg = window['Close'].rolling(window=5).mean().iloc[-1]`        `# Calculate price change and volume change`        `price_change = window['Close'].iloc[-1] - window['Close'].iloc[-2] if len(window) >= 2 else 0`        `volume_change = window['Volume'].iloc[-1] - window['Volume'].iloc[-2] if len(window) >= 2 else 0`        `# Calculate DOW price change and volume change`        `dow_price_change = dow_window['Close'].iloc[-1] - dow_window['Close'].iloc[-2] if len(dow_window) >= 2 else 0`        `dow_volume_change = dow_window['Volume'].iloc[-1] - dow_window['Volume'].iloc[-2] if len(dow_window) >= 2 else 0`            `# Calculate Exponential Moving Average (EMA) and Bollinger Bands (with a 5-period window)`        `ema = window['Close'].ewm(span=5, adjust=False).mean().iloc[-1]`        `std = window['Close'].rolling(window=5).std().iloc[-1]`        `bollinger_upper = rolling_avg + (2 * std)`        `bollinger_lower = rolling_avg - (2 * std)`        `# Calculate Relative Strength Index (RSI) if there are enough periods (14 is typical)`        `delta = window['Close'].diff()`        `gain = delta.where(delta > 0, 0)`        `loss = -delta.where(delta < 0, 0)`        `avg_gain = gain.rolling(window=14, min_periods=1).mean().iloc[-1]`        `avg_loss = loss.rolling(window=14, min_periods=1).mean().iloc[-1]`        `rs = avg_gain / avg_loss if avg_loss != 0 else float('nan')`        `rsi = 100 - (100 / (1 + rs))`        `# Calculate Dow Jones index rolling average`        `dow_rolling_avg = dow_window['Close'].rolling(window=5).mean().iloc[-1]`                `market_open_duration = get_market_open_duration(window)`        `# Print the calculated insights`        `print(f"5-minute Rolling Average: {rolling_avg:.2f}")`        `print(f"EMA: {ema:.2f}")`        `print(f"RSI: {rsi:.2f}")`        `print(f"Bollinger Upper Band: {bollinger_upper:.2f}, Lower Band: {bollinger_lower:.2f}")`        `print(f"Price Change: {price_change:.2f}")`        `print(f"Volume Change: {volume_change}")`        `print(f"DOW Price Change: {dow_price_change:.2f}")`        `print(f"DOW Volume Change: {dow_volume_change}")`        `print(f"Dow Jones 5-minute Rolling Average: {dow_rolling_avg:.2f}")`        `print(f"Daily High: {daily_high:.2f}, Daily Low: {daily_low:.2f}")`        `print(f"Buying Momentum: {buying_momentum:.2f}, Selling Momentum: {selling_momentum:.2f}")`        `print(f"Market has been open for {market_open_duration:.2f} minutes")`                `if int(market_open_duration) % 5 == 0:  # Trigger LLM every 5 minutes`            `get_natural_language_insights(`                `rolling_avg, ema, rsi, bollinger_upper, bollinger_lower,`                `price_change, volume_change, dow_rolling_avg, market_open_duration, dow_price_change, dow_volume_change, daily_high, daily_low, buying_momentum, selling_momentum`            `)

process_stock_update：process a new stock update every minute

   ``def process_stock_update():`    `global rolling_window, data, dow_rolling_window, dow_data`    `global daily_high, daily_low, buying_momentum, selling_momentum`    `if not data.empty and not dow_data.empty:`        `# Simulate receiving a new data point for AAPL and Dow Jones`        `update = data.iloc[0].to_frame().T`        `time_str = update.index[0].time()`        `print(time_str)  # Output: ['09:30:00']`        `dow_update = dow_data.iloc[0].to_frame().T`        `data = data.iloc[1:]  # Safely remove the first row without causing index issues`        `dow_data = dow_data.iloc[1:]`        `# Append the new data points to the rolling windows`        `rolling_window = pd.concat([rolling_window, update], ignore_index=False)`        `dow_rolling_window = pd.concat([dow_rolling_window, dow_update], ignore_index=False)`        `# Update daily high and low`        `daily_high = max(daily_high, update['Close'].values[0])`        `daily_low = min(daily_low, update['Close'].values[0])`        `# Calculate momentum based on price changes`        `if len(rolling_window) >= 2:`            `price_change = update['Close'].values[0] - rolling_window['Close'].iloc[-2]`            `if price_change > 0:`                `buying_momentum += price_change`            `else:`                `selling_momentum += abs(price_change)`                        `# Limit the rolling window to 5 minutes for moving average`        `if len(rolling_window) > 5:`            `rolling_window = rolling_window.iloc[1:]`        `if len(dow_rolling_window) > 5:`            `dow_rolling_window = dow_rolling_window.iloc[1:]`        `# Calculate insights (moving averages, Bollinger Bands, RSI, etc.)`        `calculate_insights(rolling_window, dow_rolling_window)

Schedule job to simulate receiving updates every minute：

   ``schedule.every(10).seconds.do(process_stock_update)``# Run the scheduled jobs``print("Starting real-time simulation for AAPL stock updates...")``while True:`    `schedule.run_pending()`    `time.sleep(1)

运行日志：

通过Llama 3给出的数据分析：

Natural Language Insight: Apple’s price is consolidating within the Bollinger Bands, with a slight dip to 226.39. The EMA and rolling average are slightly higher, indicating a short-term uptrend.

The RSI of 37.54 suggests the stock may be oversold, potentially leading to a bounce. The buying momentum is higher than selling momentum, implying investors are still cautiously optimistic. With the Dow Jones index also experiencing a slight decline, it’s possible Apple’s strength could lead it outperforming the broader market in the short term.

使用Streamlit设计一个界面：

# Streamlit UI``st.title("AI Stock Advisor")``logtxtbox = st.empty()``logtxt = '09:30:00'``logtxtbox.caption(logtxt)``message = st.chat_message("assistant")``message.write("Starting real-time simulation for AAPL stock updates. First update will be processed in 5 minutes...")`    `   ``

需要完整代码