quantra-go-algo

--- Execution Log ---
--- Starting analysis for AAPL ---
Preliminary decision for AAPL: Signal=0
--- Starting analysis for MSFT ---
Preliminary decision for MSFT: Signal=0
--- Starting analysis for GOOG ---
Preliminary decision for GOOG: Signal=0
Portfolio Agent: No BUY signals. Allocating 0% to all tickers.
Execution Agent: Submitting real trades based on portfolio plan.
Execution: Fetched cash balance: $100000.00

quantra-go-algo

--- Market is OPEN. Starting trading loop for 2026-01-12 until 16:00:00 ---

--- Running Engine Iteration at 15:32:35 ---

INFO: Fetching 90 observations of 5min data for AAPL...
INFO: Searching web for 10 news links about AAPL since 2026-01-12 15:22
Both GOOGLE_API_KEY and GEMINI_API_KEY are set. Using GOOGLE_API_KEY.
INFO: Fetching 90 observations of 5min data for MSFT...
INFO: Searching web for 10 news links about MSFT since 2026-01-12 15:22
Both GOOGLE_API_KEY and GEMINI_API_KEY are set. Using GOOGLE_API_KEY.

quantra-go-algo

(base) josgt@josgt-desktop:~/Downloads/testing11$ cd "/home/josgt/Downloads/testing11/AI-in-Trading-Workflow/LLMs/example_02_Agentic_AI_based_Portfolio_manager_using_Alpaca_API"
(base) josgt@josgt-desktop:~/Downloads/testing11/AI-in-Trading-Workflow/LLMs/example_02_Agentic_AI_based_Portfolio_manager_using_Alpaca_API$ conda activate alpaca_bot
(alpaca_bot) josgt@josgt-desktop:~/Downloads/testing11/AI-in-Trading-Workflow/LLMs/example_02_Agentic_AI_based_Portfolio_manager_using_Alpaca_API$ python3 main.py
/home/josgt/anaconda3/envs/alpaca_bot/lib/python3.12/site-packages/pyfolio/pos.py:25: UserWarning: Module "zipline.assets" not found; multipliers will not be applied to position notionals.
  warnings.warn(
/home/josgt/anaconda3/envs/alpaca_bot/lib/python3.12/site-packages/langchain_tavily/tavily_research.py:97: UserWarning: Field name "output_schema" in "TavilyResearch" shadows an attribute in parent "BaseTool"
  class TavilyResearch(BaseTool):  # type: ignore[override, override]
/home/josgt/anaconda3/envs/alpa

quantra-go-algo

# Initialize a new StateGraph with our AgentState structure.
workflow = StateGraph(AgentState)
# Add the functions as nodes in the graph.
workflow.add_node("analysis", analysis_node)
workflow.add_node("portfolio_allocator", portfolio_agent_node)
workflow.add_node("executor", execution_node)
workflow.add_node("email_notifier", email_notification_node)

# Set the entry point of the graph, which is the first node to be called.
workflow.set_entry_point("analysis")

quantra-go-algo

# Set the for loop 
for i in range(1,len(monthly_index)):
    print('='*100)
    # Set the current month-end variable
    current_month_end = monthly_index[(i-1)]
    # Set the next month-end variable
    next_month_end = monthly_index[i]

    # Set the span from the previous to the next month end
    span = len(data.loc[current_month_end:next_month_end,:].index)

quantra-go-algo

from statsmodels.tsa.arima.model import ARIMA

quantra-go-algo

pip install pandas numpy matplotlib statsmodels yfinance

quantra-go-algo

pip install pandas statsmodels matplotlib

quantra-go-algo

S_3 = Df['S_3'].dropna()
S_9 = Df['S_9'].dropna()

# Since S_3 and S_9 are moving averages, we could check their cointegration with the original Close series
close_price = Df['next_day_price']

# Conduct Engle-Granger Cointegration Test between S_3 and Close price
coint_result_3 = coint(S_3, close_price)

# Conduct Engle-Granger Cointegration Test between S_9 and Close price

quantra-go-algo

# Plot the both buy-and-hold and the strategy cumulative returns
ggplot(data=df_forecasts, aes(x = date)) +
  geom_line(aes(y = var_stra_cum_returns, color="VAR")) +
  geom_line(aes(y = tvp_var_sv_stra_cum_returns, color="TVP-VAR-SV")) +
  geom_line(aes(y = bnh_cum_returns,color="B&H")) +
  geom_line(aes(y = stra_improved_cum_returns, color="Improved TVP-VAR-SV")) +
  ggtitle("Buy and Hold and Strategies' Cumulative Returns") + xlab("Date") + ylab("Returns") + 
  theme(plot.title = element_text(hjust = 0.5, size=25), legend.position="bottom", legend.text = element_text(size=20)) + 
  scale_x_date(date_labels = "%b %y") +
  theme(